Causal Framework of Artificial Autonomous Agent Responsibility

Franklin, Matija; Ashton, Hal; Awad, Edmond; Lagnado, David A.

doi:10.1145/3514094.3534140

Cited by 19 publications

(6 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here we have argued that more intelligent, autonomous, and thus, unpredictable social robots exist today. People are willing to attribute responsibility to such robots for their mistakes Awad et al, 2020;Franklin, Ashton, Awad, & Lagnado, 2022). Further, for more anthropomorphized social robots, research suggests that people are even willing to attribute experiential mental states (Fiala, Arico, & Nichols, 2014).…”

Section: Introductionmentioning

confidence: 99%

Social robots as depictions of social agents

Clark¹,

Fischer²

2022

Behav Brain Sci

View full text Add to dashboard Cite

Social robots serve people as tutors, caretakers, receptionists, companions, and other social agents. People know that the robots are mechanical artifacts, yet they interact with them as if they were actual agents. How is this possible? The proposal here is that people construe social robots not as social agents per se, but as depictions of social agents. They interpret them much as they interpret ventriloquist dummies, hand puppets, virtual assistants, and other interactive depictions of people and animals. Depictions as a class consist of three physical scenes with part-by-part mappings between them: (a) a base scene (the raw physical artifact), (b) the depiction proper (the artifact construed as a depiction), and (c) the scene depicted (the scene people are to imagine). With social robots, evidence shows, people form the same three scenes plus mappings: They perceive the raw machinery of a robot, construe it as a depiction of a character, and, using the depiction as a guide, engage in the pretense that they are interacting with the character depicted. So, with social robots, people also recognize three classes of agents—the characters depicted, the intended recipients of the depictions (those who view or interact with the robots), and the authorities responsible for the robots (the designers, makers, and owners). Construing social robots as depictions, we argue, accounts for many phenomena not covered by alternative models.

show abstract

Section: Introductionmentioning

confidence: 99%

Social robots as depictions of social agents

Clark¹,

Fischer²

2022

Behav Brain Sci

View full text Add to dashboard Cite

show abstract

“…It may seem bizarre, from a rational perspective, to be angry at a machine, to hold it responsible, or to blame it for the outcome of its decision-our anger means nothing to machines, nor our punishments. But from a psychological perspective, people do seem to experience toward machines the same manifold of negative reactions they experience toward humans, perhaps because the machines are perceived as autonomous enough to warrant these reactions (Bigman et al 2019, Epstein et al 2020, Franklin et al 2022.…”

Section: Implicit Moral Machinesmentioning

confidence: 99%

The Moral Psychology of Artificial Intelligence

Bonnefon,

Rahwan,

Shariff

2024

Annu. Rev. Psychol.

View full text Add to dashboard Cite

Moral psychology was shaped around three categories of agents and patients: humans, other animals, and supernatural beings. Rapid progress in artificial intelligence has introduced a fourth category for our moral psychology to deal with: intelligent machines. Machines can perform as moral agents, making decisions that affect the outcomes of human patients or solving moral dilemmas without human supervision. Machines can be perceived as moral patients, whose outcomes can be affected by human decisions, with important consequences for human–machine cooperation. Machines can be moral proxies that human agents and patients send as their delegates to moral interactions or use as a disguise in these interactions. Here we review the experimental literature on machines as moral agents, moral patients, and moral proxies, with a focus on recent findings and the open questions that they suggest. Expected final online publication date for the Annual Review of Psychology, Volume 75 is January 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

show abstract

“…ical domain, they have shown that the CSM predicts people's judgments about the extent that a decision of a psychological agent caused an outcome based on counterfactual simulations where that agent has made a different decision (Wu et al, 2022). However, in the context of responsibility attribution, the shift of focus from physical objects to agents introduces additional complexity, since an agent's actions are conditioned on their epistemic state (Beckers, 2023;Franklin et al, 2022;Halpern & Kleiman-Weiner, 2018;Kirfel & Lagnado, 2021). To explore this further, Wu et al (2023) have experimented with a gridworld environment where an agent is trying to achieve an outcome in the presence of a second (potentially adversarial) agent.…”

Section: Responsibility and Counterfactual Reasoningmentioning

confidence: 99%

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

Tsirtsis,

Rodriguez,

Gerstenberg

2024

Preprint

View full text Add to dashboard Cite

When a human and an AI agent collaborate to complete a task and something goes wrong, who is responsible? Prior work has developed theories to describe how people assign responsibility to individuals in teams. However, there has been little work studying the cognitive processes that underlie responsibility judgments in human-AI collaborations, especially for tasks comprising a sequence of interdependent actions. In this work, we take a step towards filling this gap. Using semi-autonomous driving as a paradigm, we develop an environment that simulates stylized cases of human-AI collaboration using a generative model of agent behavior. We propose a model of responsibility that considers how unexpected an agent’s action was, and what would have happened had they acted differently. We test the model’s predictions empirically and find that in addition to action expectations and counterfactual considerations, participants’ responsibility judgments are also affected by how much each agent actually contributed to the outcome.

show abstract

Causal Framework of Artificial Autonomous Agent Responsibility

Cited by 19 publications

References 48 publications

Social robots as depictions of social agents

Social robots as depictions of social agents

The Moral Psychology of Artificial Intelligence

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

Contact Info

Product

Resources

About