Speaker 1: So why is it fairness part of our process here?
Speaker 1: It's because, well, as data scientists and statisticians and researchers,
Speaker 1: we had good intentions. We lack those mechanisms for action.
Speaker 1: We lack things in our process that force us to
Speaker 1: consider hard questions. We need to use our brains a
Speaker 1: little bit more than we need to for other problems
Speaker 1: that we solve every day. And so why don't we
Speaker 1: solve these hard problems? It's because we lack incentives as
Speaker 1: a community data scientist to do something. Um, it's a
Speaker 1: hard problem, and we have no transparency and no accountability
Speaker 1: for the models that we produce. Right, So that means
Speaker 1: that we have little hard business reason to prioritize fairness
Speaker 1: and to spend time working on addressing this hard problem. Well,
Speaker 1: you see a black tech green money. Let's talk about
Speaker 1: algorithmic bias. You probably like, yo will. What in the
Speaker 1: world is algorithmic bias? The wikipedias is it describes systematic
Speaker 1: and repeatable error in the computer system that create unfair outcomes,
Speaker 1: such as privileging one category over another in ways different
Speaker 1: from the intended function of the algorithm. Now we can
Speaker 1: debate whether these things are intended or not intended. But
Speaker 1: that's a different conversation for another day. But these canna
Speaker 1: have a direct impact on you when it determines which
Speaker 1: political ads you see, or how many cops are deployed
Speaker 1: in your neighborhood, or even your insurance premiums, how much
Speaker 1: you pay for insurance. It was a study that show
Speaker 1: even though black Americans are four times more likely to
Speaker 1: have kidney failure, an algorithm to determine the priority of
Speaker 1: patients on a kidney transplant list put black patients lower
Speaker 1: on the list than white patients, even when all other
Speaker 1: factors remain identical. So today on Black Tech, Green Money,
Speaker 1: we're hearing from Matthew Finney, who's a data scientist is
Speaker 1: strategy consultant at Harvard. He was a speaking from Afro
Speaker 1: Tech World and in his day job, he phillips AI
Speaker 1: decision systems to help large organizations and make an impact
Speaker 1: on their most challenging business emission problems. I can sometimes
Speaker 1: be a reluctant technologist, don't get me wrong. In the
Speaker 1: last decade we have made some amazing feats with artificial intelligence.
Speaker 1: We've been able to figure out what you want to
Speaker 1: buy before you knew you wanted it we can have
Speaker 1: a self driving, artificially intelligent electric car, and if that
Speaker 1: was enough, we put it in space. We've trained AI
Speaker 1: to read mammograms with particular skill at diagnosing a set
Speaker 1: of highly invasive cancers that radiologists had missed, but we
Speaker 1: still hadn't figured out how to make our technology treat
Speaker 1: others the way that we would want to be treated.
Speaker 1: So I promise I'm not just gonna stick to that
Speaker 1: gloom and doom topic today. So what are we gonna do. First,
Speaker 1: we're gonna define and measure algorithm bias. Then we're gonna
Speaker 1: figure out how we can isolate the root causes of
Speaker 1: poor algorithm behavior, and finally, we're going to learn how
Speaker 1: we can all take action to make algorithms more fair.
Speaker 1: So let's get started. I want to evaluate algorithmic bias
Speaker 1: here through the lens of a case study, and we'll
Speaker 1: learn how to, through this case study, apply the tools
Speaker 1: more generally. Kidneys are really important. Obviously, their main function
Speaker 1: in our body is to help us filter out waste,
Speaker 1: and so there's a metric of kidney function called the
Speaker 1: glomerular filtration rate that's very important for diagnosed and kidney
Speaker 1: disease However, this metric is really hard to measure directly.
Speaker 1: If you were going to measure directly, you need to
Speaker 1: collect the waste from the kidney over the period of
Speaker 1: twenty four hours. So it's not practical, it's not fun
Speaker 1: for anyone. That's why in the seventies they developed an
Speaker 1: algorithmic way to estimate this metric. UH Doctors can take
Speaker 1: a sample of your blood and measure the level of
Speaker 1: asset called creatomy that's in your blood sample, and there's
Speaker 1: a Russian equation that takes that crowdning metric and turns
Speaker 1: it into a kidney function index, this creating any metric
Speaker 1: that they use. When researchers were developing the model, they
Speaker 1: realized that creating is highly sensitive to someone's muscle mass,
Speaker 1: you know, given that it's actually a byproduct of muscle activity.
