Firey horse with a speed of light, a cloud of dust and a hardy high old silver, the long ranger. With his faithful Indian companion cuddle, the daring and resourceful, masked rider of the plains led the fight for law and order in the early western United States. Nowhere in the pages of history can one find a greater champion of justice. Return with us now to those thrilling days of yesteryear, from out of the past come the thundering hoop beat to the great horse Silver, the long ranger rides again. Come Silver, let's go be close. Come Silver, come! The covered wagons had been on the trail for many weary weeks, dreading a cross-trackless country from the east toward territories that had just been opened in the west. Ace of Quimby led the way, riding in the first wagon. His wife fell on the feet beside him. Quimby's face was set in hard lines of determination. Get up here!  His manner was defiant, as if he dared his wife to challenge the plans that he had just announced. Well, speak up, Kate. You've got something to say, sir. Well, Ace, sir, if you want my opinion, I don't think you're being fair to the others. Oh, you don't, eh? None of them know your plans, do they? No, Chet. Oh, I thought now. You think they'd come along with me if they'd have known what I had in mind? I know the wind has come, and I'd better fool and tell them. There'll be a big complaint when the truth is known. Anyone that don't like what I do can leave with wagon and supplies and go it alone on foot. Here comes plate Morton's boy. Well, I gotta... Hey, Mr. Quimby! Oh, hold it. Easy, boy. Mr. Quimby? Well, what do you want? My father thinks we're off the trail. You'll tell your father I know where I'm going. My father says we should be south of the Mace instead of north. Tell him I've been here before. I know where we're going. Now go on. Tell him. Yes, sir. Get up there. Everyone will know we're going wrong. Last time where we thought we were told to be sure and go south of the Mace, we were told there were bad Indians north of it. And now everyone can see the Mace on their left side. They know we're in Indian country. Well, they know to blame much. Oh, here comes Jake Porter right now. They expect he wants to tell me the same thing. In relation, read up. Oh, boy, hold it.  Read up on this. I've got to talk to you. Oh, there, boy. Don't do it. He's a great little boy. Oh, boy, hold it. Oh, boy! Well, what do you want, Jake? Speak up fast. We've got to shove on. Here comes some of the others. Better tell them the truth, eh, sir? They'll have to know it sooner or later. How's he here, Quimby? We're not going right. No? Hey, there, Quimby. We should be on the other side of that Mace. Yeah, what about it? It looks like every man in the train is coming up here to tell you. Hey, Quimby. We're north of the Mace. We should be on the other side of that. Oh, hold on. There's bad Indians in this valley. That's right. You should be south of here. Now listen to me. All of you, quiet down. I know where we are. I know as well as any one of you. Remember, I've been out here before. I know this valley. Then why'd you bring us here with this rich king danger? That danger is overrated. That Mace is a landmine. I brought you to this valley because this is where I intend to set up camp. What? What's why? How long came to stay here? Well, I don't know for how long. Depends on a lot of things. There's water just ahead and that's where we stop. Maybe there's a risk that we should have something to say about this. Anyone that don't want to stay is free to go on. I've got a wife to consider. Just remember this. Anyone that wants to go on can do so. But he'll go without horses, without a wagon, and without food. Now keep it. You heard what I said.  I own every wagon and horse and the outfit. Well, Queen Bee, that's not so. It's true that each of us owes you money for his outfit. While you read the contract, we draw it up. You'll see that I can call the loan whenever I want. You don't have the cash. I can take the outfit. Well, trust it, Doug. Now listen to me for a minute. There's gold in this air valley, and I aim to get it. Oh, correct it. You got us all interested in this trip. I promise that we could settle down on Homestead Rock. And so you can. After you've paid off your debt to me. By digging for gold. Right. You had this trick in mind from the start. Well, what if I did? You promised that we could... I will talk to you from Homestead Rock when your debts are paid off. There, boys. Looks like we got took in a plenty. Kidding, can you really take away our equipment? I guess you can. Then we can go on with nothing and starve.   I tell you, there ain't dangerous at all. That's just a story that's been cooked up to scare people away from this valley. There's gold here, and I want it. Now talk it over and make your choice. Is she? Well, Jay, if we got to stay here, you might at least pass out the guns and rifles you got in your wagon so you can all turn on me. But if the Redskins attack, we've got no way to fight them. If they attack, tell me a way to fight them. Now make up your minds what you aim to do. Acer Quimby held all the cards and played them well. He won his point, and a camp was set up in the valley. It was seen by an Indian on the very next day that the Indian was taunt-o with a faithful friend of the Lone Ranger. He reported his discovery to the Masked Man. But there is enough gold in the valley to warrant that risk, taunt-o. You may know that. I wonder if those people were warned about Bakuna and the rest of the savage Indians. Ah, them warned. They camped there in spite of the warning. I wonder how we can persuade them to move on. That's not easy. Yes, I know it isn't. The men are bitten by the gold bug. They're hard to reason with. You hear that? Yes. That Indian. One of the Bakunas. Over here, taunt-o brings cows. Come on, children. This way, taunt-o. That looks like behind the rocks. It's a lot easier now. Be safe here. There they are, taunt-o. A dozen of them. Ah, those bad Indians. They'll pass without seeing us. That's good. Look how they're heading. Head to south. They're not riding hard. They're on the warpath. Those east news must be warned. We go? Yes. If we circle to the north, we can ride hard and reach the camp before those Indians. Come on, taunt-o. Steady, big fella. Get on. Come on, children. Move it. Acer Quimby directed the work of the men in the valley. He has the men working in groups of two or three, taking a war from various places in the hillside.  Maybe we'll try to reach you with a mule. You didn't get nowhere talking to Acer, Jake. Nowhere talking. Yes. Show me his hard work. Yeah. How long do you think we'll be here? Well, if Quimby's expects to stay here and we find real payday for him, it might be months. Months? Yep. I don't see how he can get away with it. Especially when he figures to keep whatever gold we find. He's getting away with it. But Jake, is it legal? Legal. The difference is there to make. Quimby has all the weapons in his wagon and he guards him like a hawk. I suppose we wouldn't have a chance of getting away with the horses and wagons, would we? No use trying that, Tommy. Even if we did get away, Quimby would catch up with us. Sooner or later, he'd have a law on us and take everything we had. The ordinary polar cat. The only thing we can do is stay here and hope we find enough gold to set us up. Hey, Jake. Yeah? I thought I heard hoops. Well, which way? There. It seemed to come more beyond us. Yeah. There they are. You see them? They're coming this way. They're coming for us. Where's the guns? Come on, Jake. We got to make Ace a hand-over rifle for the guys. Hey, look. Coming down the hill. A mask, man. There's a lot of guns. We're caught. Let's ride them. Hey, don't do something. Quimby. They got the rifles, you guys. We got to defend ourselves. Hurry it up, Quimby. We got to have weapons. Engines from both sides. Bend that mask, man. Come on, Quimby. Hurry it up, Quimby. Wait a minute. Those ones from the far end of the valley have rained out. Why use your pass out for weapons?  Put that mask, man, in the riskier. They ain't cutting over towards us. They're heading for the six that's over yonder. They've opened fire. The lone ranger and model dashed past the camp with guns blazing and shard and straight toward the Indian Gulf. The Indians, surprised at finding settlers in the valley, were further surprised by the sudden attack in the burst of gunfire. They quickly turned and fled. Rain up, fellow. Hold on. Hold on. Ready? They're gone. Now we're going. Talk to those settlers.  They teach the Redskins how to get rubbish. Get out the guns, eh, sir. You've got to give us weapons. You don't need them. I'm covering that mask, man.  I know what I'm doing. You saw those Redskins turn tailed, didn't you? That's no nerve at all. Oh, hold on. Hold on. Thanks for taking the critters, mister. Even if you are a masked outlaw, I'm grateful. That is all right.  I'm taking no chances. You needn't think you're going to rob us. Now, clear on. You must be Aza Quimby. Well, what of it? Haven't you been warned about the Indians in this valley region? I know all about them. They're not near as savage as people say. No. Not if they're left alone. But they resent people who camp on their hunting grounds. Yeah. A pack of lies to keep people away from the gold this year. There's not enough gold to make the risk worthwhile. Oh, no. They think he's right, Quimby. We'll find out for ourselves. The Indians will return. They do? I'll fire a few shots at them like you did. They don't have any stomach for a fight. The Indians you saw were merely scouts. After they reported to their leader, you'll see another kind of attack. One that won't be turned back so easily. All right. Thanks for the advice. Now, Shabon. Are you the only armed man in this valley? This he is. Thank you, Shabon. That man seems to know what he's talking about. Listen, mister. We've got plenty of weapons that'll be brought into play if they're needed. Oh, I see. Do the rest of you men want to leave here? Yes, ma'am. They're working for me, and they've got to do as I say. He won't let none of us go to his fine gold for it.  