Welcome, everyone, once again to the EuroPython conference. I'm Rashmi Nagpal. I'm a software engineer by profession and a researcher by passion. And today, I'll be sharing my knowledge and learning in building and deploying fair and unbiased machine learning systems. So let's begin. OK, so the agenda of the talk is, firstly, we'll understand what is machine learning and its related concepts to bring all of us on the same page. And then we'll understand the core black box problem, why machine learning sometimes gives biased and unfair decisions. Further, we'll explore the strategies to build fair, unbiased, and trustworthy models. On top of it, we'll also understand the various levels of the test tank. That's the machine learning test pyramid. And towards the end, I'll share some strategies how we can address the technical deaths in machine learning systems and bunch of resources for future reference. So without any further ado, let's begin. So let's understand the broader meaning of machine learning and its related domain. So AI is kind of a very broad term, which implies that any program or a technique which mimics the human behavior. While machine learning, it's a subset of AI. So it basically focuses on the development of the algorithms. And that learns from the data set and just corresponding features and gives you certain output or real-time data-driven decisions. While deep learning, it's a subset of machine learning which extracts the patterns from the data set and gives you some data-driven decisions. For example, the handwriting recognition, it's an example of the deep learning. And spam email detection, it's an example of machine learning. OK, so now explain the basics. Let me test the waters. I want you to take a minute and think. Think, which of these faces are real? Please raise the hand if you think face A is a real one. Interesting. Five percent within the audience. Please raise the hand if you think face B is a real one. I see. OK, now please raise the hand if you think face C is a real one. Very nice. 80% of the audience have raised the hands either for the faces A, B, and C. Well, the answer is none of these faces are real. These people do not exist on the planet Earth, to say the least. I'm not sure about the extraterrestrial planets. Maybe someday if I'll go to the Andromeda, I'll see that. So this is an application of the deep-generated modeling in which the algorithms are not just only learnings, patterns from the data set, but also going a step beyond and synthesizing the brand-new data instances based on those learned patterns. It's a very complex and it's also a very powerful idea for a generative AI, you know, it's chat GPD. It's also an application of the deep-generated modeling. But wait a minute. What's the building blocks behind those algorithms? So let's see where the neural, so that's how, you know, the neural networks comes into the picture. So this is a basic structure of the neural network, you know, given the data set as an input and then you feed it into the machine learning system which comprises the weights, sum, and biases and activation function, right? And then it gives you certain output. Output could be, you know, whatever the use case that you have trained or just building your machine learning model for. So let's understand this entire structure through an example. So here the use case is, oh, that's my fluffy, which is my Maltese beat pet dog.  You need to determine to which animal category that image belongs to. So you will feed the image data set as an input, right? And then in the system, we'll understand it as a combination of zeros and ones. And then it will give you a certain output with, you know, an accuracy or kind of a confidence. So here the image belongs to the dog category with 82% confidence. Now, what if we want to deploy this image recognition example for our end users? So how our machine learning pipeline will look like. So this infographic shows the end-to-end machine learning pipeline or machine learning system holistically, you can say, which basically comprises of the three main pillars or the three main stages. The first is the bird stage. The next is the deployment. And the last one is the monitoring stage. So what happens in the build stage is you're not just ingesting the input data set, but also doing pre-processing and post-processing model training and, you know, model testing. And further on, you are packaging and registering the model. While in the deployment stage, your application is released in the production or maybe like in the Dev environment. And then you are just making it available for the end users. So that's a very high level of the machine learning system or the stages in the pipeline. But what if something goes wrong, especially in the build stage? So let's see that. So let's understand the core problem of this entire talk and which talk, I'll just give you certain examples in the subsequent slides. So this example shows a biased word embedding. You can see in the Cartesian or you can see the vector space that the affinity of the homemakers is much more towards the women than a man, you know. So word embedding, basically what happens is it's kind of a vector representation of words or the text in the vector space. So meaning that the words which are closer in the meaning, they are going to have, you know, they are much more similar in the meanings. They are going to be very much closer in the vector space. So does that imply that the homemaker and the women, they are similar in meaning? Well, unfortunately, that's true. That's what the word to web algorithm at least shows. You know, representation which is displayed here shows that the word to web algorithm, it's trained on a biased Google news data set.  