Speaker 1: And so when they were trying to make the algorithm
Speaker 1: as accurate as they could, researchers determined that because African
Speaker 1: Americans have higher muscle mass, they have higher baseline crawdning levels,
Speaker 1: and so they decided that they were going to adjust
Speaker 1: the c k D EPI algorithm, this kidney function algorithm,
Speaker 1: to increase kidney function index scorers for African Americans to
Speaker 1: control for this muscle difference. Here, a higher kidney function
Speaker 1: score indicates that your kidney is healthier, so African Americans
Speaker 1: were being given kidney index scores that were showing their
Speaker 1: kidneys were healthier than a white person with the same
Speaker 1: observable metrics. Interestingly, the United States is the only place
Speaker 1: in the world that we do this race correction for
Speaker 1: kidney functions, and there are many other places in the
Speaker 1: world where we have a large population of people with
Speaker 1: African heritage. This is because people see that this correction
Speaker 1: is unfair. There are two specific definitions of fairness that
Speaker 1: we use in the algorithm community. The first is group fairness,
Speaker 1: and the idea behind group fairness is that in your
Speaker 1: data set, you have groups that are identifiable and they
Speaker 1: should be treated similarly to the population as a whole. Right,
Speaker 1: So a group could be all people with blue eyes,
Speaker 1: people with red hair, everyone who lives in Minnesota, all men,
Speaker 1: people of Latin heritage. All those are examples of groups.
Speaker 1: And if you have an algorithm that is grouped fair
Speaker 1: that means that the algorithm treats all of these groups
Speaker 1: similarly to the rest of the population. Regardless of whether
Speaker 1: or not the algorithm has that information about the sensitive attribute.
Speaker 1: That means someone's in a group or not. So let's
Speaker 1: look at the second definition, individual fairness. Individual fairness means
Speaker 1: that similar individuals should be treated similarly. In An example
Speaker 1: of that is, let's say you have two people who
Speaker 1: have equal incomes and equal credit history, and they're applying
Speaker 1: for credit at a bank, and the bank uses an
Speaker 1: algorithmic decision system to determine whether or not to extend
Speaker 1: credit and a certain credit limit to the customers. So,
Speaker 1: given that they had the same income and the same
Speaker 1: credit history, even though one is male and the other's female,
Speaker 1: both individuals should get the same credit limit if the
Speaker 1: algorithm is individually fair. So now let's dive into this
Speaker 1: kidney function algorithm again and let's think is this algorithm fair.
Speaker 1: So first we'll look at the group fairness of the
Speaker 1: c K D E P I algorithm. UM. The chart
Speaker 1: here on the rank is taking a look at the
Speaker 1: media number of days that adults in the United States
Speaker 1: who received kidney transplants spent on the waiting list for
Speaker 1: a kidney before they receive the transplant. UM something stands
Speaker 1: out almost immediately here, and it's that African Americans can
Speaker 1: spend over twice as long as Caucasians on the waiting
Speaker 1: list for a kidney in the United States. Right, So,
Speaker 1: African Americans are spending years on the waiting list, and
Speaker 1: part of this is because of the c K D
Speaker 1: e PI algorithm that's giving them higher kidney functions scores
Speaker 1: even though their kidney might not be functioning well, and
Speaker 1: that puts them at a lower priority on the waiting
Speaker 1: list for a kidney. So this is treating African Americans
Speaker 1: as a group different from groups of other Americans, and
Speaker 1: that's something we should be concerned about. This algorithm is
Speaker 1: not group fair. So now let's consider is this algorithm
Speaker 1: individually fair. Individual fairness means that we treat similar individuals similarly.