Mister, you, mister. You shabon right away. Very well, Quimby. Are you a man? Get back to work. Just one thing, Quimby. If Bacuna and his Indians attack, you'll be responsible for anything that happens to these men and women. I know my responsibility. I don't have to be tore by a mask man. And if Bacuna knows that you're the only one who wants to stay, he might deal with you personally. Huh. Very big block. Come on, fella.  Come on, fella.  Ah. The others are at Quimby's emergency. He won't even let them have guns. If Bacuna makes an attack, they'll be wiped out before they can get weapons to defend themselves. Not all. We need help. I have a plan. Very to camp where we can talk. Come on, fella.  The curtain falls on the first act of our Lone Ranger story. Before the next exciting scene, please permit it to pause for just a few moments. Now to continue our story. After leaving the valet where Asa Quimby underestimated the peril of the savage Indians with Pete Bacuna, the Lone Ranger and Tonto hurried to their camp. Oh, sir. Hold on. Oh. Very big block. You've got plans? Yes, Tonto. Now, those cells will report to Bacuna. He'll declare war on the people in the valet. And letting us spend at least a day and a night getting ready for the war. Is that right? That may give us a time we'll need to get your friend Chief Thundercloud to help us. Him not far from here. That's why I thought of him. And Thundercloud will not have to make war. I'll listen to me. As the day advanced, the men in the valet discussed the appearance and the warning of the masked man. The more they talked about this situation, the more they resented Asa Quimby's attitude. Towards sunset, the leader and his wife receded on the ground in the shade of a wagon. Oh, Asa. I could only make you realize how wrong you are. The only thing you could do is help me guard the rifles. So the men don't get him. They do. There'll be gun play and a few people will get hurt. Remember that.  Hate myself for helping you. What's this coming? Looks like another committee to talk to me. Jake Porter in the lead. Have to have a showdown with him. Well, what do you want? Why'd you quit work? Grimby, we want to speak to you. You've spoken before, and I know what you aim to say. There'll be no more argument about this job. We don't only aim to argue. We're just going to tell you what we decided. That's all. And you can do as you please about it. Tell it, Jake. Indy, you own the wagons and the horses, guns and rifles and all the supplies. They're a bad idea. Now continue on them till you critters pay me what they cost. We might try to take the steps and run out on you. Try it. Maybe I won't be able to shoot all of you, but I'll get a few. Always. Don't talk. Well, don't worry. We don't plan to do anything like that. We'll stay here.  Like you talked about back east. You'll bet you'll stay. You'll stay although I'm alone and stuck. We'll stay, but we won't work. What's that? You'll hurt him.  You owe me money. And I'm giving you a chance to pay it by diggin' gold. With your looks, we'll be a very long time. We'll find the motherlode, the painter. That's with any digging that we do. All right. All right then. You just sit on your haunches and do nothing. You bet we will. When you're hungry for food, you can go to work and earn it. Remember, I've got all the food. Maybe you can hunt games, but you'll have to do it with sticks and stones or make some Indian arrows. You mean you starboard? I mean I'm boss. And I'll have my way. Hey, what's that? That's it. Hey, sir, look. Hey, here. Speak up on it. Let me get my gun. You'll not move. Dad, into the wagon. Why? Stand still. Come on. Come on. We're covered. You drop guns. Drop them quick. Yeah. Hey, that's the red skin that was here with a mask man. Other Indian help, Donald. You'll not make trouble. Here's what comes of keeping guns away from it. Yeah, the Tukatos. Please go on. Tukatos. Indium not hurt you, father. Only want them later. Let that look out. Please just look up your head. Please go. Let me go. Let me go. I tell you. Help me. Call me back. What do we do? You stand still. Not to move. Not to get hurt. There is my husband, Joe. You were late, man. Tukatos. Don't let him take me. Don't let him catch me. Many of us make a move will be shot. We can't do nothing crammy. You know that. Please go. Go ahead. You'll not get hurt. Yeah, me got filler. That's good. Take him. Hey, you put Mr. Quimby on that horse. Hey, we need you to help me. The rifle's in the wagon. Get him. Take him on fire. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. Oh. mask. Remember? Oh, you're in with the Indians. Your pal helped capture me. What's happened to me? I'm not sure. I'll keep your voice down lower. Get me out of here. Save me. I'll pay. You made the Indians angry. But I didn't mean no harm. You occupied their land. They've got no claim to it. They're hunting wrongs. Just don't call for them to kill me.  You were free. Would you clear out of this valley? Just let me lose. I don't do anything. I'll do anything you say. I wonder if you'd keep your word. Sure I will. Honestly. You're pretty sharp, Quimby. I'll do anything you say. Keep me out of here now. I wonder how your companions would feel if I would let you go back to them. Oh, they'd want me to die. You tricked them, Asa. It makes it hard to help you. Well, what do you mean? Just cut me loose. You're surrounded by Indians. You couldn't get out of the village. No, that wouldn't help you. Just one way you can be rescued. I'll have to bring your companions to save you. Oh, they won't do it. Can you blame them? They came west to settle homes. They came here because of promises you made. I'll keep the promises. I meant to all along. But I wanted to go home. Quimby, how about a paper with me? Paper. Yes. Before I came, I talked to your friends. They won't trust you. They talked to them. I drew up an agreement for you to sign. You promised to leave the valley at once. You promised to give every man who borrowed money from you one year to pay back what he owes without interest. Well, tell me. No, wait. That's not why I started this agreement. You've started it. I hope of making them man your slaves until they found a gold mine for you. Now things will be different. Oh, my plan. There's the agreement, Quimby. I'll let them match so you can see it. You sign it. I think your friends might be willing to try to help you. I'll sign. In the morning, Asa Quimby was hollow-eyed from lack of sleep. The night had been spent in terror. Fearing that the Indians might begin their punishment at any moment, Asa counted the minutes to morning. Then, at daybreak, he began listening for his companions to come. Presently, he heard someone approaching. His hopes rose, but they were instantly dashed when he saw Chief Thundercloud enter the wigwam. You! It means Indian plenty angry at pale face. No, no, listen. You drive games from valley. You spoil Indian hunting grounds. You just let me talk. I swear to leave here. I won't make no trouble for you. You speak with crooked tongue. Oh, no, I mean it. Make promise, not keep promise. Not good. What are you going to do to me?  No, no, please. I don't want to die. Indian not want to starve. You drive game away. Indian starve.  You not drive game away. Indian not starve. Who let me? What's that? Brave come. Take it to the state. No, no, please. Don't let me. Let me go. Let me go. Please. Please let me go. I'll leave here. I'll take you along with me, huh? My friend. Oh, my God. You just can't do it. You just can't do it. You just can't do it. You just can't do it. You just can't do it. Help. Help. Help. Me kill now. Me kill. All of you. Get away from that man. Cut these cards. Let me out of here. Help me. You'll be out in a few seconds. Turn this way so I can cut the roll hide. There you are. Take with me. Oh, take you cane. You all right? Yeah. Oh, you can stand. structural risk is back. What about these? I'll take care of these Indians, Jake. Yeah, but Ricky, we're on your way, Kimby. And remember your promise.  You came and saved me, despite of all I've done. You've left me late. I say, Peter, the men sure both can't in record time.  Of course they do. They are right gay. And so are you. Kate, I'm so glad to be alive. I don't mind leaving this valley in the gold. It might be here. It might be here. Here's your. Everyone sit still there. Good, Jake. Fine and dandy. Hey, you still got the right moves. They took from your wagon the rest of the way. You want them collected? What? You mean this hand still, boss? Yep. I was going to give you another chance. Oh, dog gone. You hear that, Kate? Kate, tell the boys to keep the weapons. Tell them to keep everything. I don't want nothing but the chance to live. And the repayment of the loans in a year or so. All right, easy. And say, that reminds me, I've been wondering. What? You could, as it came to rescue me. You fired a lot of shots at those engines. How is it that no one got hurt? You really want to know? Yes, I do. If you could, as can't you, better than that. There's no point in having rifle. Well, easy. Those engines that captured you were friendly engines. Friendly? They didn't handle me like they were friendly. Carry me away, tie me up, hand and foot. Well, easy. Just figure what would have happened if the bad engines had got you. You see, that masked man fixed it up a few Sunday clouds to help teach you a lesson. No, dog gone. I never knew how good it was to be alive, even without gold, till I was sitting in that wig warm waiting for torches. The coolest tribe is war-dancing right now, easy, getting rid of your techies valley in a few hours. Let him come, dad, Radham. We won't be here. Move over, Kate. Let me under that. Get your wagons, boys. We're starting off a homestead land. Hey, Jake. Look up on that hill. There's that masked man watching us. Yep. Who incarnation is he, Jake? Well, I'll tell you, Jake. He's called the Lone Regis. Well, I sure hope he comes to visit us when we're settled in our new home. Get out of there! Get away! What you have just heard is a copyrighted feature of the Lone Ranger Incorporated.


