Ethical data curation for AI: An approach based on feminist epistemology and critical theories of race

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Leavy, Susan
Siapera, Eugenia
O'Sullivan, Barry
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Association for Computing Machinery
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The potential for bias embedded in data to lead to the perpetuation of social injustice though Artificial Intelligence (AI) necessitates an urgent reform of data curation practices for AI systems, especially those based on machine learning. Without appropriate ethical and regulatory frameworks there is a risk that decades of advances in human rights and civil liberties may be undermined. This paper proposes an approach to data curation for AI, grounded in feminist epistemology and informed by critical theories of race and feminist principles. The objective of this approach is to support critical evaluation of the social dynamics of power embedded in data for AI systems. We propose a set of fundamental guiding principles for ethical data curation that address the social construction of knowledge, call for inclusion of subjugated and new forms of knowledge, support critical evaluation of theoretical concepts within data and recognise the reflexive nature of knowledge. In developing this ethical framework for data curation, we aim to contribute to a virtue ethics for AI and ensure protection of fundamental and human rights.
Critical theories of race , Data curation , Ethical AI , Feminist theory
Leavy, S., Siapera, E. and O'Sullivan, B. (2021) 'Ethical Data Curation for AI: An Approach based on Feminist Epistemology and Critical Theories of Race', Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 19-21 May, Virtual Event, USA: Association for Computing Machinery, pp. 695–703. doi: 10.1145/3461702.3462598