Speaker 1: And in this algorithm, we can have two individuals who
Speaker 1: have the same muscle mass and the a level of
Speaker 1: creating me measured in their blood. But if one of
Speaker 1: them is white and one of them is black, they're
Speaker 1: going to get different scores for their kidney function, such
Speaker 1: that the black person will get a score indicating a
Speaker 1: healthier kidney than the white person. Um this is concerning, right,
Speaker 1: This is not individually fair and the medical community starting
Speaker 1: to come around to this. So last year in the
Speaker 1: Journal of the American Medical Association, they published an article
Speaker 1: asking to reconsider the use of race and the kidney
Speaker 1: function algorithm. And there was a sentence here that I
Speaker 1: thought was really important. With the e G. F Our
Speaker 1: equation that's being used, it asserts that existing organ function
Speaker 1: is different between individuals who are identical except for race.
Speaker 1: Race is causing African Americans to get unfavorable scores of
Speaker 1: their kidney measurement function that might lead them to get
Speaker 1: a lower priority on the waiting list to receive an
Speaker 1: organ that's desperately needed. This might seem obvious that these
Speaker 1: types of scenarios are bad, right, and we shouldn't be
Speaker 1: using race for something that could have unfair outcomes that
Speaker 1: cause life or death situations for people. But this keeps
Speaker 1: happening over and over again. Any week you can open
Speaker 1: up the newspaper and see a new algorithm that was
Speaker 1: racist or sexist. You know, name YOURYSM. There's an algorithm
Speaker 1: that is suffering from it. So let's talk about how
Speaker 1: and why this happens. First, I want to just talk
Speaker 1: about how we make models. Algorithmic models are function of
Speaker 1: three things, technology, people, and process. On the technical front,
Speaker 1: you know, that's where we consider the data that you're
Speaker 1: using to train your model and the specific algorithm for example,
Speaker 1: so that could be a neural network, that could be
Speaker 1: a linear progression, that could be anything in between. On
Speaker 1: the people front, you know, that's where we consider the
Speaker 1: role of people like myself, data scientists, business owners who
Speaker 1: come up with the business requirements for these algorithms, and
Speaker 1: the end users who actually take the algorithms and put
Speaker 1: them into practice to make decisions. And the last component
Speaker 1: here are the processes, the processes that we use to
Speaker 1: tread our models, to evaluate our models, and apply them
Speaker 1: in practice. And by breaking down the process of building
Speaker 1: a model into these three components, we can evaluate them
Speaker 1: individually when we want to determine the root cause of
Speaker 1: algorithmic fairness or algorithmic bias. So how did we make
Speaker 1: a biased kidney function model in the context of these
Speaker 1: three components. First, let's look at technology. So when researchers
Speaker 1: were developing the c K D E p I algorithm,
Speaker 1: they had many different ways that they could consider that
Speaker 1: we're technologically feasible to measure and estimate e g. F R.
Speaker 1: There was a direct way of measuring at gloom earlier
Speaker 1: filtration rate, which was very difficult but not impossible, and
Speaker 1: we could have on with that as medical community. There
Speaker 1: were other alternatives to things that we can measure in
Speaker 1: the blood Beyond looking at the creatomy, which is sensitive
Speaker 1: to muscle mass. We could have instead decided to look
Speaker 1: at sistat and see, which is another indicator of kidney
Speaker 1: function that has no sensitivity to muscle muscle mass. And
Speaker 1: there were also better ways of measuring muscle mass that
Speaker 1: were technologically possible beyond just looking at someone's race to
Speaker 1: estimate muscle mass. Right, So technology wasn't the constraint here
Speaker 1: that let us to have a unfair algorithm for measuring
Speaker 1: kidney function. Let's evaluate the people. Now it's gonna sound
Speaker 1: like I'm glossing over this one, but I really do
Speaker 1: want to assume the researcher's best intentions here when they
Speaker 1: decided to build this regression model for measuring kidney function.
