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Related Concept Videos

Fundamental Attribution Error01:14

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In social interactions, individuals frequently seek to understand the motivations and causes behind others' behaviors. This fundamental aspect of social perception, known as attribution, plays a crucial role in shaping interpersonal relationships and guiding future actions. Attribution refers to the cognitive process through which people infer the reasons behind others' behaviors, allowing them to assess character traits, intentions, and situational influences.Attribution Theory and Its...
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Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...
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Attribution theory plays a crucial role in social psychology, helping to explain how individuals interpret the causes of behavior. One prominent model within this field is Harold Kelley's covariation theory, which provides a systematic approach to determining whether internal traits or external circumstances drive a person's actions. The model posits that individuals rely on three key types of information—consensus, consistency, and distinctiveness—to make these judgments.Consensus:...
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Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
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Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability.

Mark Coeckelbergh1

  • 1Department of Philosophy, University of Vienna (Universität Wien), Universitätsstrasse 7 (NIG), 1180, Vienna, Austria. mark.coeckelbergh@univie.ac.at.

Science and Engineering Ethics
|October 26, 2019
PubMed
Summary

Attributing responsibility for artificial intelligence (AI) actions is complex. This paper argues that explainability in AI should focus on the "patients" of AI decisions, not just the "agents," to justify accountability.

Keywords:
AnswerabilityArtificial intelligence (AI)ExplainabilityMoral agencyMoral patiencyProblem of many handsResponsibilityResponsibility attributionResponsibility conditionsTransparency

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Area of Science:

  • Philosophy of Technology
  • Artificial Intelligence Ethics
  • Legal and Moral Philosophy

Background:

  • The increasing use of artificial intelligence (AI) presents novel challenges for responsibility attribution.
  • Traditional frameworks for responsibility often assume human agents, creating difficulties when AI systems act autonomously.
  • Existing discussions primarily focus on the agency aspect of responsibility.

Purpose of the Study:

  • To explore the complexities of responsibility attribution in the context of AI technologies.
  • To identify and analyze the Aristotelian conditions for responsibility as applied to AI.
  • To propose a novel justification for AI explainability based on the concept of patiency.

Main Methods:

  • Analysis of responsibility attribution through the lens of two Aristotelian conditions: knowledge and control.
  • Identification of the
  • many hands
  • and
  • many things
  • problems in AI responsibility.
  • Examination of the temporal dimension and epistemic conditions (transparency, explainability) related to AI control.

Main Results:

  • The epistemic condition highlights significant issues with transparency and explainability in AI.
  • A novel
  • many things
  • problem is identified, distinct from the
  • many hands
  • problem.
  • The knowledge gap concerning AI agents is directly linked to the needs of responsibility recipients (patients).

Conclusions:

  • Responsibility attribution for AI requires considering both agents and patients.
  • Explainability in AI is crucial not only for understanding agency but also for empowering patients to demand reasons for AI actions.
  • A relational approach, emphasizing responsibility as answerability, provides a strong justification for AI explainability based on patiency.