Case Study: Algorithms in the Office

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En provenance du cours de University of Michigan

Data Science Ethics

40 notes

University of Michigan

Data Science Ethics

40 notes

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.

À partir de la leçon

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

Rencontrer les enseignants

H.V. Jagadish
Bernard A Galler Collegiate Professor
Electrical Engineering and Computer Science

Who to hire is a very important decision for most companies and

a very difficult one to make.

Given that we have data science

as a potential tool that can help in so many different aspects of society.

It's not a surprise that companies, particularly high tech companies,

turn to data science to help make hiring decisions.

The general idea is you train an algorithm to learn what traits,

what characteristics of applicants are correlated with success on the job.

So you can take anybody who applies or anybody who's a prospect and

you code up whatever you can know about them.

Their qualifications, their experience,

their age, their demographics, perhaps their

performance on certain test questions that you give them at the interview.

You can fold all of that into some prediction about

how well they will do their job.

This sounds like a very sensible, nice,

rational thing to do, but let's consider a possible scenario.

So let's say that you have a company which, like so

many other high tech companies, is mostly male.

And let's say that there is a work culture that actually makes it very difficult for

women to succeed.

This is a certain clubiness To the way that things operate.

An algorithm might, in a completely neutral way and a completely accurate way

deduce, based on current data from the few women who were working at the company,

that the average woman performs less well than the average man.

>> She's a good one, too.

Just look at that record.

>> Okay, now Walter, you've had your little joke.

Give her to somebody else, I asked for a man.

>> And therefore, in terms of its rating, when it's rating applicants,

may rate men higher than women where on other characteristics they're equal.

The net consequence of this is that this company is going to hire even fewer women

and this problem that they have in their workplace is going to continue and

perpetuate with a supposedly neutral algorithm

actually being a tool in this perpetuation.

>> We are alive think we ought to quit before we had a chance to fire us.

There's plenty of other good jobs around here.

I don't know what you're going to do Sally, but

I'm leaving early today to find me another job.

>> If this company wishes to change their work culture and

wishes to employ more women,

they will actually have to modify their algorithm to take that into account.

And have that algorithm become quote, not neutral just to be

able to break out of the vicious cycle that they would otherwise be trapped in.

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