Speaker 1: And I also want to assume that the doctors have
Speaker 1: only the best intentions and the best interests of their
Speaker 1: patients and mind when they make decisions on ordering this
Speaker 1: test and recommen patients for kidney transplants, So I don't
Speaker 1: think that people are the constraint here either. That led
Speaker 1: us to have a biased model. So now let's look
Speaker 1: at the process. The process here for building this model
Speaker 1: was optimized for overall accuracy of the model. So we
Speaker 1: mentioned how when researchers decided to include race in the
Speaker 1: model that they were training, they got a slight overall
Speaker 1: accuracy boost in the model, and that was the driving
Speaker 1: factor in the decision to include race as a predictor
Speaker 1: of kidney function. That process, that's where I want to
Speaker 1: dive deeper. That's where our failure was. We had a
Speaker 1: process that was optimized for accuracy and not for fairness objectives,
Speaker 1: and because of that, that's how researchers developed a kidney
Speaker 1: function model that was biased racially and had led to
Speaker 1: unfair outcomes. A couple of years ago, the US Department
Speaker 1: of Education Civil Rights Data Collection released information showing that
Speaker 1: black and Latino students lack access at the high school
Speaker 1: level to high level science and math classes and predominantly
Speaker 1: white schools, calculus was offered across fifty percent of them.
Speaker 1: In predominantly minority schools, just thirty three physics sixty seven
Speaker 1: percent for white, forty percent for minority, algebra eight fo
Speaker 1: percent for white, seventy one percent for minority. Now this
Speaker 1: matters because these have downstream effects. High aptitude in these
Speaker 1: STEM fields us higher representation in STEM careers. So when
Speaker 1: we're not represented well, the systems don't get built for
Speaker 1: us or even with our input appropriately considered. So how
Speaker 1: can these systems that weren't built with our input play
Speaker 1: out negatively in our communities? As a data scientist, you know,
Speaker 1: we are in a profession where there's a high emphasis
Speaker 1: on overall accuracy and a number of procedural technical controls
Speaker 1: that promote that. On the technical side, we have many
Speaker 1: metrics like just overall vanilla accuracy, MSc, precision recall, you
Speaker 1: name it, specialized metrics to measure the accuracy of our models.
Speaker 1: And then we have procedures like p testing that help
Speaker 1: us make determinations about whether or not we should deploy
Speaker 1: a certain model into practice. But we don't have that
Speaker 1: same infrastructure for fairness. Um. As someone who's been in
Speaker 1: the room where it happens, you know, I can tell
Speaker 1: you where I think specifically, this type of process breakdown
Speaker 1: affected our our kidney function model that we've been evaluating.
Speaker 1: So let's look at specific things that they missed. UM. First,
Speaker 1: let's address this chart here on the right. This is
Speaker 1: a chart that shows muscle mass by ray among a
Speaker 1: population of the US adults. The blue line represents white
Speaker 1: Americans and the red line represents Black Americans. So we
Speaker 1: can see that while on average, black Americans have a
Speaker 1: slightly higher muscle mass and white Americans UM, this shift
Speaker 1: is so slight that the distributions of muscle mass by
Speaker 1: race overlap almost entirely. What this tells me as a
Speaker 1: data scientist and a statistician is that an individual's race
Speaker 1: tells me next to nothing about that person's muscle mass.
Speaker 1: And so, as a researcher developing a kidney function algorithm,
Speaker 1: if I was concerned about muscle mass, I would have
Speaker 1: seen this chart and said, Wow, race is not a
Speaker 1: predictor for muscle mass. That's going to help us, uh
Speaker 1: improve the accuracy of our algorithm in a way. That's fair,
Speaker 1: because you know, if we treat individuals as just members
Speaker 1: of a race, we're actually not going to give that
Speaker 1: person the best healthcare. So nothing in their process forced
Speaker 1: them to look at whether or not race is predictive um,
Speaker 1: in in in a broad sense for their objective, which
Speaker 1: was to control for muscle mass. Nothing also forced them
Speaker 1: to consider what the impact of using race would be
Speaker 1: on the fairness of their model. So they didn't consider
Speaker 1: the societal impacts of using race and healthcare. They also
Speaker 1: didn't consider, um, how that would impact individuals you know,
Speaker 1: who are on the waiting list for a kidney, and
Speaker 1: how that might lead to individuals who are equally qualified
Speaker 1: to receive a kidney uh be uh differentially prioritized on
Speaker 1: the list to receive that kidney based on race. So
Speaker 1: why isn't fairness part of our process here? Um? It's
Speaker 1: because well, as data scientists and statisticians and researchers, we
Speaker 1: had good intentions. We lack those mechanisms for action. We
Speaker 1: lack things in our process that forced us to consider
Speaker 1: hard questions. UM. It would be really easy to say
Speaker 1: that we have biased algorithms because there are biased individuals
Speaker 1: who want to encode their bias and the algorithms. UM.
