All right, hello everybody. Welcome to our bias in algorithms 2.0 workshop today. Thank you for coming. And we've just got some information if you want to put any kind of comment or unmute yourself during this presentation we're just going to, you're going to appear in the recording so just a little disclaimer there. And we're just going to go ahead and introduce ourselves. So my name is Leah Valletta. I use she her hers pronouns and I am the GTA in the teaching and learning programs department in the Hodges library. And hi everyone, my name is Grace Darrell. I also use she her pronouns, and I am an online learning librarian in Hodges. All right, so what we're going to do today we're going to have 30 minute workshop that is going to be recorded and then we're going to have 30 minutes of unrecorded q amp a afterwards. And we are going to review just a little bit about what algorithms are and how we see them in our lives we're going to hear from some of you about how you might see them in your lives because I'm sure you do. Then we're going to go on to talk about how algorithms affect what information we see in our lives. Kind of some review from last time but a lot of new stuff in there as well. And then last we're going to kind of just generally explore how algorithms might affect the information that you see even here at the library in your academic research. All right, so first we're just going to talk about what they are and where you might see them. What an algorithm is so again we talked about this a little bit in our workshop last semester about algorithms if you want a little bit more on this introduction just send you to that first workshop. But generally an algorithm is a rule based process for solving problems. And an important point there is just that they sort of change depending on how you interact with them. They're very dynamic. They're always moving. All right, so we're going to just go ahead and hear from you all if you want to go to this padlet link. We're sure that a lot of you have are familiar with algorithms in some way we see them so often in our digital lives. If you go to social media you might even be more attuned to this. But since we figured it's likely that you've encountered them we are just curious to hear about where you see algorithms in your life online. Anything like that. And any kind of just ideas that you have about them. So where you see them and anything else you want to say about where you see them. Let's see. Suggested ads that's always a good one. The macro. Socks we're seeing some socks with pets faces on them definitely shopping websites, Google scholar results. Good one. Those ads. Those ads are the ads are real. Those ads are probably where a lot of us see algorithms and how, or I feel like ads are one of the ways that we notice really algorithms because like they're so responsive to all of the things that we search for and stuff. So yeah, like socks with your with pets faces on them if you've got a cat or a hamster or something. And the people you may know section that one can be weird sometimes to you really accurate algorithm every now and then. For sure. Media how things are organized definitely autofill responses. That's also a really good one. Yeah, I didn't even think about autofill, but I guess we kind of we talked about that in our first algorithm workshop Leah and I did when we looked a little bit at like Google and we looked at our individual autofill responses and how different they were. We even looked at like work computer versus home computers like dramatically different stuff so awesome. Well thank you guys for sharing this definitely gives us a good start. And this is some of the stuff that Grace and I were talking about when we were planning this workshop. So yeah, we definitely feel you on a lot of those ones they are everywhere. All right. So now that we've kind of talked about where we see them how they come up in our everyday experiences we're just going to kind of talk about the context of algorithms. So in the last session we talked about how algorithms are not neutral and they reflect a very specific priority of an organization and sometimes they can reflect the dominant cultural narratives that we experience, which can exclude people who don't really fit neatly into those narratives. But we wanted to be clear that while algorithms are not neutral they are also not and again we touched on this a little bit but they are also not inherently good or inherently bad. So if you want to go to the next slide. So here to get the most from our information and from our media, you kind of just need to look at the context of the algorithm which isn't always easy. But we can't really avoid algorithms honestly at this point, nor should we because sometimes they can be really useful so we just sort of wanted to take the time to draw attention to the fact that a lot of times, rhythmically literate, if you will, is more about just understanding how you're interacting with the algorithm and how the algorithm is interacting with you and just sort of understanding that context so you can still use your social media or whatever you don't have to feel like oh it's you know generating all of these algorithms like I don't want anything to do with it that's not really what we're saying. So I'm saying that by examining the context surrounding the service that you're using, you can gain some clarity about what you're actually getting and why and that can be really helpful to sort of making sense of all of this algorithmically generated information. So, you know, maybe an algorithm is prioritizing a certain type of information but maybe that's exactly the information that you needed or maybe getting ads is annoying but maybe it also really helps you find things that you need so they're not all good or all bad. But they do have specific contexts so when dealing with algorithms. It's really about just understanding that they're there thinking about what they're doing there and who they're serving and then kind of being able to use them in this context. So we have come up with some questions and this is not at all an exhaustive list so if you even think of any while we're going through these feel free to throw them in