Produced and counterfactual effort contribute to responsibility attributions in collaborative tasks

Abstract

How do people judge responsibility in collaborative tasks? Past work has proposed a number of metrics that people may use to attribute blame and credit to others, such as effort, competence, and force. Some theories consider only the produced effort or force (individuals are more responsible if they produce more effort or force), whereas others consider counterfactuals (individuals are more responsible if some alternative behavior on their or their collaborator's part could have altered the outcome). Across four experiments (N = 717), we found that participants’ judgments are best described by a model that considers both produced and counterfactual effort. This finding generalized to an independent validation data set (N = 99). Our results thus support a dual-factor theory of responsibility attribution in collaborative tasks.

Towards a computational model of responsibility judgments in sequential human-AI collaboration

“…Our work opens up many interesting avenues for future work. Since the actual contribution model best captured the participants' judgments about the AI, it would be interesting to explore the relative importance of actual and counterfactual contribution, as well as how this mixture differs when making judgments about humans and AI agents (Xiang et al, 2023). In our setting, the AI and the human agent differ mainly in terms of what they know.…”

“…Existing theories about the cognitive process of responsibility attribution have established strong ties with causality (Pearl, 2009) and counterfactual reasoning (Byrne, 2016;Kahneman et al, 1982;Roese, 1997). Humans tend to consider an object, event, action or agent as (causally) responsible for an outcome if they can mentally simulate an alternative reality where that outcome would have been different if the candidate cause had not existed or occurred in the first place (Beckers, 2023;Chockler & Halpern, 2004;Gerstenberg et al, 2018;Halpern & Kleiman-Weiner, 2018;Lagnado et al, 2013;Langenhoff et al, 2021;Triantafyllou et al, 2022;Wu & Gerstenberg, 2024;Wu et al, 2023;Xiang et al, 2023;Zultan et al, 2012). In that context, Gerstenberg et al (2021) have developed the counterfactual simulation model (CSM), a computational model that accurately predicts the extent to which people perceive an object (e.g., a moving billiard ball) as a cause of an observed outcome (e.g., potting another ball).…”

Counterfactual simulation in causal cognition

“…How much effort an agent exerted mattered less than their intentions. While effort is often highly diagnostic for intentions (107,12,127), the two can come apart in our setting, and when they do, intentions matters more. Even though the grid world setup is simple, it supports rich social interactions and inferences.…”

You should have checked! The importance of epistemic intentions in ascription of responsibility

“…People conceive counterfactuals not only when developing causal explanations 5,6 , but also when ascribing blame 7,8 and responsibility [9][10][11] . However, previous work that examined the role of counterfactual thinking in moral judgments focused mostly on agents' actions [10][11][12][13] and agents' epistemic states [14][15][16] . Less research emphasize on the role of counterfactual reasoning about agents' intentions 16,17 .…”