Speaker 1: And while I can't rule that out completely, let me
Speaker 1: tell you that of the time that is not the case. Right.
Speaker 1: Here's my hypothesis. Fairness is context specific um, meaning that
Speaker 1: depending on what type of algorithm we're training, there might
Speaker 1: be a different fairness subjective, and there might be different
Speaker 1: rules for what's fair and what's unfair. So, for example,
Speaker 1: there could be some healthcare scenarios where race is actually
Speaker 1: an important predictor of a person to have overall health
Speaker 1: or or risk for a disease, and those scenarivos might
Speaker 1: be areas where it's fair to include race in an algorithm.
Speaker 1: But it's something like this kidney function algorithm, we can
Speaker 1: see that including race is clearly unfair. Um. And it's
Speaker 1: because that there are these multiple notions of fairness with
Speaker 1: different context dependencies that fairness is actually a hard problem
Speaker 1: to solve. And for data scientists, you know, this is
Speaker 1: a hard problem without a unique, closed form mathematical solutions,
Speaker 1: meaning we need to use our brains a little bit
Speaker 1: more than we need to for other problems that we
Speaker 1: solve every day. And so why don't we solve these
Speaker 1: hard problems. It's because we lack incentives as a community
Speaker 1: data scientist to do something. Um, it's a hard problem,
Speaker 1: and we have no transparency and no accountability for the
Speaker 1: models that we produce. Right, So that means that we
Speaker 1: have little hard business reason to prioritize fairness and to
Speaker 1: spend time working on addressing this hard problem if no
Speaker 1: one's ever going to be able to see, you know,
Speaker 1: the steps that we took to address it and the
Speaker 1: impact of our work. So, considering this process and mechanism
Speaker 1: failure for fairness, how will we end algorithmic bias? So
Speaker 1: I want to return to this idea, yeah, that algorithmic
Speaker 1: models are a function of three major components technology, people,
Speaker 1: and process. This is actually a question I asked often,
Speaker 1: and I've asked in conversations about algorithm algorithmic fairness with
Speaker 1: all kinds of people technologists, computer scientists, mathematicians, lawyers, ethicist, activists,
Speaker 1: policy makers, and sociologists and many more. Right, And so
Speaker 1: I found through these conversations and through some of my
Speaker 1: own research that there are many existing approaches to addressing
Speaker 1: algorithmic bias, and they generally fall in the technology and
Speaker 1: people the veins. And so that's what we're looking at here,
Speaker 1: just a couple of those different approaches that are already
Speaker 1: out there that allows to address algorithmic fairness on the
Speaker 1: technology front. I want to highlight that we already do
Speaker 1: have class of algorithms that are always fair or fair
Speaker 1: within certain constraints, and we're not always using them our work.
Speaker 1: That's the problem. But there are tools out there that
Speaker 1: allows to implement these very directly. So IBM, for example,
Speaker 1: recently released a toolkit called AI Fairness three sixty UM
Speaker 1: and it has fair machine learning algorithms and machine learning
Speaker 1: diagnostics already implemented in Python that can be adapted to
Speaker 1: any other type of prediction problem. Now, if you're a
Speaker 1: little bit more adventurous, there's also a community of academics
Speaker 1: who are on the cutting edge of research of algorithmic fairness.