the chat. But we just made this list of questions that we thought would be useful to consider that will kind of help you make sense of the overarching structure of the algorithm and make it a little bit easier for you to appreciate the content that you're looking at. Because sometimes it's a little easier to see what you're supposed to be getting out of a service and what the service maybe is getting out of you using the service because we especially probably all see this with things that are free. So if you're not paying for a service, you're probably paying with something either your data or your attention or your time or something like that so that's kind of what we're trying to get going thinking about today. So some questions that we have are just what organization is hosting this service very simply so. Just, you know, what are they are they a social media outlet. Is it Google is it something else is it a job search engine is it a clothing company, and just kind of thinking about like what is this organization and what are they trying to do. How is it funded. How can you tell it's not always that easy to tell how something is funded but if you can't tell why or you know what what other stakeholders are involved in this. And what benefits and drawbacks are they are there to using the algorithm or the service, you might know that this algorithm is taking a certain amount of your data. And then you might have to sort of balance that with what, like how much you need the service what is the service providing you, is it working with you. Again this is a really vague overarching question it's not one that's meant to have an answer it's just kind of something to consider while you're interacting with these algorithm. And then, finally, is the algorithm, like what type of information is the algorithm prioritized, is it prioritizing the best information in terms of quality. Is it prioritizing sponsored information, or is it prioritizing something else entirely is it using your data for this information is it just simply giving you the results that paid to be at the top at the top. What exactly is the structure of the prioritization of the system that you're using that is using an algorithm. I just wanted to add here that this isn't really easy like you're not going to have a checklist and go through and satisfactorily check them all off and be like great I can use this algorithm. Sometimes it's really hard to figure out funding information it might take you like way too much time to even gather all of this information before using a service but just understanding that there's a value and just considering it, and just being aware that there might be things going on that you can't see and this might be impacting the information that you are ultimately giving to interact with. And on that note, I will pass it off to Grace to kind of talk about how this fits into your academic research. Thanks. So, you know, we kind of reviewed what algorithms are and we've talked a little bit about how they show up, put them into context and, and ask some of these questions but I'm sure you're wondering if you're watching this video right. If you're here in this workshop, you're probably like, but what about the research, right, I have to do research. I have to have, you know, sources for my assignment, like how does this do me any good when I'm doing research. And that's a totally valid question. So, now that we've kind of talked about these things we're going to put it in more of a research context. So, when you're doing research on a topic for an assignment. And you search for information, right whether you're looking for a scholarly source or a peer reviewed source whether you're looking for a primary resource, whether you're looking for a news article. If you're just learning to gain some background background information whatever you're looking for. So, when you think about algorithms and understand how they're affecting the results that you're seeing. So to talk about this we're going to look at a sample research topic, and we're going to explore the different results that we see first when we search in these different places. Hopefully this will work. If it doesn't then we've got screenshots so no worries but we're going to try to see them in real time so we can scroll and interact a little bit more. These are the platforms that we're going to look at use algorithms, every single one. And they use these algorithms to determine what results we see and what results we see first. Basically what the algorithm is programmed to think is most relevant. So, again, let's see what this looks like. So we're going to pretend that we're doing research on black lives matter and the black lives matter movement. We're going to look at four different places that you might go to look for information. And again we're just going to kind of see what search results pull up first and think more about how the algorithm affects what search results you're seeing when you research. So, first, we have Google, right regular old Google. So I guess my question for you here is what do you notice about the kinds of results that Google gives us first go ahead and type an answer into the chat. But I will scroll a little bit but looking at these results that Google gives us. What are you noticing about these results. We have an answer mostly news stories yeah we have a lot of news here. So right here in this block, right we've got news stories. If we scroll down, we've got the New York Times that's news. The Guardian BBC. We also have somebody said who said social media websites.  