It is conventionally argued that because an artificially-intelligent (AI) system acts autonomously, its makers cannot easily be held liable should the system's actions harm. Since the system cannot be liable on its own account either, existing laws expose victims to accountability gaps and need to be reformed. Recent legal instruments have nonetheless established obligations against AI developers and providers. Drawing on attribution theory, this paper examines how these seemingly opposing positions are shaped by the ways in which AI systems are conceptualised. Specifically, folk dispositionism underpins conventional legal discourse on AI liability, personality, publications, and inventions and leads us towards problematic legal outcomes. Examining the technology and terminology driving contemporary AI systems, the paper contends that AI systems are better conceptualised instead as situational characters whose actions remain constrained by their programming. Properly viewing AI systems as such illuminates how existing legal doctrines could be sensibly applied to AI and reinforces emerging calls for placing greater scrutiny on the broader AI ecosystem.

Although subordinate's perception of authoritarian leadership is viewed derogatively in the leadership literature, limited studies still claim that it can positively influence subordinate's performance. Drawing from the attribution theory, we hypothesize and demonstrate that subordinate's perception of authoritarian leadership can have a positive, indirect effect on subordinate task performance through the subordinate-attributed performance promotion motive, but only when the subordinate's perception of leader's expert power is high. We found support for our hypothesized model using multisource data collected from 246 subordinates and 76 supervisors from 11 different private sector organizations in Pakistan.

Using an original demographically representative survey, we estimate the determinants of public support for a set of supportive and punitive policies to combat the opioid epidemic among a sample of 2,131 Americans. Our findings indicate that individuals who attribute blame for the epidemic to the personal choices of individuals, conservatives and those high in racial resentment are consistently more likely to support punitive policies to combat the opioid epidemic and less likely to favour policies to support individuals with substance use disorders. Individuals who have a personal connection to someone struggling with opioid use disorder favour policies to support such individuals but have nuanced attitudes towards punitive policies. Importantly, we find overwhelming support for all supportive policies except supervised injection sites, while roughly 50% of our sample supported the set of punitive policy choices. Our research represents a significant step forward toward understanding public opinion about the opioid epidemic and policies to combat it.

The justice literature has coalesced around the notion that actors (e.g., supervisors) tend to utilize the norm of equity for resource allocation decisions because it is generally considered most fair when employees who contribute more to the organization receive more resources. Yet, actors might sometimes utilize a need norm to allocate resources to those most in need. Studies that have addressed need-based resource allocations have assumed a relatively straightforward conceptualization of need. However, research from related areas suggests that multiple characteristics of the need itself could trigger actors’ use of a need norm to allocate resources. We advance a theoretical framework that outlines various need characteristics that drive actors’ use of a need norm. The framework draws on the processes outlined in attribution theory and integrates those with the content domains addressed in fairness theory. A discussion of the implications for justice, attribution, and fairness theory research follows.

Guided by Weiner’s (1993, Am Psychol 48:957-964) attribution theory of social motivation, we examined perceptions of exonerees. Specifically, we examined whether biased police procedures impacted perceptions of responsibility, emotional reactions, and willingness to assist exonerees. Participants read a vignette involving an exoneration due to either a false confession or an eyewitness misidentification with police practices (biased vs. unbiased) manipulated across participants. Findings corroborate that participants hold more negative views of exonerees who falsely confess than exonerees who were mistakenly identified by eyewitnesses. Moreover, when police bias was high, participants were angrier at the police and less likely to perceive the exoneree as responsible for the wrongful conviction—especially when false confessions were involved. The findings are discussed in light of Weiner’s social motivation theory, and in regards to improving attitudes towards individuals who have been wrongly convicted.

This target article presents a new computational theory of explanatory coherence that applies to the acceptance and rejection of scientific hypotheses as well as to reasoning in everyday life. The theory consists of seven principles that establish relations of local coherence between a hypothesis and other propositions. A hypothesis coheres with propositions that it explains, or that explain it, or that participate with it in explaining other propositions, or that offer analogous explanations. Propositions are incoherent with each other if they are contradictory. Propositions that describe the results of observation have a degree of acceptability on their own. An explanatory hypothesis is accepted if it coheres better overall than its competitors. The power of the seven principles is shown by their implementation in a connectionist program called ECHO, which treats hypothesis evaluation as a constraint satisfaction problem. Inputs about the explanatory relations are used to create a network of units representing propositions, while coherence and incoherence relations are encoded by excitatory and inhibitory links. ECHO provides an algorithm for smoothly integrating theory evaluation based on considerations of explanatory breadth, simplicity, and analogy. It has been applied to such important scientific cases as Lavoisier's argument for oxygen against the phlogiston theory and Darwin's argument for evolution against creationism, and also to cases of legal reasoning. The theory of explanatory coherence has implications for artificial intelligence, psychology, and philosophy.