Speaker 1: And I'll point out the Symposium on the Foundations of
Speaker 1: Responsible Computing as one place where you can go and
Speaker 1: learn about a lot of those really cutting cutting edge
Speaker 1: research topics. All these videos from the symposium are actually
Speaker 1: publicly available on YouTube, so that you can add your
Speaker 1: leisure learn about these topics from the academics who developed
Speaker 1: them themselves. On the people front, right, we have a
Speaker 1: lot of existing organizations that attack length education and tackling
Speaker 1: the social movement component of this as well. Just to
Speaker 1: name a few of organizations that are doing many great things.
Speaker 1: Are we have data for black Lives and the Algorithmic
Speaker 1: Justice League that are tackling that social movements and social
Speaker 1: activism approach to encouraging algorithmic fairness. And then there's also
Speaker 1: an organization called AI for All that is UH tackling
Speaker 1: the education. So given that we see a lot of
Speaker 1: existing work out there on the technology and people fronts,
Speaker 1: I want to turn our attention to process where there's
Speaker 1: relatively less existing work, and that's where the focus of
Speaker 1: my research is what mechanisms can help us to build
Speaker 1: fair algorithmic models. I'll return to those challenges that we
Speaker 1: discussed before, the fact that algorithm fairness is hard to
Speaker 1: define and hard to measure, and because of a lack
Speaker 1: of transparency and accountability, we have a few incentives to
Speaker 1: actually go in an and tackle the heart problem. So
Speaker 1: first I want to propose an approach that will allow
Speaker 1: us to make this hard problem a little bit easier
Speaker 1: for us to solve. And it's called a fairness statement.
Speaker 1: So what is a fairness statement? That's an application specific
Speaker 1: commitment to defined and measurable fairness goals. The scope of
Speaker 1: this fairness's statement is going to include defining the relevant
Speaker 1: fairness objective or constraint for the specific algorithm that we're
Speaker 1: working on developing. So, for example, that could be we
Speaker 1: want to make sure that African American people and white
Speaker 1: people received similar kidney functions scores for similar actual kidney function. Now,
Speaker 1: now that we've defined a fairness objective, we can document
Speaker 1: potential sources of bias that might impact our fairness subjective
Speaker 1: and also the downstream impact will see two individuals or groups, right,
Speaker 1: So this might be the place where we raise well,
Speaker 1: if our algorithms racial racially bias, we might see African
Speaker 1: Americans play prioritize at a lower priority on the kidney
Speaker 1: waiting list, and I might leave to adverse healthcare outcomes
Speaker 1: for that population. Finally, once we've documented the source of biases,
Speaker 1: we can identify appropriate procedural and technical controls that we
Speaker 1: would would take to mitigate the unacceptable risks. Right. So
Speaker 1: that could be, for example, implementing one of the classes
Speaker 1: of fair algorithms that we discussed before. One of the
Speaker 1: key benefits of the fairness statement is that it gives
Speaker 1: data scientists a named goal they can work towards, and
Speaker 1: that helps them informed choices and trade offs in the
Speaker 1: development of algorithms and the deployment. So, for example, if
Speaker 1: we had a fairness statement that was in place for
Speaker 1: the researchers who developed the c k d EPI algorithm
Speaker 1: for kidney function UH, that might have helped them say, hey,
Speaker 1: we could include race and have a slight bump and
Speaker 1: overall accuracy for our algorithm. But that presents a high
Speaker 1: risk of unfair outcomes. Therefore, the cost of this solution
Speaker 1: outweighs the small benefit of controlling for race and measuring
Speaker 1: kidney function. Now, the other key thing fit here is
Speaker 1: that this allows algorithmic developers to catch problems early, at
Speaker 1: the stage when the algorithm is still in development and
Speaker 1: before it's been deployed into the world. This might mean
Speaker 1: that we catch an issue before it actually creates harm
Speaker 1: for people in real life. So now that we've talked
Speaker 1: about how we can make the UH fairness problem a
Speaker 1: little bit less hard, now let's talk about how we
Speaker 1: can incentivize people to actually tackle it. I want to
Speaker 1: propose an approach called the algorithmic Practice audit. So what
Speaker 1: is this? As an independent third party review of an
Speaker 1: organization's algorithmic the season outcomes. On the process front, we
Speaker 1: might evaluate questions like are we using a representative training
Speaker 1: data set to trade our model. We might also question
Speaker 1: whether or not the organization is using fair classes of
Speaker 1: algorithms when they exist to train models. On the outcome front,
Speaker 1: we might evaluate the actual fairness objective that was in
Speaker 1: the fairness statement. Is the model meeting the stated fairness goals.