The bigger our bowel movements, the healthier we may be. The risk of low stool weight includes bowel cancer, diverticular disease, appendicitis, various anal diseases, even perhaps the healthfulness of breast tissue. From a study of 23 populations across a dozen countries, a graph of average daily stool weight versus colon cancer incidence. As you can see, once you get down around half a pound, 200 grams or so, colon cancer rates really seem to skyrocket. And once people start dropping quarter-pounders, colon cancer incidence quadruples. The length between stool size and colon cancer may be related to transit time, the number of hours it takes for food to go from mouth to anus. The larger our stool, the quicker the transit time. The easier it is for our intestines to move things along. People don't realize you can have daily bowel movements and still be effectively constipated. You can be regular, but five days late. What you're flushing today, you may have eaten last week. If you want to test it for yourself, all you need to do is eat a big bowl of beets and see when things turn pretty in pink. And ideally it should be down to the 24 to 36 hour range to reach that half-pound target.


Hi guys, this is Amol Mejari presenting a screencast of OpenEventOrganizer Android application which was developed in Google Summer of Code 2017 under the mentorship of FOSSES here. It's a client-side application for another project under the same organization. The project name is OpenEventOrganizerServer. This is login page. The fields are validated to ensure correct entries. Like if an entered email ID format is wrong, then it notifies it to the user. If user is new to OpenEvent, he can create a new account using this signup link. This is signup page. So I am logging in with a test account. This is the initial page showing all the events in the list. The events are categorized into three categories which are live, upcoming and past. The app uses side drawer menu for navigation. On selecting an event, the dashboard page opens up. All sales analytics is shown here. So toggling button is given to toggle the public state of the event. If the event is not published, it is labeled as draped in the event list. This is attendees page. All the attendees are shown in the list here with a sync tick. A double tick means that attendees checking status is up to date with the server. If the check-in request is pending, then a single ticket is shown instead. The attend is checked in by scanning a QR code from attendees ticket. This is tickets page. All the tickets are shown here are sorted in the category. Also user can create a new ticket for the event. This is about section. All the basic event details are shown here. In the settings, an option to select a time zone is given in which all the dates in the app will be shown. This is all about the app. The alpha version of the app is published on the Google Play Store. All the relevant links are provided below. Please do like the video and subscribe the channel. Thank you.


All right, let us move on to the second topic I want to talk about. So I came across this article and I'm basically going to read a bunch of this article to you because I think it's so good and so powerful. And it relates to this issue with regard to the fact that Gama today does almost all scientific funding. So funding today in academia is almost science day in academia. It's almost all, all funded by a government. Now this is an article called the intellectual and moral decline in academic research. And I highly recommend it. It's been published by the James G. Martin Center for Academic Renewal. It, the author is, let me just find the author, the author is a man named Edward Archer. And it is truly fantastic, truly fantastic. So he starts out by citing Eisenhower's presidential farewell address in 1961. And in this address, Eisenhower warned that the pursuit of government grants would have a corrupting influence on the scientific community. He feared that while American University, and I'm reading from the article, were quote, historically the foundation of a free ideas and scientific discovery, that the pursuit of taxpayer monies would become quote, a substitute for intellectual curiosity and lead to quote, domination of the nation's scholars by federal employment and the power of money. And that was an amazing statement by Eisenhower, president, I don't have much respect for generally, but this is truly a powerful, and I think true, statement by Eisenhower. And it's played out absolutely correctly. As the author notes, the relentless pursuit of taxpayer funding has eliminated curiosity, basic competence, and scientific integrity in many fields. Now think about most of the concern today of scientists is getting that government grant. It's figuring out what will trigger the government bureaucrat who approves the grant. It's making sure that whatever you're doing fits in with the agenda of the government agency that is giving the grant. Gone are the days of trying to do breakthrough science, trying to think outside of the box, trying to do something new, interesting, risky, because the government's not going to fund any of that. The government can only fund what is in a box, what is understandable to the bureaucrat, what is understandable to the specifics, the specific organization that is approving the grant. Who, by the way, did I not answer? I've answered everybody. There are two questions in the funnel waiting to be answered. I would get to them, but I answer, I try to answer everybody, so I don't understand what the, I'm in the middle of something. I'm not going to answer a super chat question while I'm in the middle of something. I'll get to it when I finish this topic. So there's constant, constant obsession about grant writing, about how to write a grant, how to appeal to the particular interests, the particular bureaucrat at the particular government agency. Critical thinking, methodological rigor, science are gone, are irrelevant. What we train graduate students today, people who are teacher's assistants, what we train them is in writing grants. They're not trained in doing science, they're trained in getting, in getting money. So this particular author is an expert in food, and he talks about public health research. So from 1970 until 2010, taxpayer funding for public health research increased 700%. You wonder why we have a deficit, 700%. And yet, the number of papers in academic journals, papers that were funded with taxpayer money that have been retracted from those academic journals, because they were shown to be fraudulent, inaccurate, unrepeatable, has gone up by 900%. So here you have a situation where the trolls are out in mass today. It's pretty, it's pretty amazing. So I'm sorry I have to stop because I have to eliminate the ability of this idiot to, to write on the chat because otherwise it, it just, it just becomes impossible for the people on there. 900% increase in the number of research articles that have to be retracted from 1970 to 2010 in public health research. Much of that due to what's called misconduct, fraud and retractions have increased so much from 2010 to 2015 that private foundations, private funders of science have actually created the Center for Scientific Integrity to check up on these scientists and Retraction Watch, a group that basically goes out and notices the retractions and alerts the public about scientific knowledge that is spread widely that then is retracted as non-knowledge. Now it has to be non-government institutions that do this because the universities are in the pockets of both the people asking for funding and of the government. Universities don't hold their faculty accountable. They don't hold the faculty accountable for fraud. They don't hold the faculty accountable for misconduct. They don't hold the faculty accountable for sloppy research, for bad research, but even for actual fraud, known actual fraud, they don't hold them accountable. And the scientists know this. Over 14% of researchers report that their colleagues commit fraud, fraud, which means they lie. And 72% report some other questionable practices. 72% of scientists believe their colleagues engage in questionable practices in their science. And we're talking about the best universities in the world. We're talking about Duke University, the eighth largest recipient of NIH funding at 475 million NIH as the National Institute for Health, government money, taxpayer money. And yet a whistleblower at Duke alleged that $200 million in grants were obtained using falsified data. They made up the data to get the grant. They have 50 papers of being retracted from work funded by the NIH.   Harvard, which is the wealthiest university in the world, received almost $600 million in public funds from the National Institute of Health and other agencies in just 2018. Some faculty, well, these funds, they could pay tuition, room, board, and books for every undergraduate at Harvard. And yet the faculty has an ever-increasing number of attractions due to misconduct or incompetence. In one case, Harvard's teaching hospital was forced to pay $10 million because its faculty had fraudulently obtained NIH funding. Of course, the penalty is only a fraction of the grants they actually get. Cornell has constantly had to investigate researchers who receive millions of dollars from the Department of Agriculture, for example. The author claims that academic research is often conducted for no other reason than to give physicians and researchers qualifications for promotion or tenure. So it's not truth-seeking. It's grant-seeking, tenure-seeking, promotion-seeking.  If you follow diet and if you follow food research, you know, you know that the research is pathetic in its inconsistency. That study after study cannot be replicated. That study after study gives us no valuable information about how to actually live our lives. Now, I think this applies to global, to climate change, sorry, not global. I mean the fact is that all climate change studies are funded by, almost all of them are funded by the government. To get money if you're a climatologist or if you're in the field, the only way to get money from the government, the only way to do research is to do it on climate change. And if you produce results that say it's not catastrophic, it's not a problem, it's nothing to worry about. You're not going to get your funds renewed. So it's a self-perpetuating type idea. Good news does not warrant more money. Bad news does. So there's a massive incentive to produce bad news. There's a massive incentive to produce news that the government wants to hear. There's a massive incentive to produce what the bureaucrats want you to produce. And what of course that results in is in a politicization, complete influence of politics over science. And people say, well, the scientists believe that the globe is warming, but I say I don't trust scientists anymore. Because science has been politicized, not only on the issue of climate change, on the issue of food, on the issue of climate change, and on many, many other issues, anything that relates to public policy, I don't trust the scientists because they've been corrupted by politics. Now what is the solution to this? Now the author of this article, unfortunately, gives three very lame solutions. I won't bore you with them. There's only one solution to it. And it's to advocate for real capitalism. Somebody on the Super Chat said, I can't believe he said Ted Cruz doesn't believe in capitalism. Yeah, he doesn't, because capitalism is the complete separation of state from economics. But in this case, capitalism means the complete separation of state from science. The only way to stop this corruption is to eliminate the funding of science by government. Let's privatize science. Let's completely privatize the funding of science. Let's take the billions and billions and billions of dollars that go to the NIH, that go to all these research institutions, that go to all these universities, and let's return them to taxpayers. Let's eliminate them from the federal budget, and from state budgets, if the states do this to the extent that they do this. What we need is a constitutional amendment that separates science from state, that prohibits the government from getting involved in science, from prohibiting the government from funding any scientific pursuits. Until we have that, we are doomed to continue on the spiral of ever more corruption in the field of science. And it truly is sad, because science is the fountainhead of the enlightenment. It was the scientific revolution that led to the enlightenment, and ultimately the political revolution, and to capitalism. It is science that is in our culture representative of reason, representative fact, representative of rationality and reality. If science is corrupt, if science, we can't trust scientists, then we truly cannot trust anybody. So I think this idea that government corrupt science is so, I mean the beauty of this article is it documents so well and so thoroughly. And we know, and there have been other studies that have shown, that the number of scientific papers that experiments are described that could be recreated, shrinking dramatically. And that should really scare you. The decline of science might be the most scary thing happening in the world today. What we need today, what are called the new intellectual, would be any man or woman who is willing to think. Meaning, any man or woman who knows that man's life must be guided by reason, by the intellect, not by feelings, wishes, wins or mystic revelations. Any man or woman who values his life and who does not want to give in to today's cult of despair, cynicism and impotence, and does not intend to give up the world to the dark ages and to the role of the collectivist broad. Using the super chat and I noticed yesterday, when I appealed for support for the show, many of you step forward and actually supported the show for the first time. So I'll do it again. Maybe we'll get some more today. If you like what you're hearing, if you appreciate what I'm doing, then I appreciate your support. Those of you who don't yet support the show, please take this opportunity, go to uranbrookshow.com, slash support or go to subscribestar.com uranbrookshow and make a kind of a monthly contribution to keep this going. I'm not sure when the next...


