MORAL ORTHOSES: A NEW APPROACH TO HUMAN AND MACHINE ETHICS

Machines are increasingly involved in decisions with ethical implications, which require ethical explanations. Current machine learning algorithms are ethically inscrutable, but not in a way very different from human behavior. This article looks at the role of rationality and reasoning in traditional ethical thought and in artificial intelligence, emphasizing the need for some explainability of actions. It then explores Neil Lawrence's embodiment factor as an insightful way of looking at the differences between human and machine intelligence, connecting it to the theological understanding of embodiment, relationality, and personhood. Finally, it proposes the notion of artificial moral orthoses, which could provide ethical explanations for both artificial and human agents, as a more promising unifying approach to human and machine ethics.     

How prioritized is self-prioritization during stimulus processing?

Recent research has suggested that self-relevance automatically enhances stimulus processing (i.e., the self-prioritization effect). Notably, information associated with one’s self elicits faster responses than comparable material associated with other targets (e.g., friend, stranger). Challenging the assertion that self-prioritization is an obligatory process, here we hypothesized that self-relevance only facilitates performance when task sets draw attention to previously formed target-object associations. The results of two experiments were consistent with this viewpoint. Compared with arbitrary objects owned by a friend, those owned by the self were classified more rapidly when participants were required to report either the owner or identity of the items (i.e., semantic task set). In contrast, self-relevance failed to facilitate performance when participants judged the orientation of the stimuli (i.e., perceptual task set). These findings demonstrate the conditional automaticity of self-prioritization during stimulus processing.     

Age-related declines in context maintenance and semantic short-term memory

This study reports age-related declines in context maintenance (Braver et al., 2001) and semantic short-term memory (STM) and evidence for a relation between the two. A group of younger and older adults completed a context maintenance task (AX-CPT), a semantically oriented STM task (conceptual span), a phonologically oriented STM task (digit span), and a meaning integration task (semantic anomaly judgement). In the AX-CPT task, a target response is required to the probe letter “X” but only when it is preceded by the letter “A” (the context). Either three (short interference) or six distractor letters (long interference) were presented between the cue and the probe. Results indicated an age-related deficit in context maintenance. Age-related declines were also observed for conceptual span and semantic anomaly judgement but not for digit span. Context maintenance was correlated with conceptual span and semantic anomaly judgement but not with digit span. These correlations were largely mediated by age differences, which also explained variance that was unique to (and not shared among) context maintenance, conceptual span, and semantic anomaly judgement.     

Better explaining the benefits why AI? Analyzing the impact of explaining the benefits of AI-supported selection on applicant responses

Despite the increasing popularity of AI-supported selection tools, knowledge about the actions that can be taken by organizations to increase AI acceptance is still in its infancy, even though multiple studies point out that applicants react negatively to the implementation of AI-supported selection tools. Therefore, this study investigates ways to alter applicant reactions to AI-supported selection. Using a scenario-based between-subject design with participants from the working population (N = 200), we varied the information provided by the organization about the reasons for using an AI-supported selection process (no additional information vs. written information vs. video information) in comparison to a human selection process. Results show that the use of AI without information and with written information decreased perceived fairness, personableness perception, and increased emotional creepiness. In turn, perceived fairness, personableness perceptions, and emotional creepiness mediated the association between an AI-supported selection process, organizational attractiveness, and the intention to further proceed with the selection process. Moreover, results did not differ for applicants who were provided video explanations of the benefits of AI-supported selection tools and those who participated in an actual human selection process. Important implications for research and practice are discussed.