Trust constitutes a fundamental strategy to deal with risks and uncertainty in complex societies. In line with the vast literature stressing the importance of trust in doctor–patient relationships, trust is therefore regularly suggested as a way of dealing with the risks of medical artificial intelligence (AI). Yet, this approach has come under charge from different angles. At least two lines of thought can be distinguished: (1) that trusting AI is conceptually confused, that is, that we cannot trust AI; and (2) (...) that it is also dangerous, that is, that we should not trust AI—particularly if the stakes are as high as they routinely are in medicine. In this paper, we aim to defend a notion of trust in the context of medical AI against both charges. To do so, we highlight the technically mediated intentions manifest in AI systems, rendering trust a conceptually plausible stance for dealing with them. Based on literature from human–robot interactions, psychology and sociology, we then propose a novel model to analyse notions of trust, distinguishing between three aspects: reliability, competence, and intentions. We discuss each aspect and make suggestions regarding how medical AI may become worthy of our trust. (shrink)

Bioethics, Volume 36, Issue 2, Page 154-161, February 2022.

Despite the immense societal importance of ethically designing artificial intelligence, little research on the public perceptions of ethical artificial intelligence principles exists. This becomes even more striking when considering that ethical artificial intelligence development has the aim to be human-centric and of benefit for the whole society. In this study, we investigate how ethical principles are weighted in comparison to each other. This is especially important, since simultaneously considering ethical principles is not only costly, but sometimes even impossible, as developers (...) must make specific trade-off decisions. In this paper, we give first answers on the relative importance of ethical principles given a specific use case—the use of artificial intelligence in tax fraud detection. The results of a large conjoint survey suggest that, by and large, German respondents evaluate the ethical principles as equally important. However, subsequent cluster analysis shows that different preference models for ethically designed systems exist among the German population. These clusters substantially differ not only in the preferred ethical principles but also in the importance levels of the principles themselves. We further describe how these groups are constituted in terms of sociodemographics as well as opinions on artificial intelligence. Societal implications, as well as design challenges, are discussed. (shrink)

Machine Learning (ML) is on the rise in medicine, promising improved diagnostic, therapeutic and prognostic clinical tools. While these technological innovations are bound to transform health care, they also bring new ethical concerns to the forefront. One particularly elusive challenge regards discriminatory algorithmic judgements based on biases inherent in the training data. A common line of reasoning distinguishes between justified differential treatments that mirror true disparities between socially salient groups, and unjustified biases which do not, leading to misdiagnosis and erroneous (...) treatment. In the curation of training data this strategy runs into severe problems though, since distinguishing between the two can be next to impossible. We thus plead for a pragmatist dealing with algorithmic bias in healthcare environments. By recurring to a recent reformulation of William James’s pragmatist understanding of truth, we recommend that, instead of aiming at a supposedly objective truth, outcome-based therapeutic usefulness should serve as the guiding principle for assessing ML applications in medicine. (shrink)

The aim of the study is to encourage a critical debate on the use of normality in the medical literature on DSD or intersex. For this purpose, a scoping review was conducted to identify and map the various ways in which “normal” is used in the medical literature on DSD between 2016 and 2020. We identified 75 studies, many of which were case studies highlighting rare cases of DSD, others, mainly retrospective observational studies, focused on improving diagnosis or treatment. The (...) most common use of the adjective normal was in association with phenotypic sex. Overall, appearance was the most commonly cited criteria to evaluate the normality of sex organs. More than 1/3 of the studies included also medical photographs of sex organs. This persistent use of normality in reference to phenotypic sex is worrisome given the long-term medicalization of intersex bodies in the name of a “normal” appearance or leading a “normal” life. Healthcare professionals should be more careful about the ethical implications of using photographs in publications given that many intersex persons describe their experience with medical photography as dehumanizing. (shrink)

Assistive systems based on Artificial Intelligence (AI) are bound to reshape decision-making in all areas of society. One of the most intricate challenges arising from their implementation in high-stakes environments such as medicine concerns their frequently unsatisfying levels of explainability, especially in the guise of the so-called black-box problem: highly successful models based on deep learning seem to be inherently opaque, resisting comprehensive explanations. This may explain why some scholars claim that research should focus on rendering AI systems understandable, rather (...) than explainable. Yet, there is a grave lack of agreement concerning these terms in much of the literature on AI. We argue that the seminal distinction made by the philosopher and physician Karl Jaspers between different types of explaining and understanding in psychopathology can be used to promote greater conceptual clarity in the context of Machine Learning (ML). Following Jaspers, we claim that explaining and understanding constitute multi-faceted epistemic approaches that should not be seen as mutually exclusive, but rather as complementary ones as in and of themselves they are necessarily limited. Drawing on the famous example of Watson for Oncology we highlight how Jaspers’ methodology translates to the case of medical AI. Classical considerations from the philosophy of psychiatry can therefore inform a debate at the centre of current AI ethics, which in turn may be crucial for a successful implementation of ethically and legally sound AI in medicine. (shrink)