Hi everybody. Welcome to the first data AI and society seminar talk of 2021. I'll start by honoring the first Australians who are the traditional custodians of the land on which we're meeting. For those of us calling in from an you that's the none of all and memory peoples, and we should pay our respects to their elders past present and emerging and acknowledge that sovereignty was never seated. I'm pleased to introduce Brian head and who has recently moved an you as associate professor of philosophy to give our first talk. Take it away Brian. Okay, thanks Serena. Let me just share that. Is that all working properly. You can see my screen. Okay, great. So I'm going to be talking about statistical criteria of algorithmic fairness, and the plan for today. Let's see. There we go. The plan for today is I'm going to start off by looking at the most famous case study of algorithmic fairness which probably many of you have seen multiple times namely the case of the compass algorithm. I'm going to survey some proposed statistical criteria of algorithmic fairness. Then I'm going to state some impossibility theorems, not proven by me proven by others. But these impossibility theorems show that these criteria these proposed criteria of fairness are not jointly satisfiable, except in marginal cases where we can predict things perfectly, or where base rates are equal across the relevant groups. So that's a very informative question. Do we take these impossibility theorems as showing that fairness dilemmas are inevitable. We basically can't help but being unfair in some respect or another. Or do they invite us to look again at the criteria and decide that some of them are not in fact necessary for fairness, after all, and we have to separate the wheat from the chaff. The only one is that only one of the criteria that I'm going to consider is plausibly necessary for fairness. And I'll do this by using a simple case in which something that I suggest is a perfectly 100% fair algorithm can violate all of these criteria, except, except for the one. And moreover, it can violate these criteria simultaneously, and it can do so even when base rates are equal across the relevant groups. And that's important because unequal base rates is often taken to be the root of all our problems in this domain, but I think it's actually a bit less important than it's often taken to be. Okay, and then I'm going to close with some general reflections on algorithmic fairness. Compass is an algorithm used to predict recidivism recidivism is committing a crime, and it's used in jurisdictions around the US to make decisions about bail parole alternative treatments and the like so it's used in various elements of their criminal justice system. In a major report for ProPublica. These authors and when at all argued that compasses quote biased against blacks to quote their headline. And what they argued was that the reason why compass was biased against blacks is that compass yielded a higher false positive rate for blacks and for whites.  Okay, but then that's not the end of the story and there were some responses so the north point the company behind compass argued that there was in fact no bias in their algorithm because compass was equally accurate for blacks and for whites in this sort of technical statistical sense that I won't go into. Another group of authors florist at all argued that there was no bias because compass was equally calibrated for each group. That's to say that for each risk score. This is the percentage of individuals assigned that risk score who recidivated was roughly the same for blacks and for whites. So what it looks like it's happening is ProPublica tied fairness to one set of criteria, equal false positive rates and equal false negative rates, while north point and florist at all tied it to a different set of criteria so right as I said fairness to equal false positive rates and equal false negative rates across groups north point said fairness requires just equal accuracy in this technical sense florist and co authors argued that fairness requires just calibration within groups. So which criteria are necessary for an algorithm to be fair or unbiased. That's the question for today. So I'm going to be focusing on algorithms like compass that make predictions first and foremost, and not decisions. And that that I think is important that I'm focusing on predictive algorithms. In the first instance, and these algorithms take as input a set of known features which is often called a feature vector, and the output either a risk score, or a binary prediction, yes no positive negative, or both. And for simplicity, let's allow the risk score to fall in the interval 01 so that we can think of it as something like a probability that the individual caught falls into what's called the the positive class where positive might be recidivating, or it might be defaulting on a loan, or, you know, not finishing university or whatever it depends what we're trying to predict. Okay, and I'm going to focus on algorithms that output both a risk score in the 01 interval, and also a binary, yes, no positive negative prediction. Okay, so some criteria fairness might concern the inner workings of the algorithm so for example, you might have a criterion which says that the algorithm has to be blinded to race, or more generally to protected class status right. That would concern the concern the inner workings of the algorithm. But other criteria just require that certain relations between prediction and actuality be the same across the relevant groups, and that's what I'm going to focus on here. I'll call these statistical criteria of algorithmic fairness, and they're attractive in part because they don't require us to look at the inner workings of the algorithm like the date on which it's trained or how it operates. And that's, that's important because the inner workings of the algorithm might be proprietary, or potentially just opaque, right, we might not be able to just stare at a huge amount of code and figure out from that whether it's fair or unfair. But with statistical criteria, we just look at what was predicted, and what happened, and how that all shook out.  Do not worry about remembering all the details I'm going to survey 11 of them. I'd recommend if your eyes start to glaze over, just try to focus on a few two or three and remember those and then I think the general lesson will be pretty clear even if you don't remember all the details. So calibration within groups. This criterion says that for each possible risk score, the percentage of individuals assigned that risk score who are actually positive must be the same for each group, and also equal to that risk score. So the idea here is that fairness requires that a given risk score, quote unquote, mean the same thing or have the same evidentiary value, regardless of the grouping question. So this criterion governs risk scores but it has, it can be generalized to the case of binary predictions, and these are called criteria of equal positive predictive value and equal negative predictive value. So equal positive predictive value says that the percentage of individuals who are predicted to be positive, who are then actually positive must be the same for each group. And equal negative predictive value says the percentage of individuals individuals predicted to be negative, who are actually negative should be the same for each group. So this requires that a prediction of positive mean the same thing or carry the same evidentiary value for each group in question and similarly for a prediction of negative. Okay, next we've got equal false positive rates and equal false negative rates. So equal false positive rates says that the percentage of actually negative individuals who are falsely predicted to be positive must be the same for each group. And similarly, the percentage of actually positive individuals who are falsely predicted to be negative must be the same for each group. And these criteria can be motivated by the thought that individuals who behave in the same way they either recidivate or don't write, they should on average on balance be treated the same in terms of how they're predicted, regardless of the group to which they belong. These criteria govern binary risk scores but they also have natural analogs for risk scores. And these criteria that are known as balance for the positive class and balance for the negative class. So balance for the positive class says that the average risk score assigned to those individuals who are actually positive should be the same for each group. So the negative class says that the average risk score assigned to those individuals who are actually negative should be the same for each group. And so again, you know, take the take the individuals who are actually not going to recidivate the ideas on average. You know the blacks who don't recidivate should on average be assigned the same risk score as the whites who don't recidivate, and so on. Okay, then we've got a few just a few final criteria some people will defend the claim that fairness requires an equal ratios of the false positive rate to the false negative rate. So ratio of false positive rate to the right rate to the false negative rate should be the same for each group. And the idea here is that fairness maybe requires assigning the same relative weights to false positives and false negatives regardless of the group in question. Equal overall error rates. So this would say that just in this set in the following sense the algorithm should just be overall equally accurate for each group. So the number of false positives and false negatives divided by the total number of individuals should be the same for each relevant group. Statistical parity then says the percentage of individuals predicted to be positive should be the same for each relevant group. So this is a really controversial condition and in fact, most people in the literature reject it because if base rates are unequal across the groups that is to say, if the groups differ with respect to the percentage of members of that group who are positive, who say go on to commit a crime, then statistical parity would be violated even by an omniscient predictor who could perfectly predict everyone's behavior. But we can do better by moving to this final criterion, which says that the number of individuals predicted to be positive, divided by the number of individuals who are actually positive, must be the same for each relevant group. So this final criterion allows that you can have a different percentage of each group predicted to be positive, but only provided that those differences in the percent who are predicted to be positive are driven by underlying differences in the base rates. So you can predict whites to recidivate at twice the rate that you predict blacks to recidivate provided that whites have twice the base rate of committing crime as blacks do, for example. Okay, so that's a mouthful. It's a ton of statistical criteria. I'll just put them up here again. Like I said before, don't worry about trying to remember all of them or get your head around each and every one. What I want to do is just give you the sense that there's a lot of different criteria out there. They all have some sort of prima facie motivation, but we're going to face a problem in a second where we learn that they can't all be satisfied together, right? So