可解释性陷:超越可解释AI的黑暗模式
1Georgia Institute of Technology, Atlanta GA, USA.
Patterns (New York, N.Y.)
|July 15, 2024
概括
可解释性陷 (EPs) 是人工智能解释的意外负面后果,与恶意黑暗模式不同. 解决这些问题对于构建可靠的可解释的人工智能 (XAI) 系统至关重要.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 人工智能伦理学
背景情况:
- 可解释的人工智能 (XAI) 系统旨在提高透明度和可信度.
- 了解AI解释的潜在负面影响对于可靠的AI部署至关重要.
- 现有的研究往往侧重于故意操纵,忽视了无意的伤害.
研究的目的:
- 在XAI中引入和定义"可解释性陷" (EPs) 作为一种新的负面影响类别.
- 为了将EP与故意欺骗性的"黑暗模式"区分开来.
- 提出在XAI系统中减轻EP的策略.
主要方法:
- 概念表达和区分可解释性陷.
- 通过案例研究分析来实现EP的运行.
- 开发多层次的策略 (研究,设计,组织) 来应对EP.
主要成果:
- 可解释性陷代表了人工智能解释的意外负面下游影响.
- 即使没有用户操纵,这些陷也可能出现,导致诸如对数值输出的不合理信任等问题.
- 一个案例研究表明,尽管有良好的意图,但意外的负面影响仍在出现.
结论:
- 需要积极主动和预防性策略,以在研究,设计和组织层面解决EP问题.
- 重构人工智能采用,重新校准利益相关者的授权,并抵制"快速移动和打破事物"的方法是关键的含义.
- 缓解EP对于促进XAI的真正信任和负责任的创新至关重要.
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