So if you have a biased data set, then, of course, your algorithm or the output is going to be biased. Okay, so the next example, it's a biased response given by the chat GPT. I'll just give you a couple of seconds so that you can go on through the prompt and the reply being given by the chat GPT. So the prompt says that, you know, the doctor yell at the nurse because he was late. And so logically, of course, the nurse was the one who was late, not the doctor, either way around. But the response given by the chat GPT is too hilarious, at least to me, it's... So he said that, you know, the response given by the chat GPT is kind of... He was the one and he refers back to the doctor, not to the nurse. Similarly, for the next example is also very kind of a bias in which the interchange of the pronounces happen. Unfortunately, we are the consumers of these technologies. Are we aware about the biases inherent in these systems? That's a question that I really want you to ponder upon. Similarly, there is a biased response given by the chat GPT, which alters the sentence in itself. You can take a couple of seconds to read through the response given by the chat GPT, in which it explicitly mentioned that, you know, the construction of the sentence, it's wrong. And alters the sentence by itself. And then it gives, of course, the output, you know, whatever the response it gives. Okay, on a similar lines, we have a biased credit scoring. You know, sometimes it's so much so that, you know, certain sections of the group, particularly the people of color, they don't get the credit or maybe just they can't get the loan sanction. And because that's a potential kind of a discrimination in the algorithms which are running behind it. So these are examples are the bias.  So, okay. Now, after pawning over those couple of examples and their bias and unfair outcome, now it's time for us to introspect. How did we get here? You know, how can the models output such discriminatory results or decisions?  First of all, you know, historically the algorithms which are trained on biased or discriminated data set, they could amplify or just perpetuate the bias so much so, which could lead to, you know, unfair decisions, especially towards underrepresented sections of the society. And the second is when you have the lack of diversity within the data set, therefore it will lead to skewed results, right?  So if you have a new distribution of the data set or the class labels within the data set, then it will amplify that you have biased results. Next is the cognitive bias, which actually it's, you know, when the individuals and the teams, they are not themselves aware of the biases when they are training, testing, or deploying any stage of the machine learning pipeline. And it's so much so big in our lives that it shows up when we are training or, you know, when the model has already been deployed. Lastly, when your evaluation metrics, you know, when it's not aligned with the objective that we are trying to achieve. So that will lead to some haywire results. Okay, so now this leads to me a question, you know, how can we actually build some fair and unbiased models? And what are the strategies that we can follow? So there are a bunch of strategies which I've listed here. The first is when you can collect some diverse and representative data set, because you want to ensure that the training data is well representative and it's also diverse, especially to the users whom you're catering your machine learning pipeline to. The next one is pre-processing. And in the post-processing stages, you know, you can use techniques like day documentation, feature selection and so on, just to balance out the data and reduce kind of a bias. Others are like algorithmic fairness and explainability. I'll of course give you the demo of all these strategies or just a couple of strategies to make you much more comprehensive to understand.  And explainability, you know, so black box models sometimes zero results are so much haywire that you don't even understand or just hard to debug, you know, why the model's giving such decisions. So therefore use some explainability algorithms and then testing into your machine learning pipeline would help in ensuring that you don't have any bottleneck within the systems. So, okay. So this is an application of the loan classification that, you know, you are given a data set. So here I'm using the German credit data set and it's like publicly available data set. And what we want to do is using the loan classification is given a data set, you need to identify whether people from different ethnic groups, whether they'll get it sanctioned or not, right?  So we are defining here the features and also the labels. And now we are using the smart technique. I'll explain what is a smart technique as well. So it's kind of a statistical measure because you want to balance class distribution and smart basically helps in synthesizing some minority samples which are not prevalent in your data set so that you have the balance of both the class labels. So you can see that, you know, before applying the smart technique, we have the counter to class zero distribution as 569 and one is 551. But afterwards when you applied it, you get like equal distribution. So using this kind of a strategy helps in, you know, equal distribution or just balancing out class distribution. And then you can just, you know, split the data set in just 80 to 20 ratio which is like training is for 80 person and testing is for 20 person, right? And then you just feed into your model. Okay, so now here I'm using this XGBoost model and also using some parameters. It's kind of a tree based classifier because what we are doing is doing just loan classification, right? And we are defining this kind of a build pipeline in which we are ensuring that we define our model. We also then do it just training and testing on our model. And here the parameters in the XGBoost classifiers are already being, you know, you have some learning rates, you have seed values or something like that. And then you define in the fit function. So once you have defined all the parameters, then you call the XGBoost classifier model and then you do the, you know, fitting on the training and the testing data. Now for the testing, here you use the predict method for the XGBoost model and then you evaluate the model using the accuracy as one such evaluation metric. You're free to use other evaluation metrics as well. For example, precision recall and F1 score, these are other evaluation metrics that you can use to see how our model