Algorithmic decision-making increasingly shapes people's daily lives. Given that such autonomous systems can cause severe harm to individuals and social groups, fairness concerns have arisen. A human-centric approach demanded by scholars and policymakers requires considering people's fairness perceptions when designing and implementing algorithmic decision-making. We provide a comprehensive, systematic literature review synthesizing the existing empirical insights on perceptions of algorithmic fairness from 58 empirical studies spanning multiple domains and scientific disciplines. Through thorough coding, we systemize the current empirical literature along (...) four dimensions: (1) algorithmic predictors, (2) human predictors, (3) comparative effects (human decision-making vs. algorithmic decision-making), and (4) consequences of algorithmic decision-making. While we identify much heterogeneity around the theoretical concepts and empirical measurements of algorithmic fairness, the insights come almost exclusively from Western-democratic contexts. By advocating for more interdisciplinary research adopting a society-in-the-loop framework, we hope our work will contribute to fairer and more responsible algorithmic decision-making. (shrink)

The convergence of human and artificial intelligence is currently receiving considerable scholarly attention. Much debate about the resulting _Hybrid Minds_ focuses on the integration of artificial intelligence into the human brain through intelligent brain-computer interfaces as they enter clinical use. In this contribution we discuss a complementary development: the integration of a functional in vitro network of human neurons into an _in silico_ computing environment. To do so, we draw on a recent experiment reporting the creation of silico-biological intelligence as (...) a case study (Kagan et al., 2022b ). In this experiment, multielectrode arrays were plated with stem cell-derived human neurons, creating a system which the authors call _DishBrain_. By embedding the system into a virtual game-world, neural clusters were able to receive electrical input signals from the game-world and to respond appropriately with output signals from pre-assigned motor regions. Using this design, the authors demonstrate how the _DishBrain_ self-organises and successfully learns to play the computer game ‘Pong’, exhibiting ‘sentient’ and intelligent behaviour in its virtual environment. The creation of such hybrid, silico-biological intelligence raises numerous ethical challenges. Following the neuroscientific framework embraced by the authors themselves, we discuss the arising ethical challenges in the context of Karl Friston’s Free Energy Principle, focusing on the risk of creating synthetic phenomenology. Following the _DishBrain’s_ creator’s neuroscientific assumptions, we highlight how DishBrain’s design may risk bringing about artificial suffering and argue for a congruently cautious approach to such synthetic biological intelligence. (shrink)

Artificial intelligence (AI) plays a rapidly increasing role in clinical care. Many of these systems, for instance, deep learning-based applications using multilayered Artificial Neural Nets, exhibit epistemic opacity in the sense that they preclude comprehensive human understanding. In consequence, voices from industry, policymakers, and research have suggested trust as an attitude for engaging with clinical AI systems. Yet, in the philosophical and ethical literature on medical AI, the notion of trust remains fiercely debated. Trust skeptics hold that talking about trust (...) in nonhuman agents constitutes a category error and worry about the concept being misused for ethics washing. Proponents of trust have responded to these worries from various angles, disentangling different concepts and aspects of trust in AI, potentially organized in layers or dimensions. Given the substantial disagreements across these accounts of trust and the important worries about ethics washing, we embrace a diverging strategy here. Instead of aiming for a positive definition of the elements and nature of trust in AI, we proceed ex negativo, that is we look at cases where trust or distrust are misplaced. Comparing these instances with trust expedited in doctor–patient relationships, we systematize these instances and propose a taxonomy of both misplaced trust and distrust. By inverting the perspective and focusing on negative examples, we develop an account that provides useful ethical constraints for decisions in clinical as well as regulatory contexts and that highlights how we should not engage with medical AI. (shrink)

The increasing integration of Machine Learning (ML) techniques into clinical care, driven in particular by Deep Learning (DL) using Artificial Neural Nets (ANNs), promises to reshape medical practice on various levels and across multiple medical fields. Much recent literature examines the ethical consequences of employing ML within medical and psychiatric practice but the potential impact on psychiatric diagnostic systems has so far not been well-developed. In this article, we aim to explore the challenges that arise from the recent use of (...) ANNs for the old problems of psychiatric nosology. To enable an empirically supported critical reflection on the topic, we conducted semi-structured qualitative interviews with Swiss and German experts in computational psychiatry. Here, we report our findings structured around two themes, namely (1) the possibility of using ML for defining or refining of psychiatric classification, and (2) the desirability of employing ML for psychiatric nosology. We discuss these themes by relating them to recent debates about network theory for psychiatric nosology and show why empirical research in the field should critically reflect on its contribution to psychopathology research. In sum, we argue that beyond technical, regulatory, and ethical challenges, philosophical reflection is crucial to harness the potential of ML in psychiatry. (shrink)