There's a lot of profiles over here in this box. Somebody's also said the related searches are interesting. Yeah so down here at the bottom. The mission statement timeline protest riots news essay foundation picture. So yeah thinking about those related searches as well so you have lots of different things that we're seeing here. We are getting a lot of general information right so the very first thing it points us to is this homepage. And then also the Wikipedia as you can see I've clicked on the Wikipedia link before, because it's, it's a different color. Oh even look it says I visited this page on March 8 2021. So, yeah, it shows us. Right it shows us more of this general information that's what it's prioritizing. Another thing is that it's potentially privileging certain information based on the search engine optimization which is a fancy computer science algorithm word, or phrase the SEO, maybe you've heard the term SEO. But that's something within the algorithm right that it's prioritizing so like people can pay more money and get their search results bumped up near the top. It also could be very likely is privileging or potentially add revenue resources right so if you click on social media. If you go to a news website if you go to just a regular website right chances are you're probably going to see ads and Google as an advertising company. So it's also privileging the information and the sources that are going to generate that ad revenue which is going to make Google money. So again, like Leah said earlier, this is not an inherently good or bad thing. It's just something to recognize and think about so when you're going to Google those these might be the kinds of sources the kind of information that's going to be popped up to the top. So if you're looking for news if you're looking for more general information, if you want to find social media sources. Google is probably a really good place to start. And again just thinking about what kinds of things you're going to find there. Next, we are going to turn to the library. So this is one search if you've used the library's website before the giant cursor is back the giant cursor left earlier. If you've used the library website before to search for things. If you've used one search this will look familiar to you. I'm going to do the same question here so like, if when I'm searching black lives matter, what do you notice about about the kind of results that one search gives us first. What do we see here. I'll scroll a little bit. So we have an answer lots of books and ebooks. Yeah. So this is something that's more content related so conflict versus all lives matter protests etc some history. Yeah. Definitely. Yeah the content is interesting for sure it's not as general right it's a little bit more specific here. We also had a response that said lots of books and ebooks right so one search is a library resource right we pay for this. What it does is it searches with the library owns with the library pays for. So if you notice when we say lots of books and ebooks that is like directly from the library's collection. And so one search is prioritizing those library resources right there are some streaming videos in here. If we loaded. See don't want to ruin this. We loaded more results. I'm always worried about doing these things live. If we load more results right here's an article here. Here's another article another article. So like the further down we get it starts pulling in some articles, but it's going to prioritize those things that the library owns the library collection. So the ebooks and the books the things that you can check out from Hodges library are the things that we directly pay for as part of our collection. So yeah it's interesting to see the content the content change a little bit but also the type of information that you're seeing right even with something like one search. An algorithm is determining what's the most relevant and for a lot of sources, especially if you start with a more general topic, but even if you're looking at something more specific. So basically one search is probably going to try to bump up the most relevant library owned resources and the library collection probably going to bump those up first and prioritize that information. So again just something to think about as you're doing your research just asking these questions and noticing what kinds of things you see. So we've got two more left to look at Google we looked at one search now we're going to go into a couple of databases. You can really use the library database for your research if you haven't it's coming. The first one we're going to look at is pretty general. And this one is called academic search complete. It's a very general database that searches a lot of disciplines at the same time so it's not like an English database or a political science database or a chemistry database or anything like that it searches all different, all different disciplines. So, again, same question when you're looking at this. What, what results are we seeing first what is the algorithm bumping up to the top what do you notice here about the search results. As I scroll a little bit. Anything here different or noticeable about these ones. Alright, so we have someone who said some international perspectives. So maybe we're starting to get outside a little bit maybe we're broadening up events related to the black lives matter movement. Yeah, oh, this just keeps going. So one of the interesting things. You know if you looked at Google right like it shows you one page and you have to go another page, one search it stops after a few. This one just keeps going. So let's see keywords aren't in as many titles. We're getting images. Yeah, so, Right, so we have video results here we have some images. I want to see again hopefully this works. If I add quotation marks I didn't do this earlier sometimes it's not necessary other times it's helpful to add quotation marks. Oh, I did it I ruined it. That's why you don't touch things. Okay, so we're just the back button worked sourcing some similar resources. Okay. So yeah, again just seeing you know if we put things in quotation marks it might it might change a little bit but yeah right so if you can see here in some of these like this one right here so we have subject black lives matter movement. Subject, subject, subject, subject, right. So an academic search complete, you know as I'm looking at this one of the things that I noticed is that when you search something in academic search complete, it prioritizes what's in this subject field. So this one has black lives matter in the subject field and it has black lives matter in the title, but it's not necessarily prioritizing something based, obviously on content or obviously on the title. It's really prioritizing what's in that subject field right as opposed to one