Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are “black boxes.” The initial response in the literature was a demand for “explainable AI.” However, recently, several authors have suggested that making AI more explainable or “interpretable” is likely to be at the cost of the accuracy of these systems and that prioritizing interpretability in medical AI may constitute a “lethal prejudice.” In this paper, we defend the value of interpretability in the context of the use of AI in medicine. Clinicians may prefer interpretable systems over more accurate black boxes, which in turn is sufficient to give designers of AI reason to prefer more interpretable systems in order to ensure that AI is adopted and its benefits realized. Moreover, clinicians may be justified in this preference. Achieving the downstream benefits from AI is critically dependent on how the outputs of these systems are interpreted by physicians and patients. A preference for the use of highly accurate black box AI systems, over less accurate but more interpretable systems, may itself constitute a form of lethal prejudice that may diminish the benefits of AI to—and perhaps even harm—patients.

Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are “black boxes.” The initial response in the literature was a demand for “explainable AI.” However, recently, several authors have suggested that making AI more explainable or “interpretable” is likely to be at the cost of the accuracy of these systems and that prioritizing interpretability in medical AI may constitute a “lethal prejudice.” In this article, we defend the value of interpretability in the context of the use of AI in medicine. Clinicians may prefer interpretable systems over more accurate black boxes, which in turn is sufficient to give designers of AI reason to prefer more interpretable systems in order to ensure that AI is adopted and its benefits realized. Moreover, clinicians may be justified in this preference. Achieving the downstream benefits from AI is critically dependent on how the outputs of these systems are interpreted by physicians and patients. A preference for the use of highly accurate black box AI systems, over less accurate but more interpretable systems, may itself constitute a form of lethal prejudice that may diminish the benefits of AI to—and perhaps even harm—patients.

The amount of data available to healthcare practitioners is growing, and the rapid increase in available patient data is becoming a problem for healthcare practitioners, as they are often unable to fully survey and process the data relevant for the treatment or care of a patient. Consequently, there are currently several efforts to develop systems that can aid healthcare practitioners with reading and processing patient data and, in this way, provide them with a better foundation for decision-making about the treatment and care of patients. There are also efforts to develop algorithms that provide suggestions for such decisions. However, the development of these systems and algorithms raises several concerns related to the privacy of patients, the patient–practitioner relationship, and the autonomy of healthcare practitioners. The aim of this article is to provide a foundation for understanding the ethical challenges related to the development of a specific form of data-processing systems, namely clinical algorithms.

In this special issue, we unpack law and finance entities and consider their co-construction, entanglement and interchanging relationship. Adopting a processual sociology lens, we aim to connect micro-technical devices and controversies to the macroscopic big picture of financialized capitalism. We combine analytical tools from pragmatic sociology, emphasizing how social reality and institutions are (re-)enacted through trials, with a dynamic and historicized sociology of the state and the juridical field. Four avenues illustrate our research program on the sociology of financial law. First, we focus on how this juridical space is co-produced by public and private forces, organizations and initiatives. Second, we look at how financial law displaces and endogenizes core regalian purposes traditionally associated with the state. Third, we show the forms of asymmetries that pervade law enforcement in financial cases. Fourth, we address how power intervenes in normal and exceptional times, such as financial crises. The legal and financial co-production of political regimes shapes economies and legitimate forms of social distribution.