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我无法预测的,我不明白:一个以人为中心的评估框架,用于可解释性方法.

Julien Colin1,2,3, Thomas Fel1,2,4, Rémi Cadène1,5

  • 1Carney Institute for Brain Science, Brown University, USA.

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概括
此摘要是机器生成的。

当前的人工智能可解释性指标往往无法反映现实世界的有用性. 大规模的心理物理实验表明,归因方法的有效性各不相同,这凸显了对最终用户的多样化解释方法的需求.

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科学领域:

  • 人工智能的人工智能
  • 人与计算机的交互
  • 认知科学 认知科学

背景情况:

  • 许多AI可解释性方法存在,以消除AI决策的神秘性.
  • 现有的可解释性性能指标缺乏最终用户重点和现实世界的验证.
  • 可解释性技术的实际实用性和其评估指标的准确性仍然不清楚.

研究的目的:

  • 评估当前人工智能可解释性方法在现实世界中的有用性.
  • 为了确定现有的性能指标是否准确地衡量最终用户的实用性.
  • 在各种应用中调查归因方法的有效性.

主要方法:

  • 进行了大规模的心理物理实验,共有1150名参与者.
  • 在三个不同的现实场景中评估了代表性归因方法.
  • 收集有关用户对人工智能系统的理解和交互的数据.

主要成果:

  • 个别归因方法的有用性在评估的场景中显著不同.
  • 人类参与者理解人工智能系统的能力因使用的解释方法而异.
  • 没有单一的归因方法证明在提高用户理解方面具有普遍有效性.

结论:

  • 当前的可解释性方法在实际环境中具有可变的实用性.
  • 现有的指标可能无法充分捕捉AI解释的最终用户价值.
  • 未来的研究应该专注于开发互补的,质量多样化的解释策略.