Responsibility, second opinions and peer-disagreement: ethical and epistemological challenges of using AI in clinical diagnostic contexts

In this paper, we first classify different types of second opinions and evaluate the ethical and epistemological implications of providing those in a clinical context. Second, we discuss the issue of how artificial intelligent (AI) could replace the human cognitive labour of providing such second opinion and find that several AI reach the levels of accuracy and efficiency needed to clarify their use an urgent ethical issue. Third, we outline the normative conditions of how AI may be used as second opinion in clinical processes, weighing the benefits of its efficiency against concerns of responsibility attribution. Fourth, we provide a ‘rule of disagreement’ that fulfils these conditions while retaining some of the benefits of expanding the use of AI-based decision support systems (AI-DSS) in clinical contexts. This is because the rule of disagreement proposes to use AI as much as possible, but retain the ability to use human second opinions to resolve disagreements between AI and physician-in-charge. Fifth, we discuss some counterarguments.

Variantism about Responsibility

This chapter argues that experimental assessment of intuition pumps in the moral responsibility literature presses a dilemma on philosophically orthodox approaches to moral responsibility, which frequently maintain both invariantism and conservativism. Invariantist approaches maintain that there are exceptionlessly relevant criteria for responsibility attribution, while conservative approaches maintain that folk theories of responsibility are (defeasible, but substantial) constraints of philosophical theories of responsibility. However, the experimental record suggests that folk theory is conspicuously variantist (or pluralist) and evinces little commitment to exceptionlessly relevant attribution criteria. Therefore, invariantism and conservativism cannot be simultaneously maintained. Implications for theory choice and revision are discussed.

The Politics of Climate Disasters, Social Inequality, and Perceptions of Government Assistance

This chapter addresses a central research question of the politics of climate disaster: Who do citizens believe responsible for aftermath relief? The authors examine the issue of responsibility attribution in federal disaster assistance—and the related question of who voters believe deserves government disaster relief—against three devastating 2017 hurricanes, with a special focus on the impact of Hurricane Maria on Puerto Rico. The authors begin to answer the questions of responsibility and deservingness with survey data collected by them in a pilot study on the Island of Puerto Rico in 2019. They conclude by identifying fruitful links of comparative analysis between climate disaster politics and distributive and welfare politics.

A Game-Theoretic Account of Responsibility Allocation

When designing or analyzing multi-agent systems, a fundamental problem is responsibility ascription: to specify which agents are responsible for the joint outcome of their behaviors and to which extent. We model strategic multi-agent interaction as an extensive form game of imperfect information and define notions of forward (prospective) and backward (retrospective) responsibility. Forward responsibility identifies the responsibility of a group of agents for an outcome along all possible plays, whereas backward responsibility identifies the responsibility along a given play. We further distinguish between strategic and causal backward responsibility, where the former captures the epistemic knowledge of players along a play, while the latter formalizes which players – possibly unknowingly – caused the outcome. A formal connection between forward and backward notions is established in the case of perfect recall. We further ascribe quantitative responsibility through cooperative game theory. We show through a number of examples that our approach encompasses several prior formal accounts of responsibility attribution.

Validation of an Attributional and Distributive Justice Mediational Model on the Effects of Surface Acting on Emotional Exhaustion: An Experimental Study

Previous research has shown that surface acting—displaying an emotion that is dissonant with inner feelings—negatively impacts employees’ well-being. However, most studies have neglected the meaning that employees develop around emotional demands requiring surface acting. This study examined how employees’ responsibility attributions of client behavior demanding surface acting influence employees’ emotional exhaustion, and the mediational role of distributive justice in this relationship. Relying on Fairness Theory, it was expected that employees’ responsibility attributions of client behavior demanding emotion regulation would be related to their perceptions of distributive injustice during the service encounter, which in turn would mediate the effects of responsibility attribution on emotional exhaustion. In addition, drawing on the conservation of resources model, we contended that leader support would moderate the impact of distributive injustice on emotional exhaustion. Two scenario-based experiments were conducted. Study 1 (N = 187) manipulated the attribution of responsibility for emotional demands. The findings showed that distributive injustice and emotional exhaustion were higher when responsibility for the surface acting demands was attributed to the client. A bootstrapping mediational analysis confirmed employees’ attributions have an indirect effect on emotional exhaustion through distributive justice. Study 2 (N = 227) manipulated responsibility attribution and leader support. The leader support moderation effect was confirmed.

When Marriage Gets Hard: Intra-Coalition Conflict and Electoral Accountability

Combining individual-level with event-level data across 25 European countries and three sets of European Election Studies, this study examines the effect of conflict between parties in coalition government on electoral accountability and responsibility attribution. We find that conflict increases punishment for poor economic performance precisely because it helps clarify to voters parties’ actions and responsibilities while in office. The results indicate that under conditions of conflict, the punishment is equal for all coalition partners when they share responsibility for poor economic performance. When there is no conflict within a government, the effect of poor economic evaluations on vote choice is rather low, with slightly more punishment targeted to the prime minister’s party. These findings have important implications for our understanding of electoral accountability and political representation in coalition governments.