Carl T. BergstromJul 2, 2023

THREAD: Bias and disparity in a causal modeling framework.

1. A few months ago, @vtraag and @LudoWaltman posted a superb paper to the arXiv.

http://arxiv.org/abs/2207.13665

I've been meaning to write about it for a while and finally found the time.

Causal foundations of bias, disparity and fairness

The study of biases, such as gender or racial biases, is an important topic in the social and behavioural sciences. However, the literature does not always clearly define the concept. Definitions of bias are often ambiguous or not provided at all. To study biases in a precise manner, it is important to have a well-defined concept of bias. We propose to define bias as a direct causal effect that is unjustified. We propose to define the closely related concept of disparity as a direct or indirect causal effect that includes a bias. Our proposed definitions can be used to study biases and disparities in a more rigorous and systematic way. We compare our definitions of bias and disparity with various criteria of fairness introduced in the artificial intelligence literature. In addition, we discuss how our definitions relate to discrimination. We illustrate our definitions of bias and disparity in two case studies, focusing on gender bias in science and racial bias in police shootings. Our proposed definitions aim to contribute to a better appreciation of the causal intricacies of studies of biases and disparities. We hope that this will also promote an improved understanding of the policy implications of such studies.

arXiv.org
Show threadCarl T. BergstromJul 2, 2023

2. Society is rife with differences that we feel are unjust.

Differences in opportunities and outcomes according to race are ubiquitous in American society.

Or in science, I've written about gender differences in scientific authorship and citation, for example.

We often call these "biases".

Show threadCarl T. BergstromJul 2, 2023

3. In this paper, Traag and Waltman propose definitions and methodology for thinking more rigorously about the nature of these differences, in ways that help us identify where we can best intervene to ameliorate them.

To do so, they propose that we distinguish between *biases* and *disparities*.

Show threadCarl T. BergstromJul 2, 2023

4. They define biases as differences due to (1) we consider unjustified that are (2) causal and (3) causally direct.

For example, if a hiring manager chose not to hire people of a certain race, the racial differences in employment in that firm would be unjustified, caused by race, and directly so.

Show threadCarl T. BergstromJul 2, 2023

5. They define disparities as (1) unjustified (2) causal differences that are (3) causally indirect.

For example, if the education system denies opportunities to people of a certain race and thus they cannot even apply for employment at a firm, racial differences in employment at that firm will be unjustified, caused by race, but (assuming. a fair hiring manager) indirectly so.

These are disparities.

Show threadCarl T. BergstromJul 2, 2023

6. Biases upstream in a causal pathway lead to disparities downstream even when subsequent causal processes are fair.

If I'd had access to this paper back a few years ago when I wrote about what I called a gender bias in self-citation rates, I would have written a better paper.

In paper we identified a difference in self-citation rates by gender.

https://journals.sagepub.com/doi/10.1177/2378023117738903

Show threadReuben Binns⁉️Jul 2, 2023
@ct_bergstrom thanks for the thread, I will read the paper! One question / nitpick with the user of 'fair' here: couldn't fairness sometimes require remedying disparity as well as simply avoiding bias? Such that if 'subsequent causal processes are fair', then the disparities would not exist
Show threadDion Henare

@RDBinns @ct_bergstrom I think the problem is that disparities aren't always in the same direction as the original bias. So it's harder to know if you're doing the right thing when you address a disparity.

E.g. A bias against hiring women leads to the average woman (who gets hired) being more qualified than the average man. Then, a fair downstream process that rewards ability will produce disparities in favour of women. Fixing that disparity would basically double-down on the original bias.

Jul 2, 2023 at 10:27amWeb