is performing. But wait, now we have built a model. Let's find our weathered sphere enough versus not. Okay, so for that, we define our privilege and unprivileged group based on our hypothesis. So, you know, here I've defined the privilege and the unprivileged group by doing the EDA first. You know, so privilege group is something like all the men beyond the age of 18 and unprivileged is women beyond the age of 18 as unprivileged. While I'm using it because in the data set, if you see, then we have very less samples of the data with samples of the women within the data set. So we want to ensure that the model that we are building, it's not biased towards one gender versus another. So that's why we use this as more kind of, or you know, like all these techniques just to balance out and then also using kind of a metrics. So how we are testing the model, you know, we are just not, first we have trained the model, evaluated our model, and now we are using this multi-objective optimization algorithm because we want to optimize not just on the accuracy, but also fairness as a metric as well, because we don't want to compromise and either of these two. And then after running the optimization algorithm, we calculated the statistical parity, which is nothing but a fairness metric. And then it gets a statistical parity, basically it gives you the difference in probability of positive outcome between the privileged and the unprivileged group. The smaller the better, right? And after running that, you get a statistical probability of 0.0039, which implies that, you know, it's not creating any kind of a discrimination towards one gender versus another. So that's how we test and build our model in that case, just to ensure that it's not biased and also it's fair enough. So now it's time for us to understand the ML test pyramid concepts and see various levels of testing. So the first one is the unit test, which basically it's focusing on testing the individual components of your ML system in isolation. So that involves maybe you're just testing some very small functions or the modules that you have created, which are, of course, the building blocks of your ML system, right? And then we can use some integration testing just to see how it's interacting between the various components of the ML system and model testing, which is what I've given already the demo before. Then you can use some accuracy, precision, recall, all these measures to check the effectiveness of your ML system. And other ones are robustness and generalization. You can use whether the model is actually generalizing well to the unseen data set or the data which has not been trained upon. And then you can check whether it's performing reliably well in the real world scenarios, right? So in the deployment testing also, we can ensure that we have smooth CI CD pipeline and also validating the model performance in production. And the last one is ethical and fairness testing. So we want to see our ML system, which has already been deployed in production or maybe just in the verge of it, whether it's not compromising any of privacy concerns or just algorithm biases in itself. So that's why we just want to test for the ethical considerations because it's too much vital, right? We want to ensure that the models are fair and unbiased. So let's see some unit and model testing in action, okay? So here I've added some unit testers for your ML system. It's a sentiment classification problem statement. So I'll explain what's a sentiment classification. Given a text, you need to identify whether it's a positive versus a negative. For example, I'm very much thankful to the organizers of the EuroPython and so many loving people are here for giving me the support and share my learnings with all of you. So that's a positive sentiment, right? And I'm also very much thankful to my mentor to standing up and cheering me from the loud. So that's also like a positive sentiment. And the negative sentiment could be, I really don't like a pineapple on a pizza. I'm not sure how many people over here like it, but definitely not me. So that's a negative sentiment, right? So when you have defined your sentiment classification kind of a model, how you can add some test cases just defining if are they working really well with the actual output that's projected or if the data set which has been given in the model, if it's not English or maybe like some other Hebrew language, then you have to also. So you can write some kind of unit test for your system. Okay, so that's a very famous concept actually, which I'll also hope that you all like it. So that's an explainable AI. So let's, you know, how plug in the explainability just to make sure our black box models are explainable well, interpretable when I laugh. So, okay, first of all, we need to understand what's explainability. So explainability, you know, in machine learning implies that your machine learning, you know, you can explain what the model is doing right from the beginning, from the input to the output because it makes the model much more, you know, transparent and it kind of solves the black box problem. For example, medical diagnosis or maybe like in healthcare system, right? Or in just autonomous driving. The six are like super high, right? The decisions made by these ML systems, you can have like significant impact on the people's life. So to build the trust, ML systems need to be very much transparent, right? And they also need to be explainable so that the users can understand how the system works and why ML systems are making certain decisions. So the users also need to know who is responsible for the system and how they can be held responsible for its action. So let's see how we can plug in the explainability when we are building the ML system. So here, again, taking the similar problem statement of sentiment classification that given a sentence, you need to understand which positive or it's a negative, right? And positive here is one and negative is zero. So this is a textual data set. And of course we know that, you know, in machine learning system or just like, it will not understand the text. So you have to vectorize it so