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Algorithmic Fairness

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Data Science Ethics

University of Michigan

4.6 (95 ratings) | 3.7K Students Enrolled

What are the ethical considerations regarding the privacy and control of consumer information and big data, especially in the aftermath of recent large-scale data breaches? This course provides a framework to analyze these concerns as you examine the ethical and privacy implications of collecting and managing big data. Explore the broader impact of the data science field on modern society and the principles of fairness, accountability and transparency as you gain a deeper understanding of the importance of a shared set of ethical values. You will examine the need for voluntary disclosure when leveraging metadata to inform basic algorithms and/or complex artificial intelligence systems while also learning best practices for responsible data management, understanding the significance of the Fair Information Practices Principles Act and the laws concerning the "right to be forgotten." This course will help you answer questions such as who owns data, how do we value privacy, how to receive informed consent and what it means to be fair. Data scientists and anyone beginning to use or expand their use of data will benefit from this course. No particular previous knowledge needed.

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4.6 (95 ratings)

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JM

Jul 01, 2018

This course is short, slow, and easy, but I ranked it five stars because the content is important in today's growing reliance on data science.

SM

May 15, 2018

Excellent Course. Gives interesting and detailed perspectives on ethical matters related to how data can be used and should be used.

Aus der Unterrichtseinheit

Algorithmic Fairness

What could be fairer than a data-driven analysis? Surely the dumb computer cannot harbor prejudice or stereotypes. While indeed the analysis technique may be completely neutral, given the assumptions, the model, the training data, and so forth, all of these boundary conditions are set by humans, who may reflect their biases in the analysis result, possibly without even intending to do so. Only recently have people begun to think about how algorithmic decisions can be unfair. Consider this article, published in the New York Times. This module discusses this cutting edge issue.

Algorithmic Fairness10:12

Correct But Misleading Results12:09

Case Study: High Throughput Biology3:38

Case Study: Geopricing2:52

Case Study: Your Safety Is My Lost Income10:10

Unterrichtet von

H.V. Jagadish
Bernard A Galler Collegiate Professor

Skript

Welcome to this module on algorithmic fairness.

Often we say let the data speak for themselves.

What could be less biased, what could be more fair than just following the data and

letting the data take us wherever they may lead.

Well it turns out that algorithms can be biased.

And there are many ways in which these biases can arise.

You can have these biases because the training data set isn't representative

of the population.

Because the past population isn't representative of the future population.

Because there are confounding processes that lead to correlations that are flukes.

Let's begin by considering a simple example.

So here's a company that has only 10% women employees, and

it has a boys' club culture that makes it difficult for women to succeed.

The hiring algorithm is trained on current data and

based on current employee success, it scores women candidates lower.

The algorithm is fairly representing what happens today.

It's fairly representing that women have difficulty

succeeding in this company because of the boys' club culture.

But the net consequence of this is that the algorithm

is scoring women applicants lower.

And so the company ends up hiring even fewer women.

This is an algorithmic vicious cycle.

It arose because of a bias in the algorithm.

And the bias arose in the algorithm because of the data

that it was trained on.

The net effect is nicely captured in this carton.

If you've got a cyber cafe for

Macs you're not going to get any data about PCs, and you don't

know what kind of services to provide if you're going to make a PC user happy.

This kind of thing is now being recognized and it's even there on TV.

Such as an episode of the CBS serial, The Good Wife.

So what are things that can give us bad results from good data?

I want to talk about three different things.

First, a notion of correlated attributes.

The second, a notion of misleading results, even if they're correct.

And third, a more technical thing called P-hacking.

Let's begin by talking about correlated attributes.

Racial discrimination has been a major historical problem in United States.

There are many laws concerning racial discrimination.

On the one hand, you have laws that prohibit consideration of race for

some things.

For example, universities are prohibited by law from considering race

in admissions, at least some universities in some states.

Now universities may value diversity, may want to consider race,

may have affirmative action interests in mind.

And so the way that they can deal with this law is to find other

features that can get them the diversity that they seek without violating the law.

Famously, for example, the University of Texas completely

changed the notion of how they were considering race in admissions.

And they completely took it out and simply said they'd take

the top ten percent of the class in any school in Texas.

And given the strong degree of racial segregation in Texas high schools,

they knew that there would be many high schools that had minorities as,

essentially, the population of the school, and

therefore minorities in the top 10%.

And this would give them the diversity of student

body that they sought without in any way breaking the law.

Now the same notion of racial discrimination,

that intent is a problem that we've had with lenders in the past

didn't want to lend to borrowers who were black.

And laws were passed to prevent them from such discrimination.

So they found a technique of using surrogates.

Specifically, by redlining neighborhoods

in the city that were primarily minority.

And they could use that idea to get them very close to the racial

discrimination that they wished to do.

And so now the laws prohibit lenders from explicitly red lining either.

That stops one specific notion of surrogate attribute.

The point really is, that in general proxy attributes can be found.

And if you were a lender who wished to discriminate on the account of race,

it's probably not too hard for you to find other attributes that

are highly correlated with race and that you can use legally.

And you could use that to discriminate if that were your intent.

So really, the point is big data provides the technology to facilitate such

proxy discrimination.

And the saving grace is that big gata also provides the technology to detect and

address such discrimination.

And so it is up to us as responsible data scientists to make

sure that we're using the power of the technology we have to do the right thing.

One of the things that we want to do is to stop unintentional discrimination.

There is a well-known case of the Staples store that

unintentionally discriminated based on users' ZIP codes.

And this kind of thing could have been stopped if

the right discrimination analyses had been done ahead of time.

So in short, intent matters when one is considering discrimination.

And one has to be clear about what kind of discrimination one wants to avoid.

So to talk about this,

let's talk about discrimination as one target group versus everyone else.

So what we want to consider as discrimination is if an individual from

the target group gets treated differently from an otherwise identical

individual not from the target group.

So you have a black person or a white person with identical qualifications and

one of them gets called back and the other one doesn't for a job interview.

Well we can all agree that that is discrimination, but

that is discrimination at the individual level.

There's also a question of discrimination in terms of aggregate outcome.

And here it's a broader definition of discrimination.

So you can say, what's the percentage success of the target group

measured with respect to the control which is the general population.

And you can compare this amongst the candidates,

we can compare this as a ratio of the full population universe,

as a ratio of the qualified candidates.

And the exact results that one gets in each these is a little bit different.

But they all show to what extent we

are managing the societal outcomes with respect to discrimination.

Another thing to keep in mind is

that unintentional discrimination is hard to avoid.

It happens and we need to use our data analytic

techniques to be able to avoid these better.

In 2015, experiments at Carnegie Mellon University

showed that significantly fewer women than men

were shown online ads for high paying jobs.

This happen presumably because the ads that were shown

were selected based on recent past history of click throughs.

And presumably men were clicking on this ads for

high paying jobs more often than women were.

And so this was again, not intentional discrimination

on the part of some company and their choice of ads.

But rather algorithmic discrimination that took

place because, it was promulgating the status quo.

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