How can we think about building systems not for individual end users, but for groups and communities? So much of computer science is limited by the assumption that there is an individual end-user who should be the benefactor of what we build.
- @marylgray keynote #NeurIPS2021 1/
- @marylgray keynote #NeurIPS2021 1/
ML gravitates to large scale data, even though it has rarely had a robust account of where that data comes from & under what conditions, and is almost always deeply disconnected from the social relationships that produced it. 2/
Data is power. Because data has become so powerful, we must transfer the tools of data collection, aggregation, & sharing from engineers to the communities in society that carry the risks. 3/
Data capture and precision alone can not generate justice.
Friction and bugs are openings to reflection. 4/
Friction and bugs are openings to reflection. 4/
Abstraction as an on-ramp to scale is one of the reasons so many computing efforts fail society. Scaling can’t help but miss the social dynamics that make or break the value of any technical artifact. We are often asked to think our distinct localities & histories don’t matter 5/
The challenge is that not all data can be measured mathematically or captured as data points. Discretely measuring & titrating fairness values does not account for the social theory that fairness is a contested, ever-evolving social ideal. An ever-moving target. 6/
A qualitative approach does not mean throwing away quantification. Qualitative approaches mean you are constantly asking questions about what matters and to whom. It means reckoning with how we have siloed ourselves away from the other forms of expertise we need. 7/
We can measure our effectiveness in how often we disagree with one another in our collaborative work. Disagreement gets at the goal of compromise. Collectively & continuously asking who we are missing. 8/
Above are paraphrased quotes from @marylgray #NeurIPS2021 great, invited talk on "The Banality of Scale: A Theory on the Limits of Modeling Bias and Fairness Frameworks for Social Justice (and other lessons from the Pandemic)" 9/
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