that it becomes like zero and one search, numeric in that sense. Then afterwards, once we have created the model, I just wanted to put in one test case to see how it works, you know. Here I've tested on sentence, which is so exciting presenting at the Euro Python conference in Prague. So of course it's a positive sentiment. And is that model also giving it positive? So let's see. And here clearly it had said that it's a positive sentiment and also highlighted the words which are contributing towards it. Which is saying, you know, okay, these are the words which have contributed and make it, you know, it's a positive sentiment. So that's how you can plug in the explainability while you're building your ML systems. Okay. So now that's interesting, you know, 13% So why we see that, you know, just 13% of machine learning models make it production. Have you ever thought about it? So, well, you know, there are a lot of reasons why this happens. So first of all, let's understand what are the technical debts which in occur when we are building or just what are the strategies that we can use to mitigate that? So the first is the data debt, you know. So the issue is like having low quality of the data. We say that, you know, as the good data, you have the better your model or the accuracy of the model will be. So if you have the low quality or just smaller data set and it has the biases or just lack of representativeness, then you are making imperfect or kind of limited. You're working on the limited data set and which makes the model much more harder to interpret or just harder to update. And especially it's not well represented and also creates some kind of a bias and of course inaccurate outcomes, right? Second is the architecture debt, which happens when you have defined your architecture in such a way it's so complex that it's so much disorganized over the period of time and so much poorly documented or not well understood within the, you know, within the team or someone that even, you know, even if you want to just refine it or just want to update it, you can't do it. So that's where the architecture debt comes into the picture. The next one is the algorithmic is when you are relying on some outdated or suboptimal modeling approaches and just creating certain assumptions. You know, if this thing works, that's how you just do it. So having those assumptions and not waiting it through properly also creates kind of a bigger bottleneck. So when you new methods you want to emerge, you know, the system is required updating and retraining, right? But that process becomes so much difficult if there is an architecture or data pipelines which are so much designed within outdated fashion. Okay, and the testing is, you know, when you're lacking so much test either from the very beginning, you know, processing of the data side, modeling the data side or just testing it out. So having the lack of the test also leads to some, you know, testing debts. So let's see how we can address and mitigate such technical debts. So these are the bunch of strategies that I've listed here. For example, first you can review your ML pipeline, you know, just go with the entire pipeline. Right from the beginning, you know, including the data collection, pre-processing or just modeling and deployment stage. And look for any manual steps if there are or just any hacks or shortcuts if they have taken because that will help in identifying any potential technical debt. And then you just try to automate, you know, address them by automating and streamlining the pipeline. And the next one is most important is, you know, the data is in your, as what we say, right? So if you have the poor quality of data, then of course it's a major bottleneck. So looking for the missing values or just biases or if there are any label errors and just try to address them by improving the data collection pipeline. And the third one is, you know, you need to examine your model performance. It could possible, you know, if it's been trained on some data set which has, which is like so much outdated now because the new models or the new techniques are emerging and your model is not fitting well. So you need to check in for the model performance, whether you need to update the algorithms behind it. And other is, you know, you need to set up the comprehensive automated testing practices, either unit testers, integration test or so on. And also check in for the regular code updates because you can refactor one of the best ways is what I feel like, you know, just to avoid any kind of issues which could possibly rise when the model has been deployed in production and just refactor some of the repetitive code base if it's already present there. And of course, using the explainability factor is also like really important because models are considered as black box opaque, right? They sometimes it becomes so much hard to diagnose them and just to get it updated. Therefore it's better if you use a lime or sharp or these techniques or frameworks just to understand how the model is performing. Okay, so now it's a conclusion on the closing remarks which I want to leave you for this entire talk is, the first of all, it's very important to have the comprehensive testing because it helps in unlocking any kind of potential that the machine learning system could have and also to ensure that we build, test and deploy fear and reliable models. And the second is having plugged in the availability or explainability makes our model transparent to understand why it's taking certain decision. And lastly, having enough test cases or just evaluation metrics, it helps in promoting fairness, accountability and transparency. Hence it's important to monitor, you know, how the model is performing over the period of time so that it, I mean, it does the performance as what it's expected. And these are a bunch of resources which I want to leave you for the future reference. And lastly, I want to leave you with the thought provoking player words which is what I mean and here it is.  