Attention, Moral Skill, and Algorithmic Recommendation

Philosophical Studies 182 (1) (2024)
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Abstract

Recommender systems are artificial intelligence technologies, deployed by online platforms, that model our individual preferences and direct our attention to content we’re likely to engage with. As the digital world has become increasingly saturated with information, we’ve become ever more reliant on these tools to efficiently allocate our attention. And our reliance on algorithmic recommendation may, in turn, reshape us as moral agents. While recommender systems could in principle enhance our moral agency by enabling us to cut through the information saturation of the internet and focus on things that matter, as they’re currently designed and implemented they’re apt to interfere with our ability to attend appropriately to morally relevant factors. In order to analyze the distinctive moral problems algorithmic recommendation poses, we develop a framework for the ethics of attention and an account of judicious attention allocation as a moral skill. We then discuss empirical evidence suggesting that attentional moral skill can be thwarted and undermined in various ways by algorithmic recommendation and related affordances of online platforms, as well as economic and technical considerations that support this concern. Finally, we consider how emerging technologies might overcome the problems we identify.

Other Versions

reprint Schuster, Nick; Lazar, Seth (2025) "Attention, moral skill, and algorithmic recommendation". Philosophical Studies 182(1):159-184

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Author Profiles

Seth Lazar
Australian National University
Nick Schuster
Australian National University

References found in this work

Love as valuing a relationship.Niko Kolodny - 2003 - Philosophical Review 112 (2):135-189.
The Rationality of Emotion.Ronald de Sousa, Jing-Song Ma & Vincent Shen - 1987 - Philosophy and Culture 32 (10):35-66.
Experts and Deviants: The Story of Agentive Control.Wayne Wu - 2016 - Philosophy and Phenomenological Research 93 (1):101-26.

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