Towards intellectual freedom in an AI Ethics Global Community
By Christoph Ebell, Ricardo A. Baeza-Yates, Richard Benjamins, Hengjin Cai, Mark Coeckelbergh, Tania Duarte, M. Hickok, Aurelie Jacquet, Angela Kim, J. Krijger and 7 more. 2021
32 citations recorded by the source corpus.
The recent incidents involving Dr. Timnit Gebru, Dr. Margaret Mitchell, and Google have triggered an important discussion emblematic of issues arising from the practice of AI Ethics research. We offer this paper and its bibliography as a resource to the global community of AI Ethics Researchers who argue for the protection and freedom of this research community. Corporate, as well as academic research settings, involve responsibility, duties, dissent, and conflicts of interest. This article is meant to provide a reference point at the beginning of this decade regarding matters of consensus and disagreement on how to enact AI Ethics for the good of our institutions, society, and individuals. We have herein identified issues that arise at the intersection of information technology, socially encoded behaviors, and biases, and individual researchers’ work and responsibilities. We revisit some of the most pressing problems with AI decision-making and examine the difficult relationships between corporate interests and the early years of AI Ethics research. We propose several possible actions we can take collectively to support researchers throughout the field of AI Ethics, especially those from marginalized groups who may experience even more barriers in speaking out and having their research amplified. We promote the global community of AI Ethics researchers and the evolution of standards accepted in our profession guiding a technological future that makes life better for all.
Subfields: Artificial Intelligence, Cognitive Neuroscience, Safety Research, AI Ethics, Fairness & Society
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Citations in the corpus
- The Gradient of Generative AI Release: Methods and Considerations (2023, background): “Google’s public position on responsible AI practices encourages in-house risk evaluation and mitigation, but conflicts of interest can result in internal critics being unable to share or publish findings [76] and dismissal [30].”
- Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance (2022, background): “For instance, Google researchers on the internal AI team were dismissed and blocked from publishing critiques on the large-scale language models [18, 27], which would later be revealed to be critical to the company’s future product roadmap [23, 85, 87].”