we've got these three statistical criteria for risk scores, calibration within groups, balance for the positive class, balance for the negative class. And then we've got eight that govern binary predictions. So equal false positive rates, equal false negative rates, equal positive predictive value, equal negative predictive value, equal ratios of the false positive rate to the false negative rate, equal overall error rates, statistical parity, and equal ratios of predicted positives to actual positives. Impossibility results. So there are several theorems in this literature that have a sort of very pessimistic feel, right? So they say that we can't have it all. We can't satisfy all of these criteria fairness together. So the first theorem says that the criteria one, two, and three, that's to say calibration within groups, balance for the positive class and balance for the negative class are not jointly satisfiable unless either base rates are equal across the groups. So again, unless the same percentage of each group is in fact positive, or if prediction is perfect. But those are very marginal unlikely circumstances to get into. John Kleinberg and some co-authors, and in some ways this is like the seminal paper in this literature. A related theorem due to cold to cova says that criteria for five and six are not jointly satisfiable again unless base rates are equal, or prediction is perfect. So this is equal false positive rates, equal false negative rates, or sorry. Yeah, equal false positive rates equal false negative rates and equal positive predictive value. And then there's another theorem saying that no algorithm can satisfy more than one of the conjunction of four and five that's equal positive predictive value equal fall negative predictive value, six and seven that's equal false positive rates and equal false negative and 11 which is equal ratios of predicted positives to actual positives, again unless base rates are equal or prediction is perfect. Okay, now I'm not going to go through the proofs of these or really say a whole lot more about them. The main takeaway is just, these theorems say that we can't have it all, we can't satisfy all of these intuitively attractive criteria fairness, except in these very marginal scenarios where base rates are equal, or we can just predict things perfectly. Right. So we can't have it all. So what do we say about that how do we respond to impossibility. I think the prevalent response in the literature is a pessimistic one. It's to say that fairness is impossible, except in marginal cases fairness dilemmas are all but inevitable. We cannot help but be unfair in some respect or another. So, Kleinberg and co authors say, any assignment of risk to doors, can it principle be subject to natural criticisms on the grounds of bias. That's the conclusion of their impossibility result. Gabriel Johnson says that these results show that there is no such thing as an unbiased program. Sandra Mason says, they show that race neutrality is not attainable. And Burke and co authors in an important survey article, say the implications of the impossibility results are huge. The goal of complete or race or gender neutrality is unachievable. There's also a more optimistic response, you could look at the impossibility results and take them to show that just not all of our criteria one through 11 are in fact genuine necessary conditions on fairness. Some are just specious. Some look like they're important for fairness, but in closer inspection, they're not. We could if we were sort of tempted by this optimistic response we could go criterion by criterion and subject each one to scrutiny to try to separate the wheat from the chaff. Or what we could do is find a perfectly 100% fair algorithm and just see which criteria it can violate. And if some criterion can be violated by a perfectly 100% fair algorithm that proves that that criterion isn't necessary for fairness. Now this methodology this ladder methodology might seem impractical since you know you might think it's always going to be controversial whether a given algorithm is fair or not. So how could you even implement this, maybe the best we can do is go criterion by criterion and stare really hard at each one and try to figure it out. But I think we can do better. I think we, we can in fact employ this second sweeping methodology. So here's my argument. I'm going to argue that none of the statistical criteria, except perhaps the first one calibration within groups is necessary for algorithmic fairness. And this is because all of the other criteria can be violated by a predictive algorithm that is perfectly 100% fair. And moreover, all these other criteria can be violated simultaneously by a perfectly 100% fair algorithm, and moreover they can be violated simultaneously by this perfectly fair algorithm, even when base rates are equal across the relevant groups. Okay, so here's the case people coins and rooms.  The individual in our population is randomly assigned one of these coins. And then each individual is randomly assigned to one of two rooms and be intuitively think of them as our demographic groups. And our aim is to predict for each individual, whether their coin lands heads or tails. And this is to say whether their behavior is that of a heads person or a tails person. Right, so we're predicting the behavior we're predicting is heads or tails. Right, we've got these demographic groups room a and room B. Okay, so that's that's our task. Now luckily for us, each coin comes labeled with its bias towards heads so it just says how biased towards heads, it is. It does that with a with a number between zero and one representing the objective chance that it lands heads. Okay, here I submit is a perfectly fair and moreover uniquely optimal predictive algorithm for each person take their coin and read its label. If the label says X assigned that risk person a risk score of X. If X is greater than point five, make the binary prediction that the individual is a heads person. And if X is less than point five make the binary prediction that the individual is a tails person. What about when X equals point five I mean then we face an issue of do we just randomize our prediction, or do we arbitrarily assign them all a prediction of heads or what. And then a sidestep that issue by just assuming that none of the coins is labeled point five none are perfectly fair coins. So that just skirts that issue. Okay. Now, this algorithm, I submit is perfectly 100% fair, its risk scores are not sensitive to the room from which a person comes. And on which its risk scores and predictions are based, namely the labeled bias of the coin is clearly the relevant one to focus on, and is not a proxy for room membership. And not only is the algorithm not unfair to any individuals in virtue of their room membership, but there's seemingly no unfairness anywhere at all in the situation. Of course there's plenty of randomness, but there's no unfairness, I take it. So, and moreover, the algorithm is uniquely optimal so no alternative predictive algorithm could be expected to do to do as well or better at predicting heads people versus tails people. Now, here's the lesson. Our fair algorithm cannot violate criterion one. It cannot violate calibration within groups, at least assuming that relative frequencies match coin biases so that, you know, about 90% of the coins labeled point nine. In fact, Landheads, about 30% of the coins labeled point three Landheads and so on. So assuming relative frequencies match coin biases, our algorithm can't violate calibration within groups, but it can still violate all of the other criteria two through 11. It can violate them simultaneously, and it can violate them simultaneously, even when base rates are equal across the two rooms. That's to say, the same percentage of people in each room are heads people versus tails people. So here's a case right here's here's just filling in the numbers. So let room a happened to have contained 12 people with coins labeled point seven five. So these people are all assigned risk score point seven five and predicted to be heads people. And because we're assuming that relative frequencies are going to match coin biases, 75% of them, that is to say nine of them are heads people. And it also contains eight people with coins labeled one eighth or point one to five, and they're all assigned that risk score point one to five and predicted to be tails people are negative. And in fact, one of them is a heads person right one out of the eighth, one out of the eight people with coins labeled one eighth, in fact as a heads person. Let room be contained 10 people with coins labeled point six, they're all signed risk score point six and predicted to be heads people. And in fact, six of them are heads people. And it also contains 10 people with coins labeled point four, they're all assigned risk score point four and predicted to be tails people or negative. And in fact, four of them are heads people. Right. So that's just the setup. And note that in the base rates are equal across the two rooms in each room, 10 people are heads people out of a total of 20 people. So each room has a base rate of point five. So we've got equal base rates here. Okay. Now, just crunching the numbers. We're going to see that this given this population as input are perfectly 100% fair algorithm is going to violate all of the other criteria of fairness except for calibration within groups. I just have a table here. I won't go through each and every one of them. But, you know, what we look at is, you know, what's the average risk score that was assigned to people who are actually positive. Well, for room a it's point six eight seven five for room B it's point five two. Similarly, you get a difference for average risks who are assigned to people who are actually negative. The false positive rate of 3 tenths for room a for 10 for room be a false negative rate of 1 10 for roommate for 10th for room B different positive predictive values different negative predictive values. You know different ratios of the false positive rate to the false negative rate different overall error rates different ratios are predicted positives actual positives and different percentage of people who are actually predicted to be positive. which is perfectly fair. If it works on this population, it's gonna violate all of our criteria except for calibration within groups. So if you agree with me that the algorithm is perfectly fair, you have to reject all of these other criteria as necessary conditions on fairness. They're specious. Okay. That I just said. Now note that I don't claim that calibration within groups is sufficient for fairness. I think that's an open question. It's just that it's the only one left standing in the face of my perfectly fair algorithm. And I also, so one lesson here is that when the other criteria, other than calibration or violated, this could be due to unfairness in the algorithm or it could be due to the distribution of evidence and risk in the population and not to unfairness in the algorithm. It's also possible that violations of these other criteria provide evidence for unfairness without actually constituting or entailing unfairness. I think that's very plausible that a violation of one of these other criteria still could provide some prima facie evidence that there's unfairness, but that's not to say that the