Speaker 1: We might also be able to look at whether or
Speaker 1: not biases introduced by humans in the last mile of
Speaker 1: the algorithmic decision making process, right, so in that stage
Speaker 1: where the algorithm has made a prediction and then it
Speaker 1: takes a human to go and implement it and turn
Speaker 1: it into a decision. So a key benefit of this
Speaker 1: is that it's a forcing function that allows our data
Speaker 1: scientists to actually invest time in algorithmic fairness because there
Speaker 1: are penalties. There are real penalties um to not actually
Speaker 1: having a fair algorithm. And another key benefit is that
Speaker 1: this can be a signal for your organization to your
Speaker 1: customers and shareholders that any algorithmic services you provide are
Speaker 1: correct and fair. Right, So imagine that you're a customer
Speaker 1: and you can transact with an organization that you know
Speaker 1: has fair algorithms and that is certified as such, or
Speaker 1: you can spend your money with another organization where it's
Speaker 1: upanly or whether or not their algorithms are fair. You
Speaker 1: might choose as a customer to spend your money with
Speaker 1: an organization that has fair algorithms. Now, if you're a shareholder,
Speaker 1: you might also be at more confident in an organization
Speaker 1: that you know is UH spending time and energy on
Speaker 1: algorithmic fairness, because that might be a signal to you
Speaker 1: that the organization won't end up on the front page
Speaker 1: of the New York Times for having an unfair racist
Speaker 1: algorithm in the future. And I want to just highlight
Speaker 1: that while this seems like a hard problem, these types
Speaker 1: of mechanisms actually work and we can implement them to
Speaker 1: make change in the way that algorithm predictions happen. So
Speaker 1: let's look at the example of the system Risk indicator.
Speaker 1: In the Dutch government developed the system Risk Indicator to
Speaker 1: detect benefit fraud. Right, But while the government developed it,
Speaker 1: it was only applied by a certain number of cities,
Speaker 1: and the cities that applied this algorithm um only applied
Speaker 1: it to some of the applications for benefits that they received,
Speaker 1: and specifically it was applied in low income and immigrant neighborhoods,
Speaker 1: So these populations of people were specifically targeted by the
Speaker 1: algorithm to identify possible benefit risk. This is unfair and
Speaker 1: and the Dutch court actually UH did an investigation and
Speaker 1: found just as much. UM. They shut down this algorithmic
Speaker 1: system because of the possibility of discrimination based on socio
Speaker 1: economic status, ethnicity, and religion. Essentially, what they found was
Speaker 1: that the algorithm did not meet the stated fairness objectives
Speaker 1: of the Dutch government because it was discriminating against people
Speaker 1: based on immutable characteristics. And because of that, they stopped
Speaker 1: using this algorithm UH in benefit processing for Dutch citizens
Speaker 1: and residents. So we know it works. What will you
Speaker 1: do to create fair algorithms? I want to leave you
Speaker 1: with a couple of my suggestions, UM, and this is
Speaker 1: something that we can tackle as organizations and also as individuals.
Speaker 1: In an organization, you might question whether or not you're
Speaker 1: using existing classes of fair algorithms, such as those released
Speaker 1: by IBM and the AI three sixty tool kit. You
Speaker 1: might also consider whether or not you have mechanisms in
Speaker 1: place to ensure algorithm fairness, such as the Fairness Statement
Speaker 1: and the algorithmic Practice audit. As an individual, you might
Speaker 1: do an inventory of all the algorithmic decisions that occur
Speaker 1: in your life. You know, with customers that you work with,
Speaker 1: with companies that you buy from, with your employer, with
Speaker 1: your apartment building. These are everywhere. And then once you've
Speaker 1: done that inventory, you might request and review algorithmic audits
Speaker 1: from the organizations that you know are making some of
Speaker 1: the most impactful decisions about you using algorithms. Black Tag
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