Year and volume: 2025 (Vol. 92).

Author(s): Adrian K. Yee.

Abstract: Governments and social scientists are increasingly developing machine learning methods to automate the process of identifying terrorists in real time and predict future attacks. However, current operationalizations of “terrorist”’ in artificial intelligence are difficult to justify given three issues that remain neglected: insufficient construct legitimacy, insufficient criterion validity, and insufficient construct validity. I conclude that machine learning methods should be at most used for the identification of singular individuals deemed terrorists and not for identifying possible terrorists from some more general class, nor to predict terrorist attacks more broadly, given intolerably high risks that result from such approaches.

Doi: https://doi.org/10.1017/psa.2024.65

Year and volume: 2019 (Vol. 14).

Author(s): Cameron Buckner.

Abstract: Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been largely silent on this technology so far. This is remarkable, given that deep learning neural networks have blown past predicted upper limits on artificial intelligence performance—recognizing complex objects in natural photographs and defeating world champions in strategy games as complex as Go and chess—yet there remains no universally accepted explanation as to why they work so well. This article provides an introduction to these networks as well as an opinionated guidebook on the philosophical significance of their structure and achievements. It argues that deep learning neural networks differ importantly in their structure and mathematical properties from the shallower neural networks that were the subject of so much philosophical reflection in the 1980s and 1990s. The article then explores several different explanations for their success and ends by proposing three areas of inquiry that would benefit from future engagement by philosophers of mind and science.

Doi: https://doi.org/10.1111/phc3.12625

Year and volume: 2024 (Vol. 30).

Author(s): George Kwasi Barimah.

Abstract: In this paper, I develop and defend a moralized conception of epistemic trust in science against a particular kind of non-moral account defended by [Stephen] John (2015, 2018). I suggest that non-epistemic value considerations, non-epistemic norms of communication and affective trust properly characterize the relationship of epistemic trust between scientific experts and non-experts. I argue that it is through a moralized account of epistemic trust in science that we can make sense of the deep-seated moral undertones that are often at play when non-experts (dis)trust science.

Doi: https://doi.org/10.1007/s11948-024-00489-x

Year and volume: 2022 (Vol. 105).

Author(s): 성두현(Doohyun Sung).

Introduction: 최근에 우주바이오 연구개발(R&D)에 관한 논의가 뜨거워짐에 따라 유의미한 투자 및 규제 전략에 대한 요구가 커지고 있다. 본 글은 우주바이오의 미래에 관한 담론에 기여하기 위해 글쓴이의 연구 분야인 과학철학(philosophy of science)의 관점에서 우주바이오 정책의 세부 분야라고 할 수 있는 항공우주의학(aerospace medicine) 규제의 미래를 논하고자 한다.

본 글의 목적은 다음과 같다. 첫째, 본 글은 생명의학 연구자 및 우주바이오 관련 정책 의사결정자(policy decision-maker)를 대상으로 의학 연구(medical research) 및 의약품 규제(pharmaceutical regulation)에 관한 최신 과학철학(philosophy of science) 연구를 소개하고자 한다. 둘째, 본 글은 소개한 문헌을 바탕으로 미래 항공우주의학 규제의 도전 과제를 논의하고자 한다. 마지막으로 본 글은 서술한 도전 과제를 이해하고 논의하는데 있어서 유용한 자원을 소개하고자 한다.

본 글의 순서는 다음과 같다. 우선 본 글은 먼저 항공우주의학이라는 분야의 특성을 간략하게 논하고자 한다. 과학기술 분야의 모든 특성을 빠짐없이 논하는 것은 매우 난해하므로 본 글은 의학 연구 및 제약규제의 관점에서 중요하다고 판단되는 특성(연구방법론 및 치료방식) 위주로 논한다. 다음으로 본 글은 의료연구 및 제약규제에 관한 과학철학 문헌을 바탕으로 미래 항공우주의학 규제가 극복해야 하는 도전 과제를 소개한다. 마지막으로 본 글은 소개한 도전 과제에 관한 정책담론에 보탬이 될 수 있는 자원을 소개하고자 한다.

본 글은 논의를 시작하기에 앞서서 다음을 강조하고자 한다. 첫째, 본 글의 목적은 특정 정책안 혹은 제도 설계를 제시하는 것이 아니라 항공우주의학 규제를 위한 정책안을 제시하는데 있어서 고찰해야 하는 전략적 난제를 소개하는 것이다. 둘째, 본 글은 미국 식품의약국(FDA)으로 대표되는 미국 제약규제 맥락을 대상으로 한 과학철학 문헌을 논의의 바탕으로 삼는다. 이는 기존 과학철학 연구가 미국 제약규제를 주요 논의의 대상으로 삼아왔기 때문이다. 다만 본 글은 소개하는 전략적 도전 과제가 한국의 제약규제의 맥락에서도 고찰되어야 한다고 주장한다.

URL: https://www.bioin.or.kr/board.do?num=318730&cmd=view&bid=report

Year and volume: 2023 (Vol. 90).

Author(s): Eamon Duede.