So let's sit through the biases and push them out. In the realm of fairness, one size never fits. It's a kaleidoscope of perspectives and intricate pets. So thank you very much. Thank you for being a warm and lovely audience. And of course to the organizers for giving me this amazing opportunity and my mentor for being here, cheering me up and again, being my source of inspiration. And thank you so much, everyone. Thank you, Razmi. Great talk. We have time for questions. Please use the microphones. I will put mine too. Thank you for the presentation. So can you recommend some packages for fairness monitoring? Some packages for explaining models and for monitoring models? Right, right. So there is one famous package that's a fair learn model by the Microsoft that definitely you can use it out. And for explainability, Lime and Sharp are the two packages that I've already shown. Like I've explained one example of the Lime. So you can definitely use that when you're using any of the textual, you know, data set. And Sharp is also one such package that you can use for our framework that you can use for when you are building any kind of machine learning system. Yeah. Thanks, really interesting talk. And nice to see some solutions to this because you don't always get that. My question was about, I work as a software engineer, so maybe less from the data science side. And I think quite often I'm gonna have to start working with LLMs. And I wondered if you have any tips on how I can try and make them fairer. Because when you don't get to control the model, but you still have to work with it. Right, right, okay. So when you're saying that you want to start with the LLMs and also make them fairer is what the question is. Yeah, I guess from my perspective, I might not get to choose what the model is, but I'll still have to ship a product with it. So maybe it's impossible, but I'm wondering if I can do anything to try and help.  Right, right. Okay, it's short. So if you have the constraint over the model that you have to choose, you know, if you don't have the choice of choosing any kind of a model, but just given, okay, this is the model that you have to use. And then there are a lot of strategies that you can use. Just checking in on how the model is performing over various data sets, whether it's generalized well enough. And if it's actually performing really well on the real world scenario, because you are working in a simulator environment, right? You need to just test it out on the real world data, how it's performing on the real world data. And if it's not performing really well, then fairness techniques are of course there, or you can use some data augmentation techniques. And at the same time, you need to understand how the model has been trained. For example, the word-to-beg algorithm, the representation which I've shown in here, it's trained on the Google News data set, which is a biased, right? So if the model which is, or maybe if you're using any pre-trained model, which is trained on certain aspect of the data, right? If it's already biased, then definitely you can't remove the bias at all, right? And if you're creating a model from the scratch, then definitely you will take some design challenges or just design choices in consideration. But I would say if you are starting with the LLMs, just choose those LLMs which are performing really well on a diverse data set and flash to your use case that you are catering to. Yeah. Thanks, it's very helpful. That's no problem. Hello, thanks for your talk. I wanted to ask you something about the last thing you explained, the design bias somehow. Right. For example, imagine you wanna design a model that predicts the success in the schools. And one of your features is the postal code. So there are gonna be areas where, well, because they are poorer, then people are gonna have more like school failure usually. Right. So for example, the model that you would train on that would be biased, but somehow would be correct. Correct in terms of it's not gonna have a bad prediction. Right. It's not gonna be, not that it's not biased. This is like the result mathematically, it's not gonna be that bad. How would you like re-design this? I think since, okay, so the data set that you are saying, it's a school curriculum data set, what's the data set? Yes, yes, it's like a school success or who's gonna finish a school? Probably to finish a school, for example. It's a hypothetical case. Okay, right, right. I think that's an interesting question, I think, and point of one. But all I can think on the top of my head right now is whether it's catering to the, you know, the hypothesis. So first of all, before even going for the training, you clean the data set, right? You also do hypothesis testing. That's a part of the EDA. That's explanatory data analysis. You get the insights from the data set, right? And then you create some certain hypothesis whether it's catering to that particular use case versus not or just it's diverse well enough. Whether it's general, you know, if you can use on a different sub-population. And in that case, I think, if it's not satisfying the hypothesis testing or during the EDA process in itself, then definitely I will not take that model ahead. And even if we want to take it, you know, you need to identify various bottlenecks out there or just writing thorough test cases. Possibly it could be unit testers or, you know, the prediction when the model has been trained. These kind of accuracy, you know, precision recall. You need to identify exactly which hypothesis that you're trying to cater to, right? For example, in the credit scoring. So the ethnicity, you know, or the gender were one, these kind of hypothesis were, you know, the hypothesis was I didn't want to create a model which is creating a bias towards underrepresented sections of the society. So that was one of my hypothesis that I've thought provoking, you know, the model should be revolving around it. So for you in that case as well, if it's performing really well on certain hypothesis, then I think you can carry it forward. Otherwise, there are a lot of, maybe just doing some testing techniques. Possibly that answers your question. Okay, yeah, thank you. No problem. Any other questions? No? Thank you, that was me. If anyone has.


