Unlike the “narrow AI” systems that TESCREALists lamented the field of AI was focused on, attempting to build something akin to an everything machine results in systems that are unscoped and therefore inherently unsafe, as one cannot design…Mehtab Khan and Alex Hanna, 2022. “The subjects and stages of AI dataset development: A framework for dataset accountability,” SSRN (13 September). doi: https://dx.doi.org/10.2139/ssrn.4217148, accessed 31 January 2024.
Raffi Khatchadouria…
Heidy Khlaaf
engineer · 2 mentions across 1 reading
In this course
Khlaaf appears in the course materials primarily through co-authored work on AI dataset accountability and the governance structures surrounding machine learning systems' development. Her contributions help frame how datasets become sites of contestation between technical design and ethical scrutiny, challenging the assumption that AI safety can be engineered at the model level alone. This work is central to the seminar's investigation of how power and control operate through data practices rather than just algorithmic architectures.
Mentioned in 1 reading
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