criterion is a necessary condition on fairness. Okay, so I'm gonna close briefly with just considering two upshots of this argument or two lessons. The first has to do with inframarginality. So most of the criteria violated by my fair algorithm fail for reasons related to the so-called problem of inframarginality. The idea here is that at least an important part of fairness is treating marginal cases the same for different groups. So for example, the least suspicious African-American person who would be predicted to be carrying contraband should be exactly as suspicious as the least suspicious white person who would be predicted to be carrying contraband. The least promising woman who would be predicted to succeed in the job should be exactly as promising as the least promising men who would be predicted to succeed and so on. But many of the criteria that we've considered and now rejected concern in part how things turn out with non-marginal or inframarginal cases. So for example, if you want balance for the positive class, you want people who are actually positive to be assigned the same average risk score, well that concerns everyone who's actually positive, not just the ones who are close to the margin where you would tip the balance between predicting one way versus predicting the other way. It also concerns how many clear cases there are. Now in real life, it's always gonna be controversial whether a given case is or ought to be considered a marginal case. But with coin flips, things are clearer. So basically what's happened is a lot of our criteria were violated simply because room A contained relatively few marginal or unclear cases, everyone had a coin with either a heavy bias towards heads or heavy bias towards tails, whereas room B contained predominantly marginal or unclear cases, everyone had a coin that was roughly pretty close to 0.5. So they were just harder to predict. And then a little bit of asymmetry just gets us to violate the other criteria. The second thing I wanna talk about and this is the sort of closing point is the importance of the fact there are multiple possible intervention points in any real life system like this. So when a predictive algorithm is used to make decisions with distributional consequences that we deem to be unfair or unjust or just otherwise disvaluable, this doesn't necessarily mean that the predictive algorithm itself was unfair or biased. The unfairness or disvalue or injustice could instead lie with the background conditions of society or with the way that decisions are made on the basis of the predictions out quoted by the algorithm. And so as a result, the best response also might sometimes be not to modify the predictive algorithm itself, but rather to intervene elsewhere. And here's an analogy that I think is illuminating. Suppose we face two problems in our city, traffic and inequality. And we're deciding whether to adjust, adopt a congestion pricing scheme where we aim to reduce traffic through higher tolls or fees for driving during rush hour. Okay, I think congestion pricing schemes are really attractive, but they're also really controversial in part because one might worry that congestion pricing schemes would be unfair to low income people, right? After all, low income people would have less flexible work hours, have to drive for their work and therefore they'd wind up paying a higher percentage of their income in these congestion fees. So we might in response be tempted to either just abandon congestion pricing altogether or adopt some more complex scheme where low income drivers are exempted from the fees or something along those lines. But there's a better response available. We should adopt the original congestion pricing scheme along with something like an across-the-board tax cut for low income people or insert your favorite anti-poverty or wealth redistribution scheme here, right? We can do multiple things at once. We can walk into gum. We have multiple goals, reducing traffic and reducing inequality, but we've also got multiple points where we can intervene and we shouldn't ask the congestion pricing scheme itself to do all the work addressing traffic and inequality at the same time. Now, of course, maybe it's gonna be politically infeasible to adopt this more optimal divide and conquer strategy. So it might be second best to adopt the more complex version of congestion pricing that exempts low income drivers, but we shouldn't be confused into thinking that in fact, fairness itself requires this second best scheme. I think the fairest and best response is just to do the congestion pricing with an across-the-board tax cut for low income people, right? Divide and conquer, intervene at multiple points. Okay, now similarly with predictive algorithms, right? We've got multiple goals. We want fair and accurate predictions. We want fair decisions. We want adjust and flourishing overall society, but we've also got multiple points where we can intervene. We can modify the predictive algorithm. We can modify the way decisions are made on the basis of predictions or we can intervene elsewhere in society. We shouldn't necessarily ask the predictive algorithm itself to achieve all of these goals on its own by itself. Now, of course, again, if it's politically infeasible to adopt some fair predictive algorithm where while making suitable big picture, heavy-duty interventions elsewhere, it might be second best to fiddle with or modify the predictive algorithm to get the distributional results that we want. But again, we shouldn't be misled into thinking that fairness itself requires the predictive algorithm to be tailored to get these results on its own. Okay, now in actual fact, I think there may be good practical reasons to try to achieve distributional outcomes in part through our predictive algorithms. Algorithmic designers don't have the authority typically to intervene elsewhere. So if you're just designing a compass algorithm, you don't have the authority to pass a sweeping nationwide criminal justice reform act or pass reparations or do massive changes to the tax code, right? You're just able to intervene in a small one area. And you might think it's also more just unlikely that these big sweeping changes will be made. But I think it's a hard empirical matter how predictive algorithms should be designed in light of anticipated failure to take more drastic steps to address these inequalities. And it's not gonna be something we can capture with a simple statistical measure like saying that false positive rates should be equal across the two groups. One reason, and this is the final point for this that we can't capture this complex sort of goal we might have with a simple statistical measure is that whether the violation of some statistical criterion is bad for some group overall or not, that's gonna depend on the payoff structure associated with the use of that algorithm. It's gonna matter what's the cost of a false positive for each group or a false negative or a given risk score. What are the payoffs? What are the effects not only for people whose behavior is being predicted by the algorithm but also for third parties, right? And all this stuff is gonna vary case by case. So it's not gonna be something that we can capture with a simple statistical measure. Okay, so in conclusion, I've surveyed a bunch of statistical criteria of fairness that have been discussed in the literature. We've seen some impossibility results saying they can't all be satisfied together. I've given a case which I think shows that at most the first criterion calibration within groups is necessary for fairness, none of the others are necessary for fairness. So that's the optimistic response to the impossibility results. And I've closed by talking about the problem of inframarginality and the importance of the fact that we have multiple points where we can intervene in a system. Thank you. And here are some references. And I'll stop sharing my screen. Thank you so much for that lovely talk. I now now open the floor to questions. So those of you in the audience, please use the Q&A function. And panelists, you can use the raise hand feature. And I'll start with Atuza. Thanks very much, Brian. So I was wondering if you can say a little bit more about the philosophical or conceptual basis of this 100% fair algorithm. Because I fear that we can have some worries like similar or parallel to worries about ideal versus non-ideal theories of fairness or justice. And so I was just wondering, it seemed to me that you are adopting maybe implicitly or maybe you know, what is this ideal conception of fair algorithm? And so if that is the case, if you are kind of like using this ideal theory and then you're saying that for that ideal theory or that ideal conception, this statistical criteria or many of them are not relevant, then someone who is more the defender of a non-ideal conception of algorithmic fairness and wants to develop some accounts of fairness that take care of the context relevant features and sophisticated social democratic values would say that, okay, maybe this is great for this super ideal conception of fairness and algorithmic fairness, but the ramification of it to the real concerns of practitioners who care about algorithmic fairness are not super evident. Okay, yeah. So one thing is certainly that my case involves a pretty cleaned up idealized model, right? So I don't claim that real life cases like the case of the compass algorithm look a lot like my case of people coins and rooms for all kinds of reasons, right? But, you know, human behavior is not like random in the way that coin flips are. We don't have the same kind of evidence about individuals as we have in my case about the coin flips. And also in real life cases, you know, well, my demographic groups, the rooms are sort of, not the basis of historical oppression. They don't play any sort of causal role in determining the feature vectors that we have available to base our predictions on or, and they also don't have any causal role to play in, you know, determining people's behavior. Whereas all that stuff could be true in the case of other, you know, black and white and we're talking about crime, right? There's gonna be, there are demographic groups or the basis of historical oppression and they play a causal role in determining who's got what features and also how people behave in all kinds of ways. So what I would say in response is that what I'm doing with this model, this idealized model is trying to abstract away from some of these issues to try to focus on the issue of is false positive, equal false positive rates, for example, a necessary condition on fairness, right? And I think it's useful then to abstract away from these facts that, you know, in real life cases, the demographic groups are the basis of historical oppression, for example, right? It's also the case that to argue that something is not a necessary condition on fairness, all you need is one case, right? You just need one case where the criterion is violated, but there's no unfairness and that's what I've aimed to provide. So I take that I've just proven that these are not necessary conditions on fairness. Now I think someone, you know, so someone might listen to my talk and they're, you know, they're trying to design their own predictive algorithm and try to ensure that it's fair. Now it doesn't give a recipe for how they