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#algorithmaversion — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #algorithmaversion, aggregated by home.social.

  1. Human–AI Evaluation and Gender Transparency: Application Decisions in Competitive Hiring
    docs.iza.org/dp18517.pdf
    #AI involvement deters applicants, particularly women, across both pure algorithmic and hybrid human-in-the-loop regimes. This effect is driven by non-competitive candidates; non-competitive women apply least despite receiving the strongest objective evaluations under AI assessment. Competitive men exhibit #overconfidence -driven selection, while competitive women remain resilient and well-calibrated under AI assessment. Notably, randomizing candidate #gender disclosure does not significantly impact application behavior in any evaluation category.
    #hiring #llms #algorithmaversion #LaborMarkets #jobtech #ExperimentalEcon

  2. Human–AI Evaluation and Gender Transparency: Application Decisions in Competitive Hiring
    docs.iza.org/dp18517.pdf
    #AI involvement deters applicants, particularly women, across both pure algorithmic and hybrid human-in-the-loop regimes. This effect is driven by non-competitive candidates; non-competitive women apply least despite receiving the strongest objective evaluations under AI assessment. Competitive men exhibit #overconfidence -driven selection, while competitive women remain resilient and well-calibrated under AI assessment. Notably, randomizing candidate #gender disclosure does not significantly impact application behavior in any evaluation category.
    #hiring #llms #algorithmaversion #LaborMarkets #jobtech #ExperimentalEcon

  3. Human–AI Evaluation and Gender Transparency: Application Decisions in Competitive Hiring
    docs.iza.org/dp18517.pdf
    #AI involvement deters applicants, particularly women, across both pure algorithmic and hybrid human-in-the-loop regimes. This effect is driven by non-competitive candidates; non-competitive women apply least despite receiving the strongest objective evaluations under AI assessment. Competitive men exhibit #overconfidence -driven selection, while competitive women remain resilient and well-calibrated under AI assessment. Notably, randomizing candidate #gender disclosure does not significantly impact application behavior in any evaluation category.
    #hiring #llms #algorithmaversion #LaborMarkets #jobtech #ExperimentalEcon

  4. Human–AI Evaluation and Gender Transparency: Application Decisions in Competitive Hiring
    docs.iza.org/dp18517.pdf
    #AI involvement deters applicants, particularly women, across both pure algorithmic and hybrid human-in-the-loop regimes. This effect is driven by non-competitive candidates; non-competitive women apply least despite receiving the strongest objective evaluations under AI assessment. Competitive men exhibit #overconfidence -driven selection, while competitive women remain resilient and well-calibrated under AI assessment. Notably, randomizing candidate #gender disclosure does not significantly impact application behavior in any evaluation category.
    #hiring #llms #algorithmaversion #LaborMarkets #jobtech #ExperimentalEcon

  5. Human–AI Evaluation and Gender Transparency: Application Decisions in Competitive Hiring
    docs.iza.org/dp18517.pdf
    #AI involvement deters applicants, particularly women, across both pure algorithmic and hybrid human-in-the-loop regimes. This effect is driven by non-competitive candidates; non-competitive women apply least despite receiving the strongest objective evaluations under AI assessment. Competitive men exhibit #overconfidence -driven selection, while competitive women remain resilient and well-calibrated under AI assessment. Notably, randomizing candidate #gender disclosure does not significantly impact application behavior in any evaluation category.
    #hiring #llms #algorithmaversion #LaborMarkets #jobtech #ExperimentalEcon

  6. Withdrawing life support was judged more harshly when medical #AI made the decision or recommendation than when human clinicians made it.

    When patients were conscious or AI seemed more competent, the #algorithmAversion #bias faded.

    doi.org/10.1016/j.cognition.20

    #medicine #bioethics

  7. Withdrawing life support was judged more harshly when medical #AI made the decision or recommendation than when human clinicians made it.

    When patients were conscious or AI seemed more competent, the #algorithmAversion #bias faded.

    doi.org/10.1016/j.cognition.20

    #medicine #bioethics

  8. Withdrawing life support was judged more harshly when medical #AI made the decision or recommendation than when human clinicians made it.

    When patients were conscious or AI seemed more competent, the #algorithmAversion #bias faded.

    doi.org/10.1016/j.cognition.20

    #medicine #bioethics

  9. Withdrawing life support was judged more harshly when medical #AI made the decision or recommendation than when human clinicians made it.

    When patients were conscious or AI seemed more competent, the #algorithmAversion #bias faded.

    doi.org/10.1016/j.cognition.20

    #medicine #bioethics

  10. Withdrawing life support was judged more harshly when medical #AI made the decision or recommendation than when human clinicians made it.

    When patients were conscious or AI seemed more competent, the #algorithmAversion #bias faded.

    doi.org/10.1016/j.cognition.20

    #medicine #bioethics

  11. #AlgorithmAversion is a tendency to judge errors in automated decisions more harshly than errors in human decisions.

    Telling people a decision is typically made by machines eliminated or even reversed the #bias.

    🔓 doi.org/10.1017/jdm.2025.8

    #AI #cogSci #xPhi #business #edu #tech

  12. #AlgorithmAversion is a tendency to judge errors in automated decisions more harshly than errors in human decisions.

    Telling people a decision is typically made by machines eliminated or even reversed the #bias.

    🔓 doi.org/10.1017/jdm.2025.8

    #AI #cogSci #xPhi #business #edu #tech

  13. #AlgorithmAversion is a tendency to judge errors in automated decisions more harshly than errors in human decisions.

    Telling people a decision is typically made by machines eliminated or even reversed the #bias.

    🔓 doi.org/10.1017/jdm.2025.8

    #AI #cogSci #xPhi #business #edu #tech

  14. #AlgorithmAversion is a tendency to judge errors in automated decisions more harshly than errors in human decisions.

    Telling people a decision is typically made by machines eliminated or even reversed the #bias.

    🔓 doi.org/10.1017/jdm.2025.8

    #AI #cogSci #xPhi #business #edu #tech

  15. #AlgorithmAversion is a tendency to judge errors in automated decisions more harshly than errors in human decisions.

    Telling people a decision is typically made by machines eliminated or even reversed the #bias.

    🔓 doi.org/10.1017/jdm.2025.8

    #AI #cogSci #xPhi #business #edu #tech

  16. 😱 Disclosure: The #fieldexperiment shows that disclosing the use of the AI application leads to significantly less interest in an offer among job candidates (compared to no information). #algorithmaversion
    ⚙ Deployment: Results indicate that the person–job fit determined by the leaders can be predicted by the AI application. However, both assessments (from the human and the AI applications) may have different forms of gender biases. More research needed. #digitaldiscretion (2/2)

  17. 😱 Disclosure: The #fieldexperiment shows that disclosing the use of the AI application leads to significantly less interest in an offer among job candidates (compared to no information). #algorithmaversion
    ⚙ Deployment: Results indicate that the person–job fit determined by the leaders can be predicted by the AI application. However, both assessments (from the human and the AI applications) may have different forms of gender biases. More research needed. #digitaldiscretion (2/2)

  18. 😱 Disclosure: The #fieldexperiment shows that disclosing the use of the AI application leads to significantly less interest in an offer among job candidates (compared to no information). #algorithmaversion
    ⚙ Deployment: Results indicate that the person–job fit determined by the leaders can be predicted by the AI application. However, both assessments (from the human and the AI applications) may have different forms of gender biases. More research needed. #digitaldiscretion (2/2)