A taxonomy of bias: sensemaking, heretical physics, and the Tom Hanks/Bill Murray multiverse

005 | 2025-11-25

Bias has a pretty well-known definition in the world of AI/ML programming. But today’s paper asks us to expand that definition and consider how cultural, organizational, and human forces can intersect with development. The authors of “Identifying and Categorizing Bias in AI/ML for Earth Sciences” argue that, beyond considering bias and before mitigating bias, developers ought to be able to identify bias in all its modes & forms. Their taxonomy of bias is helpful, and inspired us to create an actionable check list of our own.

Paper

Identifying and Categorizing Bias in AI/ML for Earth Sciences, McGovern et al

Chapters

00:00:03 - Intro
00:02:39 - Abstract
00:03:33 - Discussion of framing
00:09:03 - A taxonomy of bias 09:03
00:47:52 - Our proposed check list for mitigating bias
01:08:14 - Reading recs

A check list for mitigating bias (in developing AI or for really any kind of technological development)

Use the language of “bias”, acknowledging both social and technical bias
Define your specific user and use case early
Establish a clear baseline for comparison
Ensure diversity of perspectives on your team
Practice reflexivity (question your assumptions, and continue that line of questioning throughout the development process.)
Examine your incentives vs. end-user incentives
Prioritize transparency (in your decision-making and in your incentives & goals.)

Recommended reading

AI2ES Newsletter
Groundhog Day (film AND musical theater adaptation)
“Science as a Vocation” by Max Weber
Book of the New Sun by Gene Wolfe (four-book series)
Infinite Powers by Steven Strogatz