mlsanityData to 14 Oct 2026

Survey on AI Ethics: A Socio-technical Perspective

arXiv 2311.17228 ↗First posted 28 Nov 2023cs.CY cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 145 of its 314 references that are in the map. First posted on arXiv 28 Nov 2023.

Builds on

145 of 314 references.

References · 145

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50 most cited of 145 All 145 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024Safeguarding Large Language Models: A Surveycited by 78
  2. 2025Towards Fair In-Context Learning with Tabular Foundation Modelscited by 8
  3. 2025Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Modelscited by 2
  4. 2026ERTS: Adversarial Robustness Testing of Ethical AI via Semantic Perturbation in a Bounded Consequence Spacecited by 0