The heuristics gap in AI ethics: Impact on green AI policies and beyond

This article analyses the negative impact of heuristic biases on the main goals of AI ethics. These biases are found to hinder the identification of ethical issues in AI, the development of related ethical policies, and their application. This pervasive impact has been mostly neglected, giving rise...

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Main Author: Guglielmo Tamburrini
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:Journal of Responsible Technology
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2666659624000301
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author Guglielmo Tamburrini
author_facet Guglielmo Tamburrini
author_sort Guglielmo Tamburrini
collection DOAJ
description This article analyses the negative impact of heuristic biases on the main goals of AI ethics. These biases are found to hinder the identification of ethical issues in AI, the development of related ethical policies, and their application. This pervasive impact has been mostly neglected, giving rise to what is called here the heuristics gap in AI ethics. This heuristics gap is illustrated using the AI carbon footprint problem as an exemplary case. Psychological work on biases hampering climate warming mitigation actions is specialized to this problem, and novel extensions are proposed by considering heuristic mentalization strategies that one uses to design and interact with AI systems. To mitigate the effects of this heuristics gap, interventions on the design of ethical policies and suitable incentives for AI stakeholders are suggested. Finally, a checklist of questions helping one to investigate systematically this heuristics gap throughout the AI ethics pipeline is provided.
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spelling doaj-art-0a3814fdbbba40d1900c6207e8650dc52025-08-20T03:00:07ZengElsevierJournal of Responsible Technology2666-65962025-03-012110010410.1016/j.jrt.2024.100104The heuristics gap in AI ethics: Impact on green AI policies and beyondGuglielmo Tamburrini0DIETI – Dept of Electrical Engineering and Information Technology, Università di Napoli Federico II, Via Claudio 21, 80125 Napoli, ItalyThis article analyses the negative impact of heuristic biases on the main goals of AI ethics. These biases are found to hinder the identification of ethical issues in AI, the development of related ethical policies, and their application. This pervasive impact has been mostly neglected, giving rise to what is called here the heuristics gap in AI ethics. This heuristics gap is illustrated using the AI carbon footprint problem as an exemplary case. Psychological work on biases hampering climate warming mitigation actions is specialized to this problem, and novel extensions are proposed by considering heuristic mentalization strategies that one uses to design and interact with AI systems. To mitigate the effects of this heuristics gap, interventions on the design of ethical policies and suitable incentives for AI stakeholders are suggested. Finally, a checklist of questions helping one to investigate systematically this heuristics gap throughout the AI ethics pipeline is provided.http://www.sciencedirect.com/science/article/pii/S2666659624000301AI ethicsCognitive heuristicsDecision-making biasMentalization biasGreen AI
spellingShingle Guglielmo Tamburrini
The heuristics gap in AI ethics: Impact on green AI policies and beyond
Journal of Responsible Technology
AI ethics
Cognitive heuristics
Decision-making bias
Mentalization bias
Green AI
title The heuristics gap in AI ethics: Impact on green AI policies and beyond
title_full The heuristics gap in AI ethics: Impact on green AI policies and beyond
title_fullStr The heuristics gap in AI ethics: Impact on green AI policies and beyond
title_full_unstemmed The heuristics gap in AI ethics: Impact on green AI policies and beyond
title_short The heuristics gap in AI ethics: Impact on green AI policies and beyond
title_sort heuristics gap in ai ethics impact on green ai policies and beyond
topic AI ethics
Cognitive heuristics
Decision-making bias
Mentalization bias
Green AI
url http://www.sciencedirect.com/science/article/pii/S2666659624000301
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