Preprint

Alvarado, Ramón (2022) AI as an Epistemic Technology. [Preprint]

Birch, Jonathan (2023) Medical AI, Inductive Risk, and the Communication of Uncertainty: The Case of Disorders of Consciousness. [Preprint]

Browning, Heather and Veit, Walter (2022) Longtermism and Animals. [Preprint]

Cirkovic, Milan M. (2020) Anthropocentrism and the roots of resistance to both human bioenhancement and space colonization. [Preprint]

Fleisher, Will (2021) What's Fair about Individual Fairness. [Preprint]

Jebari, Karim and Lundborg, Joakim (2019) Artificial superintelligence and its limits: why AlphaZero cannot become a general agent. [Preprint]

Johnson, Gabbrielle (2020) Algorithmic Bias: On the Implicit Biases of Social Technology. [Preprint]

Khosrowi, Donal and van Basshuysen, Philippe (2023) Making a Murderer –- How risk assessment tools may produce rather than predict criminal behavior. [Preprint]

LaCroix, Travis (2022) The Linguistic Blind Spot of Value-Aligned Agency, Natural and Artificial. [Preprint]

LaCroix, Travis (2022) Moral Dilemmas for Moral Machines. [Preprint]

Mizrahi, Moti (2020) How to Play the “Playing God” Card. [Preprint]

Peters, Uwe (2022) Algorithmic political bias in artificial intelligence systems. [Preprint]

Pruss, Dasha (2021) Mechanical Jurisprudence and Domain Distortion: How Predictive Algorithms Warp the Law. [Preprint]

Rabinowitz, Avi (2021) A Brain’s Self-identification as "Materialist" or “Non-Materialist” (dualist, panpsyschist, idealist etc) as an unwitting indication of its deep structure/wiring category rather than a statement of its philosophical stance. A survey associated with the article will identify relevant candidates for a brain- experiment. [Preprint]

Russo, Federica and Schliesser, Eric and Wagemans, Jean H.M. (2022) Connecting ethics and epistemology of AI. [Preprint]

Sullivan, Emily (2022) Inductive Risk, Understanding, and Opaque Machine Learning Models. [Preprint]

Symons, John and Alvarado, Ramón (2022) EPISTEMIC INJUSTICE AND DATA SCIENCE TECHNOLOGIES. [Preprint]

Veit, Walter and Browning, Heather (2023) Defending Sentientism. [Preprint]

Weinberger, Naftali (2022) Path-Specific Discrimination. [Preprint]

Published Article or Volume

Babic, Boris and Gerke, Sara and Evgeniou, Theodoros and Cohen, Glenn (2019) Algorithms on Regulatory Lockdown in Medicine. Science.

Gerke, Sara and Babic, Boris and Evgeniou, Theodoros and Cohen, Glenn (2020) The need for a system view to regulate artificial intelligence/machine learning-based software as medical device. Nature Digital Medicine.

Graves, Mark and Ratti, Emanuele (2022) Who Is a Good Data Scientist? A Reply to Curzer and Epstein. Philosophy & Technology, 35.

Heesen, Remco and Romeijn, Jan-Willem (2023) Measurement Invariance, Selection Invariance, and Fair Selection Revisited. Psychological Methods, 28 (3). pp. 687-690. ISSN 1082-989X

Kasirzadeh, Atoosa and Klein, Colin (2021) The Ethical Gravity Thesis: Marrian Levels and the Persistence of Bias in Automated Decision-making Systems. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society.

Kasirzadeh, Atoosa and Smart, Andrew (2021) The Use and Misuse of Counterfactuals in Ethical Machine Learning. FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency.

List, Christian (2021) Group Agency and Artificial Intelligence. Philosophy & Technology, Online.

Miller, Ryan (2023) Holding Large Language Models to Account. Proceedings of the AISB Convention 2023. pp. 7-14.

Ratti, Emanuele and Bezuidenhout, Louise (2020) What does it mean to embed ethics in data science? An integrative approach based on microethics and virtues. AI & Society.

Ratti, Emanuele and Graves, Mark (2021) Cultivating Moral Attention: A Virtue-oriented Approach to Responsible Data Science in Healthcare. Philosophy & Technology.

Ratti, Emanuele and Graves, Mark (2022) Explainable machine learning practices: opening another black box for reliable medical AI. AI and Ethics.

Räz, Tim (2022) Understanding risk with FOTRES? AI and Ethics.

Sullivan, Emily (2022) How Values Shape the Machine Learning Opacity Problem. Scientific Understanding and Representation (Eds) Insa Lawler, Kareem Khalifa & Elay Shech. pp. 306-322.