“Decisions from experience” (DFE) refers to a body of work that emerged in research on behavioral decision making over the last decade. One of the major experimental paradigms employed to study experienced-based choice is the “sampling paradigm”, which serves as a model of decision making under limited knowledge about the statistical structure of the world. In this paradigm respondents are presented with two payoff distributions, which, in contrast to standard approaches in behavioral economics, are specified not in terms of explicit (...) outcome-probability information, but by the opportunity to sample outcomes from each distribution without economic consequences. Participants are encouraged to explore the distributions until they feel confident enough to decide from which they would prefer to draw from in a final trial involving real monetary payoffs. One commonly employed measure to characterize the behavior of participants in the sampling paradigm is the sample size, that is, the number of outcome draws which participants choose to obtain from each distribution prior to terminating sampling. A natural question that arises in this context concerns the “optimal” sample size, which could be used as a normative benchmark to evaluate human sampling behavior in DFE. In this theoretical manuscript, we relate the DFE sampling paradigm to the classical statistical decision theoretic literature and, under a probabilistic inference assumption, evaluate optimal sample sizes for decisions from experience. In our treatment we go beyond analytically established results by showing how the classical statistical decision theoretic framework can be used to derive optimal sample sizes under arbitrary, but numerically evaluable, constraints. Finally, we critically evaluate the value of deriving optimal sample sizes under this framework as testable predictions for the experimental study of sampling behavior in DFE. (shrink)

Germany is widely seen as a “dualized” economy driven by a powerful and stable “insider” coalition in the manufacturing sectors. In this article, that picture is challenged. An examination of the political economy of the outsider-friendly 2014 Minimum Wage Act, using public opinion data, document analysis, and qualitative interviews, shows how earlier dualizing reforms led to unintended negative feedback effects: First, public opinion reacted negatively to increasing inequality in the years preceding the introduction of the minimum wage. Second, a remarkable (...) shift is found among trade unions toward support of a minimum wage, even in manufacturing. Although the threat of low-wage competition and flexibilization did play a role, trade union solidarity was at least as important. Those endogenous dynamics came together in a self-undermining process unfolding over a relatively short period of time. Potential alternative explanations are explored, including classical partisan politics, party competition, and employer preferences. (shrink)

Wie ist der Freiwillige Verzicht auf Essen und Trinken und eine medizinische Begleitung dabei ethisch zu bewerten? Die ethische Bewertung des Freiwilligen Verzichts auf Essen und Trinken stellt Patienten und Angehörige, aber auch begleitende Ärzte und Pflegende vor erhebliche Schwierigkeiten. Basierend auf Ergebnissen eigener qualitativer Interviews mit Personen nach dem FVET ihrer Angehörigen legt dieser Artikel die bestehende Unklarheit und inhärente ethische Ambivalenz des Begriffs FVET frei, stellt aber in der Unterscheidung von FVET-Fällen – mit bzw. ohne terminale Erkrankung – (...) einen Weg zur Lösung der Frage nach der ethischen Bewertung des „Sterbefastens“ und seiner medizinischen Begleitung vor: Ethisch wird der Freiwillige Verzicht auf Essen und Trinken und seine medizinische Begleitung erst in der Analyse von Situation und Intention der Handelnden beurteilbar. Ausgehend vom Konzept eines frei gewollten Verzichts auf Essen und Trinken bei einsichts- und einwilligungsfähigen Personen mit dem Bewusstsein, damit ihr Leben zu verkürzen oder zu beenden, wird mit der Philosophie Thomas von Aquins argumentiert, dass eine ethische Bewertung die Begleitumstände und beabsichtigten Ziele miteinschließen muss, ebenso wie die Auswirkungen auf Angehörige und die Implikationen von und für ein Behandlungsteam. (shrink)

Critical management education typically assumes that management courses that emphasize critical reflection—that is, courses that critique problematic systems and structures, and ask students to dialogue about and actively reflect upon these critiques—will foster student transformation. In contrast, critical theory typically suggests that transformation requires praxis, that is, critical reflection plus practical action where students enact their new knowledge in their everyday lives. We empirically test these assumptions by measuring student transformation in management classes that emphasize critical reflection and in other (...) classes that emphasize praxis. We find no significant differences in three measures of student transformation in classes where only critical reflection is emphasized, but significant differences in all three measures when praxis is emphasized. In a content analysis of student praxis assignments, we find further evidence of the link between praxis and student transformation. Specifically, the majority of students value praxis, describe changes in their self-understanding/consciousness thanks to assignments that emphasize praxis, and plan to continue new behaviors that address concerns raised by critiques of problematic systems and structures. Finally, we find that the greater a student’s self-described effect of praxis, the greater that student’s transformation. Taken together, the results suggest that the practical application dimension of praxis is a key “missing ingredient” in the teaching of business ethics as students move from critically reflecting on ethical issues, to actually becoming transformed by their knowledge. (shrink)