I look back and saw that I had no pants on the back of my legs and Literally the skin was hanging off my arms and my legs. I just I Just knew something horrific could happen Electricity is an essential part of our lives It lights our homes and powers our industries We rely on electricity every day Electricians and other workers who install and maintain electrical power systems and equipment make this all possible These workers are at risk for electrical injury on a daily basis In industries like construction and mining for example electrical incidents are a leading cause of fatal injuries on the job Shock is one of the more obvious risks in electrical work But a hazard that sometimes goes unrecognized is a danger from an arc flash An arc flash can occur while working on an energized circuit or from an electrical equipment failure When an arc flash occurs workers can suffer serious burns and other injuries Such injuries may require months of painful recovery Result in permanent disability or even be fatal You're about to hear the personal stories of three experienced electricians who were injured in two separate arc flash incidents They will recount the sudden and destructive power of an arc flash and will discuss how these events affected their lives my story begins in 1995 and the spring of 1995 in may and we were To show up at a building and perform a preventative maintenance on some switch gear And we arrived at the job site about seven o'clock at morning And we got together the two other gentlemen and myself got together and discussed what tasks we had to perform One of the tasks to be performed was the change of disconnect out in one of the panels so I was standing in the front of the one compartment and I said well I'll change out this disconnect and John was to my right and scott was to my left And John was going to proceed tightening the load wires of the disconnects in the panel that he was in And scott was going to do the ones that he was into the left And when I got to one of the switches which was still energized on the line side when I shot that disconnect door That's when the incident started It was so fast. It was unbelievable The the room was as bright as it could be right now horrific noises Humming banging just Metal flying everywhere I sort of remember that I can remember it. I can remember it being right in front of me I believe I got blown back And there was a wall not too far away So there wasn't too much room that I could go back maybe three feet. I hit a wall And I went straight down Luckily one of the workers was Walking out the door for something You know, I'm not even sure what he was doing getting another extension or whatever He was lucky enough just to scamper out of the room When this happened so he didn't get burned at all My working partner, however, he did eventually get burned but The the incident happened right in front of me And I went down he was not in front of the the actual arc But he did turn around and to come and lift me up And as he was lifting me up and this Here is still Parking and melting down This is when he received burns on on his backside and the back of his legs and the back of his arms As he was trying to pick me up I remember I was bent and dying to pick up the disconnect and I could see his motion of him closing the door And when he closed the door, it was just a horrific Explosion, I mean the ball of fire and the noise the more the noise it was so deafening that It was just like like somebody was in there with shotguns just shooting at you As it turns out as a result Of the incident I received burns Over 58 percent of my body The worst burns were from my fingertips Up to my elbows While I was Under in the hospital they had I did skin grafts To my arms through my fingers Luckily I had enough skin left Although it was close from what I heard to to do my arms. They took skin from my midsection They would have liked to Did skin grafts to my legs But I didn't have enough skin and it was borderline and they they thought maybe there were a couple areas That probably could have used them on my legs Pretty much as I laid there in the hospital and and Finally started realizing what happened. I mean there wasn't too much of my body that I I that was not burned Some places were better than others But I can remember being at the time I had Garments on on my arms sort of this is like the maybe the first time that I woke up and was conscious And and just couldn't believe that the total coverage of the The injuries My legs Both legs Both arms My back there's a there's a patch on my back that got burnt pretty good My face and my ears my ears were pretty bad Um, I like to tell everyone they look like if you leave a marshmallow in a campfire too long That's sort of what my my ears look like They would actually When I would lay in the bed in the hospital and if my ears would touch the pillow They would they would almost stick to the pillow I can remember not being able to move my head because both of my ears were stuck to the the pillow I'm here to tell you that This accident is one that can't happen to you. I'm not here to scare you.  I'm gonna retire as an electrician My son has started the program I constantly talk to him about what's going on What can happen I can't talk to every electrician out there what can happen But maybe this message here will get to a lot of you I would say the The biggest way it's changed It's it's in my attitude. It's It's trying to expect the unexpected It's it's being ready for anything because anything may happen With what we do we're we're more at risk Of something like that happening Then maybe anybody else that would be walking down the street or maybe you you you know, whatever you do in your profession We may be more Suspect to those freak freakish kind of accidents that happen It's kind of funny My when I was in the hospital recovering when I was well enough to speak and see people Every electrician that I talked to Had their own little Arc story that they've lived through a lot of them were harmless You know injury free However, they were arcing incidents and well could have been You know Cause of an injury