Position: Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis

Jessica Dai
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:9834-9845, 2024.

Abstract

What is agency, and why does it matter? In this work, we draw from the political science and philosophy literature and give two competing visions of what it means to be an (ethical) agent. The first view, which we term mechanistic, is commonly— and implicitly—assumed in AI research, yet it is a fundamentally limited means to understand the ethical characteristics of AI. Under the second view, which we term volitional, AI can no longer be considered an ethical agent. We discuss the implications of each of these views for two critical questions: first, what the ideal system “ought” to look like, and second, how accountability may be achieved. In light of this discussion, we ultimately argue that, in the context of ethically-significant behavior, AI should be viewed not as an agent but as the outcome of political processes.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-dai24a, title = {Position: Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis}, author = {Dai, Jessica}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {9834--9845}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/dai24a/dai24a.pdf}, url = {https://proceedings.mlr.press/v235/dai24a.html}, abstract = {What is agency, and why does it matter? In this work, we draw from the political science and philosophy literature and give two competing visions of what it means to be an (ethical) agent. The first view, which we term mechanistic, is commonly— and implicitly—assumed in AI research, yet it is a fundamentally limited means to understand the ethical characteristics of AI. Under the second view, which we term volitional, AI can no longer be considered an ethical agent. We discuss the implications of each of these views for two critical questions: first, what the ideal system “ought” to look like, and second, how accountability may be achieved. In light of this discussion, we ultimately argue that, in the context of ethically-significant behavior, AI should be viewed not as an agent but as the outcome of political processes.} }
Endnote
%0 Conference Paper %T Position: Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis %A Jessica Dai %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-dai24a %I PMLR %P 9834--9845 %U https://proceedings.mlr.press/v235/dai24a.html %V 235 %X What is agency, and why does it matter? In this work, we draw from the political science and philosophy literature and give two competing visions of what it means to be an (ethical) agent. The first view, which we term mechanistic, is commonly— and implicitly—assumed in AI research, yet it is a fundamentally limited means to understand the ethical characteristics of AI. Under the second view, which we term volitional, AI can no longer be considered an ethical agent. We discuss the implications of each of these views for two critical questions: first, what the ideal system “ought” to look like, and second, how accountability may be achieved. In light of this discussion, we ultimately argue that, in the context of ethically-significant behavior, AI should be viewed not as an agent but as the outcome of political processes.
APA
Dai, J.. (2024). Position: Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:9834-9845 Available from https://proceedings.mlr.press/v235/dai24a.html.

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