Thread by @math_rachel

Power imbalances in Machine Learning. Technical community has control over:
- what data is collected
- what data is used
- how much to reveal about training dataset
- choosing models
- interpreting outputs
- assessment & verification
- deployment of models @JamelleWD #PAML2020

Beyond Fairness & Ethics: Towards Agency & Shifting Power by @JamelleWD of @Data4BlackLives

Watch here: slideslive.com/38930952/beyon… #ICML2020 #PAML2020

Reclaim data narratives with intention

Ex: covid-19 racial disparities

False, harmful narrative: due to genetics or Black people being uncareful or unhealthy

Reclaimed, accurate narrative: racism NOT race is the root, unequal access, lower quality of care, structural barriers,

As ML researchers, we should all be asking ourselves:
- whose voices are driving interpretations of datasets?
- what are the underlying assumptions we are making with a given dataset?
- what is the social, political, historical context of the dataset?
@JamelleWD @Data4BlackLives

Data trusts: an alternative to current data governance practices. A tool for redistributing data ownership back to the community. #PAML2020 #ICML2020

In the past, @seanmmcdonald has highlighted how data trusts could be used to hold companies accountable for their broken promises & create mechanisms for recourse:

https://twitter.com/math_rachel/status/1250818605601734657?s=20

Using data as a tool for protest, accountability, & collective action requires more than definitions of ethics or metrics of algorithmic fairness. It requires shifting power from the hands of a few to the masses of people.

@Data4BlackLives d4bl.org #PAML2020