It's amazing. How many Very high percentage You'd probably be hard pressed to talk to an electrician that Never was exposed to an arc that he had caused Or was around We as electricians should know this And and be ready for It's going to happen What are you going to be like when it happens? What are you going to have on your body when it happens? What are you going to have over your eyes when it happens? Uh We may not stop them from happening, but we can stop them from hurting you June 19 97 1997 Was Requested to go To brownsville elementary school met my partner At a bus stop a park and ride And we went in one vehicle went in a company vehicle To the job site took us about an hour to get there. It was a pleasant morning We got to the job site. What we're going to do is Check to see if the hardware Would let us adapt new buckets to old switch gear This switch gear was in a revamped boiler room clean atmosphere But uh the boilers had been removed and Upon inspection when we removed the side panels on the switch gear The uh I could see that there was a old film across the buses And uh pretty good indication that All the paint on the outside didn't help what was inside But this switch gear uh there was quite a few switches and it was defective and the the intent here was to order new buckets to be installed Beans the manufacturer the original manufacturer Was no longer in business. This was a retrofit And it was just a matter of Seeing if the connecting hardware Was going to line up I decided that I was going to see if I Could safely disengage a bucket and uh See if we could get the hardware to match up my partner went out to the Vehicle to get something and in the meantime I was uh I disconnected I was I was able to reach in and work around this switch rather comfortably I disconnected the switch electrically and mechanically And I was attempting to remove it from the switch gear When uh the accident occurred the uh it had arched over and it uh There was a flash Brightest light I ever saw in my life noise was deafening And uh I probably blacked out for I have no idea how long seconds It couldn't have couldn't have been it couldn't have been more than a minute but uh Regaining my composure I looked at myself and I saw that uh I had Been burned pretty severely I had skin hanging from My ears and from my arms My chest I was able to walk out of the building met my partner who was coming in And uh he he was just aghast he just didn't uh Couldn't believe it because this is just A matter of seconds basically from Was hardly any time from the last time I saw him till I I Saw him again and he just looking at me like He was uh probably more upset than I was because I was in shock Uh decided that He should drive me over to the hospital rather than waiting for emergency help because the hospital was so close We did that went to brownsville hospital and uh The decision was made to Send me down to West Penn hospital burn unit which interestingly enough I was instrumental in wiring Two things that uh I never really got to enjoy was the the helicopter ride because I was out and The burn unit Never really did see the inside of it because I was I was uh More or less in a coma the whole the whole time and that Coma was induced that I was out for five weeks The skin on my face had basically burned off Uh lost parts of my ear ears There was actually metal Impaled in my teeth So yeah like spears my arms Were burnt from Where my sleeves I had a short sleeve shirt on so we're Under the short sleeves my arms were burnt My neck was burnt I had A hard hat on had safety glasses on And essentially that was About as far as it went Pires should have been shut off plain and simple I made a Very costly error by not Insisting that the power was turned off and uh As a result I came very close to dying I had uh Acute respiratory distress from the smoke that I had inhaled And uh that was probably the the biggest thing that they had to work with or on I'd have to say that My family Didn't deserve That mistake that I made Again, I'll just reiterate if you could actually see this Light and explosion that takes place on the devastation after the fact you could see that If you could witness that you'd never work on anything that was hot Part of what happened to me was because of my own ego because I've done it so many times That like I said you you get the feeling that you're infallible Find out that uh you just skin and bones I've I was never complacent. I mean I Always very careful always very careful, but My fault that was my fault because in retrospect I made that decision to work on it hot Your life has to be just as important as everyone else that Someone's asking you to work on that hot Okay to what inconvenience could that be to Being in hospital for nine months What's the what Was it worth it when it comes to working on live circuits live equipment You really do have a choice you don't You don't have to Work on Energized equipment It can be shut off You're just too important Think of your family Thank you your family. It's important to recognize that much That can be done to prevent arc flash events are beyond the control of the individual who may be at risk of injury Uh Today we have the the tools and technology to to analyze the workplace to identify arc flash hazards We can use that that those tools and knowledge to better engineer facilities from the very beginning To a better engineer facilities if we're retrofitting or renovating a facility so that we reduce arc flash hazards Either in magnitude or in frequency Uh It's important that the commitment to manage this hazard be visible at the very top of an organization management must demonstrate the commitment to um Provide a safe work environment and apply that commitment to the unique hazards of electric arc flash uh engineers that do the design uh of the of the facility of the processes of of work practices Uh can bring added value to Preventing these these events from happening Uh partnerships with the equipment suppliers that can bring innovative technology to uh to both from uh Say an industrial switch gear or a commercial switch gear as well as in the tools