Supporters of algorithmic reparation suggest taking lessons from curation professionals such as librarians, who’ve had to consider how to ethically collect data about people and what should be included in libraries. They propose considering not just whether the performance of an AI model is deemed fair or good but whether it shifts power.
The suggestions echo earlier recommendations by former Google AI researcher Timnit Gebru, who in a 2019 paper encouraged machine learning practitioners to consider how archivists and library sciences dealt with issues involving ethics, inclusivity, and power. Gebru says Google fired her in late 2020, and recently launched a distributed AI research center. A critical analysis concluded that Google subjected Gebru to a pattern of abuse historically aimed at Black women in professional environments. Authors of that analysis also urged computer scientists to look for patterns in history and society in addition to data.
Earlier this year, five US senators urged Google to hire an independent auditor to evaluate the impact of racism on Google’s products and workplace. Google did not respond to the letter.
In 2019, four Google AI researchers argued the field of responsible AI needs critical race theory because most work in the field doesn’t account
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