should do that, right? It doesn't, because, you know, their case doesn't involve people wearing, you know, their objective risk label on their forehead or anything like that. So what do they do? Well, I take it the lesson isn't that I haven't aimed to give them a recipe for designing their own algorithm. What I have said though is, or what lesson they can take when they're doing this kind of stuff in a non-ideal scenario is say, well, okay, we probably really should worry about making sure that it satisfies calibration within groups. That's at least plausibly necessary for fairness. But if it violates equal false positive rates or equal negative predictive value or equal ratio of false positive rate to false negative rate, that doesn't mean that our algorithm is unfair. It could still be fair. And we, you know, we shouldn't, you know, try to ensure that somehow or other we're gonna get equal false positive rates or somehow or other we're gonna get equal ratio of the false positive rate to the false negative rate. Cause those are just not necessary for fairness. Now, what I really think is also I'm fairly consequentialist about these things. So I think I wanna see a lot more sort of empirical social science kind of work to kind of try to figure out what do we want to, you know, suppose we are concerned with wanting our predictive algorithm to, you know, benefit historically disadvantaged groups or harm them as little as possible, then I think it's just a hard empirical question how to do so, right? But I don't think it's gonna boil down to any trying to ensure the satisfaction of some simple statistical criterion. Right, next question, Seth. Okay, thanks Brian for your talk. That was fantastic. So I kind of wanna pick up on those last couple of points and just sort of press you on whether it's actually the case that someone should take those two lessons from the paper. So I thought that, you know, like you were careful to say that calibration within groups is perhaps necessary for fairness, but it's perfectly possible when imagines that someone could come up with another case that was, you know, given the creativity and imagination involved in coming up with this case, something else similarly creative and imaginative, which leads to a violation of calibration within groups, at least for all that you said in the paper, right? So that seems something that's kind of possible. In fact, you could have a real life case, for example, that didn't exhibit calibration within groups, but which we thought was perfectly fair, conceivably. And then on this point about, you know, if we show that these other conditions aren't necessary conditions on fairness, then we shouldn't worry so much if they're not satisfied. I'm not sure that that follows either because it could be the case that some, you know, satisfying some statistical property is importantly productive of the values that we want to achieve when we're aiming at fairness. It might not be a necessary condition on fairness in as much as we can imagine a perfectly fair scenario with certain features that doesn't exhibit that statistical property, but it could also still be, you know, like a good-making feature with respect to fairness in many other cases and also departures from it could be a bad-making feature with respect to fairness. So then I wonder whether, you know, one should take comfort if one's algorithm doesn't satisfy one of these criteria from the notion that it isn't a necessary condition on fairness. Yeah, good, excellent question. So I think on the first case, yeah, so it certainly, I haven't foreclosed the possibility that someone could find another case where there's a perfectly fair algorithm that violates calibration within groups.  And I think, you know, you can't predict in accordance with, you know, known sort of objective risk and violate calibration within groups. So if you think that that's, you know, I would sort of want to resist. So any, if you give me an algorithm and it violates that calibration within groups, I would resist the claim that it's perfectly fair, right? Cause it can't be predicting in accordance with sort of known objective risk. But yeah, having said that, I mean, I haven't definitively foreclosed that possibility. I should also add one reason you actually might want to reject calibration within groups as necessary is actually that it might conflict with blinding this condition that says that the algorithm should be blinded to group membership, right? So there would be some cases where the only way to satisfy calibration is to base predictions partly on group membership, right?  The only way to achieve calibration then is to base predictions partly on room membership. And then, so then we have a conflict between blindness and calibration, and you get aside which one goes, and that's it. So one other feature that might, you know, one other problem that might be raised for calibration within groups as well then is if the sort of the boundaries of the groups are determinate and there are problems with, are indeterminate and there are problems with the actual assignment of groups. Yeah. That's also a situation where, you know, it might carry problems with it. Yeah, yeah. Now your second point was maybe I was too quick in fully dismissing some of these other criteria as saying that algorithm designers just don't need to really worry about it. And perhaps I did overstate that a little bit. So I think your point is well taken that as I would put it, as a contingent matter, and depending on the context, it might be that satisfying one of these statistical criteria is important for fairness. But the gloss on that, so I acknowledge that that's a possibility. What I would wanna say about that is that it's still not that say equal false positive rates is sort of constitutive of fairness. What I would wanna say is, you know, maybe we've got some independent gloss on fairness, you know, here's a crude version, right? Fairness requires, you know, increasing the aggregate wellbeing of the historically disadvantaged group. Now that's obviously much too crude to be a full analysis, but just suppose that we're working with that. But then it's like, it'll be the sort of thing where like, well, depending on the case, it could be that equalizing false positive rates redowns to the aggregate benefit of the historically disadvantaged group. But that's gonna crucially depend on this bridge principle linking satisfaction of the statistical criterion with effects on sort of aggregate wellbeing of the disadvantaged group or something like that. So I would, maybe what I should have said is just, if you're an algorithm designer and you sort of worry, hey, my algorithm violates one of these other statistical criteria, I think it's maybe more, maybe I shouldn't have said, oh, just don't worry about it. Maybe I should have said, look again and try to think, is there some contingent relation between satisfying or violating this criterion and something else that we independently care about? That's fairness. So I mean, in a way one could say then that the, maybe there aren't any real standards of statistical fairness as such. Maybe fairness is a broader value that any given kind of algorithmic intervention can only kind of touch on kind of tangentially. And maybe there are no, I mean, it's possible too that like, so like Atuza was saying, perhaps there's no real sense in which something can be statistically fair, intrinsically. It's much more a question of fairness as a property of societies rather than of statistical methods. Yeah, and it's worth also just bringing in, so I've talked about fairness, but it's certainly related to a notion of discrimination. And there is a literature actually in legal scholarship distinguishing between discrimination as disparate treatment and discrimination as disparate impact. And I think if you tie, say discrimination or unfairness to disparate treatment, then at least my algorithm counts as perfectly fair in that sense, right? Cause it really isn't treating people differently depending on their group membership. But disparate impact is just a matter of, does it kind of unbalance, negatively affect some group in a somewhat unjustified way, right? And that's gonna be just super contingent and it's not gonna be, yeah. So if you buy into disparate, I mean, there's a debate in the law about whether disparate impact is discrimination, right? But if you say it is discrimination or it's unfair, then yeah, it's just gonna be very contingent which things are unfair because it's gonna be, it's gonna depend on whether some fact about your algorithm actually yields a disparate impact or not. And that's gonna be case by case. So we have a question from Kathy Reed which asks, are there lessons or contributions here to emerging best practice for machine learning practitioners? Is your argument by extension, reducing the responsibility or accountability that machine learning practitioners have for ensuring fairness in models? That's a good question. I wouldn't wanna quite say that I wanna reduce their responsibility. I think I wanna shift their focus a little bit, right? So again, I mean, this is kind of piggybacking on what I was saying to Seth. I think it's plausible to think that they should really try to make sure that they satisfy calibration. Although I haven't fully argued for that climate. Again, that was just the last man standing in the face of my argument. But as for the other criteria, I think what they need to do is not just focus on simple statistical criteria but focus on the sort of contingent effects of the use of this algorithm on different populations. And that's gonna, basically it's gonna require a methodology that's less kind of a priori statistics, right? And more empirical social science, right? So, you know, we need to know, for example, for compass, right? I don't wanna foreclose the possibility that compass is unfair, but I feel like if it's calibrated then what we need to see to know whether it has any other objectionable features isn't just whether it violates, whether the false positive rates are unequal across the groups. I wanna know sort of what on balance are the algorithms effects on the African-American population and the white population, right? And that depends again, not just on how it behaves with respect to people who actually get fed into the algorithm but also effects on third parties, right? How does it affect crime in the broader community? How does it affect people's behavior and incentives? So I think it's not, I don't wanna lessen responsibility. I wanna just shift the focus of it. Like basically these impossibility results are less important than I think they're often taken to be. Okay, next question from Jenny. Yeah, hey Brian, great job. That was a really interesting talk. So I guess one thing I wanna sort of push your question is from kind of the very foundations of what you were saying and sort of this juxtaposition of pessimism and optimism with regard to data bias. And so sort of the way that you set it up is if we can't be statistically fair, this is sort of, if fairness is impossible and bias is inevitable, then this is sort of a pessimistic view of fairness to machine learning. I wonder about potentially repositioning that pessimism into an optimism about alternative pathways forward. And I think this builds on a bit of what others say a bit of what others have been saying.  