search. Or if you remember back here we have lots of different things that have black lives matter in the title and academic search complete doesn't necessarily pull things with black lives matter in the title. You can change that here where you type in your search terms. If you go to field there's an option right here to do title so if you really care about your search term or your topic being in the title, you can click that. And you can kind of see what that does I'm not going to do it because clearly the quotation marks it didn't like, and this probably would mess it up to and wouldn't show anything. So again just just a different way that this algorithm works right it's going to give you different kinds of information. It's going to be different. And again what it's prioritizing and what the information that it's privileging and the information that it pushes up to the top is going to be different than the other things that you use. And so finally we're going to look at one last database, which is a more subject specific database if you're doing subject specific research. This database is called the PIS index. Oh no, continue working. This is kind of a public policy database so it deals with a lot of policy law, things like that is more of a political science database so again, as I've been asking the same question. What do you notice about the results that this is giving us first anything that we notice here, lots of scholarly journals, no images so far. Yeah. So we're getting images or videos like we did on academic search complete that's for sure. I think something that I also notice. Here we go. Another answer some of these don't actually relate to the BLM movement. Sure. Yeah, something that Leah and I noticed when we were planning this workshop and we were talking about these things and looking at these is that like PIS tends to focus on that law and policy and so like a lot of these seem more negatively skewed right. Which is really, really interesting. I'm talking about violence police violence we have protests, fatal interactions and crime. Again, something that like maybe is is relating the movement to criminal activity, something to be aware of if you're searching for, for this kind of topic in this database. Yeah, and I'm sure to so we have don't relate. I'm sure to, if we actually put in the quotation marks, which again, I didn't do encounter a problem right. So this happens sometimes it'll time you out of searches, not a big deal session ended excellent. So yeah, we can go back to the slides at this point and there is a, there is a screenshot here. And I think I actually did quotation marks on this one so maybe you can see a little bit better that if you use quotation marks. You might get you'll probably you'll get more relevant results but I just forgot to do that today. But yeah, um, it's really interesting to see the kinds of again the kinds of sources that you're going to get because of the algorithm right because the database is only is looking at a different subset of materials. And the way that the algorithm works, it might decide to privilege certain information over others. So, that's just kind of a larger thing for you to consider. Here are just some some general things to keep in mind as you move forward and as you're doing your research. Just a reminder that again, all searching platforms, including our library resources use algorithms. Right. And again, an algorithm is not inherently good or bad. We're not saying that they're neutral. They're not neutral by people, they are not neutral, but algorithms are useful, and we use them every day. And even the library resources that you are going to look, you're going to be using to look for research like one search databases, all of these use algorithms. And it's just helpful to understand how the algorithms impact their results that you see. So when you're doing research understanding what information is going to be privileged. You might pop up first what the database is assuming is most relevant for you, and knowing that you might have to do a little bit of digging or you might have to look in several different places to get the sources that are best for your research. Right. The best and the best source of information for your research is not always necessarily going to be the first one, or in the first place that you search. So searching a lot of different platforms to get lots of different kinds of information is probably going to be a good move. And then just paying attention to where you search and what results you get right the whole point of this is hopefully not to bog us down a lot of different things but just to to increase our awareness. And just ask us to start paying attention right just pay attention ask questions. Be thoughtful when you're searching and when you're doing research and that can be really helpful. And maybe we'll, we'll spoke some questions that we would love to chat with you about because this stuff is really interesting to us. So we have just the reference for the definition we we talked about at the very beginning of the workshop, and then all the screenshots again are from our own feeds and accounts our own searches. So thank you so much for being with us today. We have questions about how algorithms show up in research if you want to talk more about algorithms. If you need help finding sources. Any of that you can always chat with us on the library website, or you can schedule a consultation via email or through our consultation form if you would like someone on one time with a librarian. We can help you kind of navigate those databases navigate library resources and help you with your research so do feel free to reach out to us. And so that's it again thank you so much for being here today we really appreciate your time and attention. Next week, we are starting our am a sessions so we have to am a sessions. These are not going to be recorded they're just dropping sessions where you can come and ask librarians ask a couple of librarians any questions you have about research so feel free to register for that if you're interested. And again, thank you so much for being here, and now we are going to stop the recording, and we will enter into Q&A time.