Abstract: While philosophers have focused on epistemological and ethical challenges of using artificial intelligence (AI) in science, scientists have focused largely on opportunities. I argue that this disconnect between philosophical pessimism and scientific optimism is driven by failures to critically examine the practice of AI-infused science. To appreciate the epistemic justification for AI-powered breakthroughs, philosophers must analyze the role of AI as part of a wider process of discovery. I demonstrate the importance of this with two cases from the scientific literature, and show that epistemic opacity need not diminish AI’s capacity to lead scientists to significant and justifiable breakthroughs.

Doi: https://doi.org/10.1017/psa.2023.8

Year and volume: 2026 (Vol. 45).

Author(s): Nina Lauharatanahirun; Jason A. Aimone; Jeffrey Braxton Gately.

Abstract: Research has long documented how decision-making in risky environments differs between environments where the probabilities of uncertain outcomes are known and where the probabilities are unknown, the latter often referred to as “ambiguous” environments. Yet, there is a dearth of research examining how decisions may be affected by the source responsible for the distribution of uncertain outcomes. The source responsible for generating distributions of uncertain outcomes may be generated by another person (i.e., is social in nature) or by a nonsocial probabilistic mechanism. While a few studies examine how the source responsible for uncertain outcomes affects decisions when probabilities are known, the present study extends prior research to the realm of ambiguity by testing how the source of uncertainty affects both decisions when probabilities are fully known and when probabilities are partially unknown using a within-subjects experimental design. We calculate a general measurement of Social Risk Sensitivity to capture how individuals differ in their sensitivity across three uncertainty environments: risk with no ambiguity, risk with low ambiguity, and risk with high ambiguity. We find evidence showing strong correlations between Social Risk Sensitivity across all three levels of ambiguity. Our results corroborate the previous literature regarding ambiguity effects on decision-making behavior and extend prior work for the first time in this area by demonstrating that individual decisions are shaped by their individual sensitivity to the source from which uncertainty is derived.

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

Year and volume: 2026 (Vol. 45).

Author(s): Emma Soane.

Abstract: Organizational risk is the possibility of events preventing the achievement of objectives and disrupting organizational viability. Developing understanding of organizational risk is necessary to allow realization of opportunities and protection from harm. However, much existing theorizing focuses on either a higher level of analysis, for example, studies of organizational risk culture, or a lower level of analysis, such as studies of individual perception, personality, and risk-taking. One way to advance theorizing involves connecting both levels of analysis. These connections are central to a microfoundations perspective that suggests organizational phenomena can be understood by linking macrolevel contexts with microlevel contexts and actions. I draw on this perspective to develop a model of organizational risk and explain how cross-level processes connect macro- and microlevel concepts. I focus on the organizational psychology literature that encompasses higher and lower levels of analysis to select and examine relevant concepts. I explain how organizational cultures create contexts for individual risk-taking that are homogeneous when constraints are strong and directional or variable when constraints are weak and ambiguous. These behaviors aggregate within and across units to influence organizational risk. Individual risk-taking also influences organizational risk via autonomy and discretion. In developing the model, I show how theories of cross-level processes extend understanding of organizational risk. I discuss implications for advancing theorizing about organizational risk by encompassing its microfoundations and linking them with managerial actions and objectives. Future research could examine these mechanisms through empirical studies and shed light on how leaders influence processes and change organizational risk.

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

Biddle, Justin B. 2022. “On Predicting Recidivism: Epistemic Risk, Tradeoffs, and Values in Machine Learning.” Canadian Journal of Philosophy 52: 321-341. doi: https://doi.org/10.1017/can.2020.27.

Brown, Matthew J., and Jacob Stegenga. 2023. “The Validity of the Argument from Inductive Risk.” Canadian Journal of Philosophy 53: 187-190. doi: https://doi.org/10.1017/can.2023.37.

Harvard, Stephanie, and Eric Winsberg. 2022. “The Epistemic Risk in Representation.” Kennedy Institute of Ethics Journal 32: 1-31. doi: https://doi.org/10.1353/ken.2022.0001.

Harvard, Stephanie, and Eric Winsberg. 2026. “Representational Risk Revisited.” In The Routledge Handbook of Values and Science, ed. Kevin C. Elliott, and Ted Richards, 95-105. Routledge. doi: https://doi.org/10.4324/9781003469100-10.

Kukla, Rebecca, and Justin B. Biddle. 2017. “The Geography of Epistemic Risk.” In Exploring Inductive Risk: Case Studies of Values in Science, ed. Kevin C. Elliott, and Ted Richards, 215-238. Oxford University Press. doi: https://doi.org/10.1093/acprof:oso/9780190467715.003.0011. Note: Please visit Quill Kukla’s homepage for citation instructions.

Year: 2025.

Editor(s): Jan G. Michel (University of Düsseldorf).

Editorial status: closed for submissions.

URL: https://link.springer.com/collections/jdcaafgfjh

Year and Volume: 2024 (Vol. 54).

Author(s): Justin B. Biddle; John P. Nelson; Olajide E. Olugbade.

Abstract: Many AI development organizations advertise that they have offices of ethics that facilitate ethical AI. However, concerns have been raised that these offices are merely symbolic and do not actually promote ethics. We address the question of how we can know whether an organization is engaging in ethics washing in this way. We articulate an account of organizational power, and we argue that ethics offices that have power are not merely symbolic. Furthermore, we develop a framework for assessing whether an organization has an empowered ethics office—and, thus, is not ethics washing via a symbolic ethics office.

Doi: https://doi.org/10.1017/can.2025.9