Right, and to say, if we are inevitably biased, how do we channel that bias in a way that surges resources to those who've been historically disadvantaged? Just creating or constructing a just society, if not a fair one? Yeah, I think I'm quite, right. So one point is just that I framed this response that a lot of people have as pessimistic. And I think that is actually the flavor that you get in there, papers, but yeah, you could agree with the point that fairness dilemmas are inevitable and say, hey, that's not so bad because we just wanna make sure we're unfair in the right ways, right? And we wanna make sure we're biased, let's say, but biased in favor of historically disadvantaged groups, right? It's biased, but it's biased in the right direction. Yeah, that's certainly one less pessimistic reading you could have on the alternative interpretation of the impossibility results that I suggested. But I just wanna say, I don't, I'm not convinced that fairness dilemmas are inevitable. Like I resist that conclusion because I don't think it's been shown. I think what's been shown is that you can't satisfy all of these criteria together, but I just don't think they're all necessary for fairness. So for me, it remains to be seen whether fairness dilemmas are inevitable. But I guess too, and I think there's other hands, but I guess, I mean, I guess too to sort of push on that a little bit more, whether or not like it feels like that sort of hanging on by fingernails to fairness, right? So if we say I don't accept that fairness is impossible, right? Why are we hanging on to fairness as the value rather than say just redistribution of resources, right? So like, if fairness is a real struggle to hang onto or we're sort of fighting to keep it, why? Why fight to keep it? Okay, yeah, yeah. So, say, maybe I'll make two points. One is actually, I'm very sympathetic to like not talking about fairness as much and instead talking about just redistribution of resources and so forth. And in fact, I would go further, like I'm a consequentialist, so I'm happy to talk about just optimal promotion of value. But I also don't think that, if you do wanna talk about fairness, I don't think it is the sort of thing where we're hanging on to fairness by a thread, right? And that it's really hard to satisfy. I actually think if you buy my argument, like for everything I've said, it might be pretty easy to satisfy. Like maybe all you gotta do is satisfy calibration. And certainly you don't have to satisfy all these other things. So I think if I were a sort of a certain kind of non-consequentialist who really cares about fairness, I would take away from my talk that, hey, this is a lot more, I think we really can be fair, right? On the other hand, if you're a consequentialist, like I really am, then it's like, well, okay, yeah. Fairness, I think then the lesson is, if fairness isn't as hard to achieve as you might have thought, but also we should independently think that fairness isn't the super important notion in the vicinity. Yeah, great. Yep, that's where I was going. Thank you. Okay, we have another question from Seth. Sure, yes, and anyone else who wants to go should, but so I just was thinking about the sort of walking into chewing gum point that you're making, Brian. And this connects up a bit with Cathy's point as well, but I guess the notion that we should try to kind of remedy the shortcomings of algorithms or shortcomings of any particular intervention by thinking about social policy more generally, obviously I completely agree with that. And I completely, I guess I would add as well that you don't necessarily want a particular group of kind of technological elites making social policy choices in the way in which they design these algorithms. There's a question of legitimacy there as well. At the same time, the sort of the phenomenon that emerged out of the Compass algorithm is kind of, it occurs in many, many different places where the similar types of algorithms being used and they're being used in an increasing number of different places. Similar underlying phenomena lead to all sorts of things from the ways in which silly apps that take your face and turn it into an oil painting work through to sort of things with much higher stakes, through to things like where, what type of ads you receive from Facebook or whatever. So I do wonder whether you might wanna be, whether you would be happy kind of sticking with the central point, that as far as the actual algorithm design goes, it's a matter of calibration within groups and you're good, or whether you would sort of want to take, whether you think there's anything general that one can say, because the problem seems to be pretty general and it's not only the problem of the background injustice that exists, there's also something that just, you take these similar methods, you apply them in lots of places, you get lots of similar problems, we should be able to aim at something to do a bit better in that respect, but I'm just sort of skeptical about whether calibration within groups would be sort of sufficient in terms of resolving that sort of range of problems. Right, right. And one thing, and this is another way in which I think my argument is more limited than maybe I sold it as is, I'm really just focusing on predictive algorithms. And I think, so one of the things another intervention point is, we could change the way we make decisions on the basis of the predictions. And I think with these other algorithms, where it's deciding what ads to show you or what translation of a bit of another language to show you in Google translate or how it, what oil painting sort of thing it gives you in response to your photo or I think there was something with a, some app had a hot filter and it tended to just lighten people's skin, right? Those are all, I mean, there's sort of, maybe there's a predictive element in there, but it's a lot of just making decisions. And I do think that, if we're talking about decision making, then I think that raises a bunch of other issues of fairness that really aren't addressed by my talk.   And I think we're gonna have to decide, what is fair action? Again, I'm kind of tempted towards something about, something where it's really emphasizing, sort of aggregate wellbeing of historically disadvantaged groups, but I fully admit that that's, a lot of people will think that's much too crude and maybe I would, I probably couldn't see that before long. But yeah, I think once you bring in decisions, you gotta, my argument really is just talking about the predictive side of things.


12s what up? Well, I know a lot of 12s They're probably still sleeping right now cuz they're on the West Coast But you two team keep it clean. What's going on? It's engraving here with another video and in this video the Seahawks Interested in Antonio Brown and then doubling down with being interested in Josh Gordon still I'm not mad at it before we get into this video I got a shout out my guide norb cam if you a Seahawks fan You probably already know who he is you probably already subscribed to his channel But if you're not make sure you do that. We just did a live stream last night talking about the number one and number two spots For the NFL top 100 of course that being Russell Wilson and Lamar Jackson Well, actually I should say Lamar Jackson Russell Wilson because he said wanted to anyway And we talked about a lot of other NFL things so y'all check that out again check him out real cool Do good people great quality too. Oh boy. His streams are in like 8k and it's fire. Anyway The Seahawks who came out last night Well, probably early evening for Seahawks fans since y'all a couple hours behind us on the on the East Coast But it came out that the Seahawks were interested in both receivers Antonio Brown and Josh Gordon. Let's actually read the article from ESPN comms Brady Henderson It reads Seattle Seahawks head coach Pete Carroll acknowledged that the team has interest in Antonio Brown saying general manager John Snyder has been monitoring the all-pro wide receivers Complex situation and it certainly is a complex situation And it's not even done being unraveled yet because it's still a little bit more peace to the puzzle But anyway Article continues Carol went a step further when asked about the team's interest in another suspended wide receiver Saying the Seahawks are very open to a reunion with Josh Gordon so with that That is a no surprise For both of them That the Seahawks are interested in each of them Because of course, we know we all saw the videos of Russell Wilson and Antonio Brown working out together Russell Wilson has a lobby for Antonio Brown. He's lobby for Josh Gordon He is lobbied just for the Seahawks to bring in more weapons And he does have some nice weapons at wide receiver now, but why not get more? Why not get more? Why not get two more receivers who they really got all the potential in the world? And if they can come in with a clear mindset a clear headspace, they'll be good to go I believe with Josh Gordon. I think he was dealing with a death in the family So that kind of messed him up That was one of the most recent things that he was dealing with and that's extremely unfortunate for Josh Gordon For Antonio Brown. He was dealing with a whole another set of things Some of them have been cleared up is just one last thing that he has left to get cleared up Because yeah, he was suspended or is suspended for the first eight games of the season but he has one more case that is like on September 14th or something like that It's like right after week one of the season so Hopefully for his sake that case can be taken care of and will there won't be any any more suspension added to it But we won't know till we know won't know till we get there So we'll see what happens with that But they on on the football field Both of them will make Seattle that much more dangerous that much stronger And they will position Seattle for a big playoff run because you would have a Josh Gordon You have Antonio Brown. You have a Tyler locker. You have a DK Metcalf And you have these guys you would have them going into the playoffs because you obviously not gonna have them for the entire season together Because one of them is out for the first eight games But I'm sure that will be a nice gift for Russell Wilson and he wouldn't mind that because I'm sure he would make do as he has make Made do with what he's had already and done pretty good So if anything does come to fruition with this I think it'll be a good thing. I think it could end up being a really good move. Well really good moves For the Seahawks for them to double dip at their receiver position And again, the biggest thing that we want for both of these gentlemen for both Antonio Brown and for Josh Gordon Is to be in a clear headspace for them to be in a good headspace That's the that's first and foremost before football. You got to make sure your life is together You got to make sure everything is good to go You got to make sure everything is right your sound you're at a end. You have peace So they got to take care that first and if that's taking care of a I'm sure Seattle will welcome them with open arms So like both Josh Gordon shouldn't be for too much longer They should be announcing his reinstatement any day now really And like Antonio Brown is for the first eight games of the season for now I'm out