Mr. General Secretary, Mr. Deputy Secretary, generally you are Excellencies Director of the Bureau, Member of the Secretariat, Ladies and Gentlemen, Dear Colleagues. I'm honored to represent Romanian Government in this important forum and to make my address to such a distinguished audience. My name is Julian Popescu, and I'm Secretary of State of the Ministry of Research, Innovation and Digitalization of Romania. I'm proud to say that over the last 20 years Romania developed one of the best mobile and fixed data infrastructure. Romania IT and C sector has experienced continuous growing over the last decade with high performance in connectivity, cybersecurity, digital development, and with many companies specialized in software development. Therefore, the theme of the forum WTFE 21 is one of the greatest interests to us. Rising the issue of policies to mobilize new and emergency telecoms and ICTs for sustainable development. The last two years have been extremely relevant in this regard with the COVID-19 pandemic sweeping to the planet, highlighting the importance of digitalization and the need to global inclusion. And I think it had made us realize that without digitalization inclusion we cannot function in a modern world. The five draft opinions will form the basic of WTFE 21 discussions on the same topic that we consider very important. If not vital for the future of the digital field, developing and deploying emerging technology for sustainable development, affordable and secure connectivity, digital literacy skills, and use of ITC for future pandemic and epidemic response. The WTFE does give us a unique opportunity to came together to pass, reflect and to debate the emerging issues in telecom and ICT and their significance for us all. Most important, it allows us to look at how we can connect and unconnect, which is the heart of the ITU does. The timing of the year, the timing of these years, WTFE is particularly appropriate as we stand as a tipping point between a difficult situation which made us adapt and the things we need to do for the future. As the host country of PP22, we will strive to make the event a successful story. And as a representative of the Romanian government, I assure you that I will do my almost to encourage constructive discussions, teamwork, consent and a common approach. We are committed to making this conference a forum for collaboration on all the key issues that will shape the future architecture of ITU. Distinguished delegates, thank you for the approach to address such a special audience today. Thank you very much.


In this video we will look at the chemistry behind the production of iron from iron ore. This is the reactivity series of metals which was discussed in this video. In early earth history iron like all other metals would have been found as an element in the crust of the planet. Later when plants evolved that released oxygen iron combined with this new reactive gas making oxides. This process would have taken many millions of years. Iron is found as the compound iron oxide in the ore known as hematite. To displace iron from its oxide we need a material that is more reactive than iron inexpensive and easily obtainable carbon ticks all of those boxes. Until the industrial revolution in the 18th century the source of carbon had been charcoal which contributed to deforestation in many parts of Europe. The discovery that coke almost pure carbon could be made from coal was a major breakthrough. Carbon acts both as a reducing agent it can remove oxygen from a compound and as a fuel to supply the high temperatures a furnace needs over 1500 degrees centigrade to enable the reaction to proceed. The actual reaction is a three step process. The carbon burns to produce carbon dioxide. The carbon dioxide reacts with more of the carbon to make carbon monoxide. The carbon monoxide then reduces the iron oxide to iron. The reduction reaction can be summarized as this. This reaction can be classified in several ways. This is a displacement reaction as carbon is more reactive than iron and so can displace iron from iron oxide. The reaction is also an example of a reduction reaction where oxygen is removed from a compound. At the same time carbon is being oxidized to make carbon dioxide iron oxide has been reduced to iron. As reductions and oxidation occur simultaneously these are called redox reactions. The transfer of electrons which is what chemistry is all about can be tracked during this redox reaction which gives another more useful definition of what redox means. We look at how the oxidation states of iron and carbon change in the reaction. Iron begins in the positive three state and ends up in the zero state. Remember all elements have a zero oxidation state. To achieve this the iron three positive ion must gain three electrons. Reduction is gain of electrons. Carbon begins in the zero oxidation state and ends up in the positive four state. Carbon must have lost four electrons to achieve this change. Oxidation is loss of electrons. All of the electrons lost by the carbon atoms are given to the iron three positive ions so we have reduction and oxidation a redox reaction. To recall which way electrons move in a redox reaction think of oil rig. Oxidation is loss of electrons. Reduction is gain of electrons. Only metals below carbon in the reactivity series can be obtained by reduction of their oxide using carbon. Copper is extracted under less harsh conditions when compared to iron as it's a less reactive metal. This in part explains why copper extraction was discovered well before that of iron. If you liked the video give it a thumbs up and don't forget to subscribe comment below if you have any questions why not check out our Fusco app as well until next time


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