and protective clothing That uh people use on a daily daily basis the safety professionals Uh have a really a very important role in coaching the organization management as well as workers in and uh Keeping the workplace safe And I think the more that the safety officials understand about arc flash they'll be able to apply their expertise in uh hazard control measures again to the unique hazard of electric arc flash Uh trainers and educators whether they're in-house or or people that you may contract with to provide those services to you We need to make sure that they're knowledgeable and up to speed with uh current State of the art knowledge on arc flash hazards As well as electric shocks so that uh, we're training and teaching people You know the best we know how I think lastly the the worker the person at risk Again having have the training and education provided having had the Tools and protective clothing provided Need to really make a commitment to to use that knowledge to use those tools to use that personal protective equipment every day Uh to watch out for their co-workers make sure they're worth doing that too Your safety depends upon all electrical work being carefully planned and carried out The safest way to work on an electrical circuit is to de-energize Lock out and then verify that the circuit is de-energized It's important to follow recommended safe work practices and use proper tools and equipment in addition to appropriate PPE The decision to work safely is yours The choices you make can save your life


Item Number – SCP-4559 Object Class – Thaumiel Special Containment Procedures – All information regarding SCP-4559 within scientific institutions and other organizations are to be removed under the guise of being incorrect information. Description – SCP-4559 is a probabilistic anomaly associated with the choice on whether a person wants a receipt or not within most grocery stores. In the case where there are either rules that state the customer always receives the receipt, or in the event that the customer is given the receipt without their prior approval, SCP-4559 will not trigger. If none of those events occur, in the event that the customer is asked if they wish to receive a receipt, the choice on whether or not the subject accepts or denies the receipt cannot be predicted. The subject's mental state, gender, or wealth will not influence the outcome, and the chance for each choice is exactly the same. Currently, SCP-4559 is the only truly unpredictable or random phenomenon in the universe known to the Foundation. For example, quantum mechanics, weather, ocean currents, and the algorithms for standard random number generators can be meaningfully predicted, but SCP-4559 cannot be predicted in any meaningful way. Addendum – By order of the O5 Council, SCP-4559 has been reclassified as Thaumiel for its use in the containment of numerous anomalies that require a random number generator. The Hensworth Alice number generator uses security camera footage from nearly 20,000 grocery stores within the US, which analyzes footage from the checkout aisle and uses the information generated from SCP-4559 to create a random integer. Currently, the Hensworth Alice number generator is being used in the containment of 39 safe class anomalies, 21 Euclid class anomalies, 8 Cater class anomalies, and 2 Thaumiel class anomalies.


One of the biggest problems of water supply and sanitation services is that a large portion of the population in the global south do not have access to safely managed services. Approximately 31% of the population in Sub-Saharan Africa, for example, have access to safely managed services which means that 69% of the population still needs to be supplied with safely managed water supply and sanitation services by 2030. The overwhelming majority of the 69% of the population resides in low-income areas. What we are doing here in Kigali is we have organised a course together with UN Habitat and together with FEI Dutch Water Operators and we are bringing together the coordinators from water providers who are targeting service provisioning in low-income areas. Often within these water utilities, these water and sanitation utilities, servicing low-income areas is not a priority and that means that a lot of these coordinators have a very difficult task within their water utility. What we do in this course is we try to stimulate exchange, knowledge exchange between the participants, exchange of experiences all with the idea that they learn from each other and also that they develop a network that if in the future they have questions or ideas that they can contact each other and support each other. This is the second course that we are doing. We did a first course also with coordinators of water service provisioning to low-income areas in Nairobi in 2018 and what we see is an enormous improvement in the motivation but also the capacities of these coordinators to develop new ways of servicing low-income areas. There seems to be a lot of momentum, a lot of new ideas and you see a lot of interaction between the different water utilities. What we are looking at basically is for them to first of all do research, find out how many Q6 should be there in a specific area, what kind of management model they should be able to utilise. So they need one for a lot of monitoring, a lot of research and maybe working with the community especially to protect the infrastructure. We are doing a lot of plantations in the country that keeps the impact of environment lower so that they will keep their dams safe and serve for a longer time and their boroughs become productive because the land is very rich and the weather is very good to have a plantation and to have trees everywhere so that we can cope up with the current situation. Strengthening the capacity of these coordinators that coordinate water service provisioning to low-income areas is important because ultimately if we are to achieve target 6.1 or progress in achieving target 6.1 these coordinators are going to be the main people that are going to make that happen. Without them there is no way we even come close to achieving target 6.1.