The Ethics of AI Ethics: An Evaluation of Guidelines

By Thilo Hagendorff. Minds and Machines, 2019

1,599 citations recorded by the source corpus.

Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compares 22 guidelines, highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems—and how the effectiveness in the demands of AI ethics can be improved.

Subfields: Artificial Intelligence, Health Informatics, Safety Research

View Paper: https://www.semanticscholar.org/paper/11159bdb213aaa243916f42f576396d483ba474b

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Citations in the corpus

Design Principles for Generative AI Applications (2024, methodology): “We held two workshops with two diferent teams (Table 4) to evaluate the design principles and strategies in practice.”
Trust in AI and Its Role in the Acceptance of AI Technologies (2022, background): “Their guidelines commonly recommend that trustworthy AI should be built into the system by design (Hagendorff, 2020; Jobin et al., 2019).”
Transitioning to Human Interaction with AI Systems: New Challenges and Opportunities for HCI Professionals to Enable Human-Centered AI (2021, background): “…a lot of attentions in research and application, and multiple sets of ethical guidelines from various organizations around the world are currently available, large high-tech companies have published internal ethical guidelines for development of AI systems (e.g., Hagendorff, 2020; IEEE, 2019).”
GPT-3 and InstructGPT: technological dystopianism, utopianism, and “Contextual” perspectives in AI ethics and industry (2022, background): “…is also beneficial when considering that the key responsible agent is not a machine, but a human faced with abstract ethical guidelines, economic incentives, job dissatisfaction, or an egoistic work environment, all of which can work to the detriment of ethical practices (Hagendorff [28], p. 109).”
Principles alone cannot guarantee ethical AI (2019, neutral): “Equivalent fiduciary relationships and complementary governance mechanisms do not exist for private sector AI developers 5 .”
From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices (2019, methodology): “The first and simpler is that the ‘problem’ of ‘interpreting’ an algorithmic decision seems tractable from a mathematical standpoint, so the principle of explicability has come to be seen as the most suitable for a technical fix (Hagendorff 2019).”
Artificial Intelligence (AI) Student Assistants in the Classroom: Designing Chatbots to Support Student Success (2022, background): “…(AI) has provided promising opportunities for addressing important problems in education and society (Cowls et al., 2021; Tomašev et al., 2020) while also provoking considerations about the unintended consequences and risks of AI usage (Hagendorff, 2020; Peters et al., 2020; Atkins et al., 2021).”
The uselessness of AI ethics (2022, methodology): “After surveying 22 sets of guidelines, Hagendorff [28] concludes that AI ethics is failing on many levels; they lack any enforcement mechanisms and their values are easily over-written by economic incentives, often becoming little more than marketing devices.”
Trust in AI: progress, challenges, and future directions (2024, neutral): “For a comprehensive review of AI ethics guidelines, the practical implementation of AI and ethics, and advancements in AI ethics, interested readers are referred to (Hagendorff, 2020).”
Trustworthy artificial intelligence (2020, background): “…and AI ethics, which focus on discussing foundational ethical themes (e.g., general ethics frameworks) (Floridi 2019; Floridi and Cowls 2019; Floridi et al. 2018; Hagendorff 2020) and how to embed values that promote wellbeing into AI at the design and development stages (de Swarte et al. 2019).”
AI Ethics - Too Principled to Fail? (2019, neutral): “5 AI developers do not commit to ‘public service’, which in other professions requires practitioners to uphold public interests in the face of competing business or managerial interests.”
ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports (2022, background): “Given that non-maleficence is a major principle of medical ethics, also in the context of AI applications [18, 19], we do see a need for further adaption of ChatGPT and similar LLMs to the medical field, in order to mitigate the risk for harmful errors.”