Survey on <scp>AI</scp> Ethics: A Socio‐Technical Perspective

Abstract The past decade has observed a significant advancement in AI, with deep learning‐based models being deployed in diverse scenarios, including safety‐critical applications. As these AI systems become deeply embedded in our societal infrastructure, the repercussions of their decisions and actions have significant consequences, making the ethical implications of AI deployment highly relevant and essential. The ethical concerns associated with AI are multifaceted, including challenging issues of fairness, privacy and data protection, responsibility and accountability, safety and robustness, transparency and explainability, and environmental impact. These principles together form the foundations of ethical AI considerations that concern every stakeholder in the AI system lifecycle. In light of the present ethical and future x‐risk concerns, governments have shown increasing interest in establishing guidelines for the ethical deployment of AI. This work unifies the current and future ethical concerns of deploying AI into society. While we acknowledge and appreciate the technical surveys for each of the ethical principles concerned, in this paper, we aim to provide a comprehensive overview that not only addresses each principle from a technical point of view but also discusses them from a social perspective.

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Publication Details

Journal
Computational Intelligence
Published
2025-11-02
DOI
https://doi.org/10.1111/coin.70149
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

Survey on AI Ethics: A Socio‐Technical Perspective

Meghana Bhange, Samira Ebrahimi Kahou, Patrik Joslin Kenfack, Ivaxi Sheth et al.
Computational Intelligence
Adversarial Robustness in Machine Learning
article

Survey on AI Ethics: A Socio‐Technical Perspective

Meghana Bhange, Samira Ebrahimi Kahou, Patrik Joslin Kenfack, Ivaxi Sheth, Dave Mbiazi, Maryam Babaei
article en

Abstract

Abstract The past decade has observed a significant advancement in AI, with deep learning‐based models being deployed in diverse scenarios, including safety‐critical applications. As these AI systems become deeply embedded in our societal infrastructure, the repercussions of their decisions and actions have significant consequences, making the ethical implications of AI deployment highly relevant and essential. The ethical concerns associated with AI are multifaceted, including challenging issues of fairness, privacy and data protection, responsibility and accountability, safety and robustness, transparency and explainability, and environmental impact. These principles together form the foundations of ethical AI considerations that concern every stakeholder in the AI system lifecycle. In light of the present ethical and future x‐risk concerns, governments have shown increasing interest in establishing guidelines for the ethical deployment of AI. This work unifies the current and future ethical concerns of deploying AI into society. While we acknowledge and appreciate the technical surveys for each of the ethical principles concerned, in this paper, we aim to provide a comprehensive overview that not only addresses each principle from a technical point of view but also discusses them from a social perspective.

Computational IntelligenceVol. 41(6)
University of Calgary (CA), Chartered Institute for Archaeologists (GB), Canadian Institute for International Peace and Security (CA), Helmholtz Center for Information Security (DE), Mila - Quebec Artificial Intelligence Institute (CA), Polytechnique Montréal (CA)
Openalex Percentile: Top 83%
Adversarial Robustness in Machine Learning
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