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Errors in Model Design

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Data Science Ethics

密歇根大学

4.6（93 個評分） | 3.5K 名學生已註冊

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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（93 個評分）

5 stars
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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.

BP

Mar 06, 2018

I liked this course very much and Data Science Ethics explained very nice manner. I will recommend to join this course.

從本節課中

Data Validity

Data validity is not a new concern. All too often, we see the inappropriate use of Data Science methods leading to erroneous conclusions. This module points out common errors, in language suited for a student with limited exposure to statistics. We'll focus on the notion of representative sample: opinionated customers, for example, are not necessarily representative of all customers.

Choice of Attributes and Measures6:12

Errors in Data Processing8:22

Errors in Model Design8:01

Managing Change5:18

Case Study: Three Blind Mice4:35

Case Study: Algorithms and Race3:44

Case Study: Algorithms in the Office3:18

Case Study: GermanWings Crash5:50

Case Study: Google Flu5:16

教學方

H.V. Jagadish
Bernard A Galler Collegiate Professor

You could end up with invalid conclusions from your analysis because of errors

in model design, even if you had perfect inputs, perfect data going in.

There are many ways why the model could be incorrect.

You could have errors in your model structure.

You're doing extrapolation.

You're doing feature selection.

And there are some more fancy things that we'll get to.

So let's look at all five of these things in turn.

First, let's look at a question model structure.

The thing that is important to keep in mind is that most machine learning

just estimates parameters to fit a predetermined model.

How do you know that the model you have is appropriate?

Quite often, we end up choosing a very simple model.

Not because we know that the simple model is going to work, but

simply because that's easy.

And so if you have a complex nonlinear process,

that's going on in the world, and you've decided to build a simple linear model.

You could try to fit the data to your linear model and

you can learn the best linear fit, it's not going to be perfect and

you're going to make a decision on whether that correctly represents anything or not.

This kind of situation becomes particularly problematic

when you have to do things like extrapolation.

So here's this simple data, two dimensional data,

where you set, you've got a perfect linear graph in the range from 1 to 4.

And now you want to say, well Y equals 7, what is X equal to?

Well, we really don't know.

Perhaps we can assume, since it's so nice and linear between 1 and

4, that it'll continue to be linear from 4 on through 7.

But perhaps not.

Maybe the real world is extremely nonlinear, and

our choice of linear model is wrong.

And so extrapolation, in particular, is something that can be very dangerous,

unless we have reason to know that our model is correctly chosen.

There's an issue of feature selection.

Did you know for example, that taller people are more likely to grow beards?

No, you say, why is that true?

Well, here's the logic, women are generally shorter than men,

women don't grow beards.

So if you look at the population at the universe of people who have beards,

we're all men.

Since men are generally taller than women,

people with beards are generally taller than people without.

Notice that this happened because we have a large fraction,

about half the population who don't have beards and tend to be shorter.

I did a perfectly reasonable correct analysis,

came out with this factorial statement,

which is on the first bullet here of the slide, which is completely true,

but it doesn't tell us the first thing about taller versus shorter men.

And that actually is the question that's perhaps much more interesting.

Here's a related problem that we have.

Often we have aggregated data and so we'll analyze results for a group and

based on this analysis of group data, we'll ascribe results to the individual.

For example, suppose we have data at the district level that tells

us the districts with higher income have lower crime rates.

Well, from here, we may be tempted to infer that

richer people are less likely to commit crimes.

Actually, this inference doesn't follow from the aggregate data.

That is a possible reason why we might have got the aggregate result that we got,

but there are other possibilities too, having to do with the way

we have the distribution of people with incomes and crime rates and districts.

One particular case of this kind of fallacy

is something called the Simpson's paradox.

And let's work through an example.

Let's say that we're worried about gender discrimination.

And I have the simple matrix here of two universities,

Easy University and Hard University.

And let's look at the acceptance rates of these two places.

So reading the first row of this table, ten men apply to Easy University,

seven of them were accepted, so that's 0.7 acceptance rate or 70% acceptance rate.

Five women applied, four were accepted so

we have 0.8 acceptance rate.

So, if you look at Easy University, the acceptance rate for

women is greater than the acceptance rate for men.

Now, let's look at Hard University.

Hard University is much harder to get into.

Ten men applied only three got in.

So, the acceptance rate for men is 0.3.

Fifteen women applied and five got in,

an acceptance rate of 1/3 or 0.33 which again,

is a little bit higher than the acceptance rate for men.

So, again at Hard University

women are accepted at a slightly higher rate than men.

But now let's look at the last row,

which is the aggregate data of easy and hard combined.

So if you look at all the universities put together, 20 men applied,

10 were accepted and so 50%.

Twenty women applied and only nine were accepted, so it's 0.45.

And there are fewer women being accepted than men to universities.

What's the problem here?

This is known as Simpson's Paradox.

And this arises because the aggregate data is reflecting

the combination of two separate ratios where

the number of candidates in the two columns isn't the same in the two.

So many more women than men apply to Hard U.

And since the acceptance rates at Hard U are so much lower than the acceptance

rates at Easy U, the overall result that we're seeing

Is reflective of more women applying to Hard U

rather than discrimination against women in this made up example.

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