Year & volume: 2020.

Author(s): Daniel Schiff, Justin Biddle, Jason Borenstein, and Kelly Laas.

Abstract: Since 2016, more than 80 AI ethics documents – including codes, principles, frameworks, and policy strategies – have been produced by corporations, governments, and NGOs. In this paper, we examine three topics of importance related to our ongoing empirical study of ethics and policy issues in these emerging documents. First, we review possible challenges associated with the relative homogeneity of the documents’ creators. Second, we provide a novel typology of motivations to characterize both obvious and less obvious goals of the documents. Third, we discuss the varied impacts these documents may have on the AI governance landscape, including what factors are relevant to assessing whether a given document is likely to be successful in achieving its goals.

Doi: https://doi.org/10.1145/3375627.3375804

Year & volume: 2025 (Vol. 16).

Author(s): Xiangru Tang; Qiao Jin; Kunlun Zhu; Tongxin Yuan; Yichi Zhang; Wangchunshu Zhou; Meng Qu; Yilun Zhao; Jian Tang; Zhuosheng Zhang; Arman Cohan; Dov Greenbaum; Zhiyong Lu; Mark Gerstein.

Abstract: AI scientists powered by large language models have demonstrated substantial promise in autonomously conducting experiments and facilitating scientific discoveries across various disciplines. While their capabilities are promising, these agents also introduce novel vulnerabilities that require careful consideration for safety. However, there has been limited comprehensive exploration of these vulnerabilities. This perspective examines vulnerabilities in AI scientists, shedding light on potential risks associated with their misuse, and emphasizing the need for safety measures. We begin by providing an overview of the potential risks inherent to AI scientists, taking into account user intent, the specific scientific domain, and their potential impact on the external environment. Then, we explore the underlying causes of these vulnerabilities and provide a scoping review of the limited existing works. Based on our analysis, we propose a triadic framework involving human regulation, agent alignment, and an understanding of environmental feedback (agent regulation) to mitigate these identified risks. Furthermore, we highlight the limitations and challenges associated with safeguarding AI scientists and advocate for the development of improved models, robust benchmarks, and comprehensive regulations.

Doi: https://doi.org/10.1038/s41467-025-63913-1

Year & volume: 2025.

Author(s): M.Z. Naser.

Abstract: This paper examines the paradoxical decline in engagement with philosophy of science among engineers precisely when machine learning (ML) systems are increasingly performing complex epistemological functions in engineering practice. We identify how philosophical naivety, characterized by the uncritical adoption of reductive frameworks regarding consciousness, intelligence, and ethics, creates tangible organizational and technical liabilities. We then demonstrate how conceptual limitations in engineers’ philosophical foundations lead to three primary flaws: 1) ontological misclassification of system capabilities, 2) ethical blind spots in ML system design and application, and 3) inadequate epistemological approaches and hidden philosophical commitments for interpreting model outputs. Thus, we argue that renewed engagement with the philosophy of science is not merely academic but necessary for engineers to maintain epistemic authority and responsibility in an era where engineering judgment is increasingly delegated to or mediated by ML systems. In response, we propose a technical-philosophical framework integrating perspectives from philosophy of mind, ethics, epistemology, and engineering to address these shortcomings systematically.

Doi: https://doi.org/10.1007/s11229-025-05044-0

Year & volume: 2025 (Vol. 90).

Author(s): Angela O’Sullivan; Lilith Mace.

Abstract: Three philosophical accounts of risk dominate the contemporary literature. On the probabilistic account, risk has to do with the probability of a disvaluable event obtaining; on the modal account, it has to do with the modal closeness of that event obtaining; on the normic account, it has to do with the normalcy of that event obtaining. The debate between these accounts has proceeded via counterexample-trading, with each account having some cases it explains better than others, and some cases that it cannot explain at all. In this article, we attempt to break the impasse between the three accounts of risk through a shift in methodology. We investigate the concept of risk via the method of conceptual reverse-engineering, whereby a theorist reconstructs the need that a concept serves for a group of agents in order to illuminate the shape of the concept: its intension and extension. We suggest that risk functions to meet our need to make decisions that reduce disvalue under conditions of uncertainty. Our project makes plausible that risk is a pluralist concept: meeting this need requires that risk takes different forms in different contexts. But our pluralism is principled: each of these different forms are part of one and the same concept, that has a ‘core-to-periphery’ structure, where the form the concept takes in typical cases (at its ‘core’) explains the form it takes in less typical cases (at its ‘periphery’). We then apply our findings to epistemic risk, to resolve an ambiguity in how ‘epistemic risk’ is standardly understood.

Doi: https://doi.org/10.1007/s11229-025-05044-0

Year & volume: 2026 (Vol. 207).

Author(s): Alexander Williams Tolbert.

Abstract: AI has become a common assistant for making choices, from minor to major ones. It can inform our beliefs relevant to a decision by both helping us to find existing information and generating new information. But in what ways and to what extent is AI useful when making a rational decision? The present paper provides answers to this question for three different types of choices: easy choices, hard choices, and transformative choices. In easy choices, where the rational action is, in principle, straightforward, AI can make the decision-making process more efficient and accurate, increasing derived value (at least in the long-term). In hard choices, where options are on a par, AI can help us when we commit to an option by assisting us in the creation process of new will-based reasons. In transformative choices, where we cannot, even in principle, know by ourselves which option maximizes expected value, AI cannot fill the epistemic or metaphysical gap characteristic of such choices, and therefore cannot enable rational decision-making. Overall, the analysis shows that if the values of our options do not already allow us to determine the rational choice without AI, its assistance does not change that.

Doi: https://doi.org/10.1007/s11229-026-05528-7

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https://url.kr/gg9jl5

The Ethics of Artificial Intelligence: A Philosophical Introduction

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