DARPA's Explainable Artificial Intelligence Program
DARPA's Explainable Artificial Intelligence Program
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DOI：
10.1609/aimag.v40i2.2850
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发表时间：
2019-06-01
期刊：
影响因子：
0.9
通讯作者：
Aha, David W.
中科院分区：
文献类型：
--
作者：
Gunning, David;Aha, David W.
机器学习的巨大成功引发了新一波的人工智能应用（例如交通、安全、医学、金融、国防），这些应用提供了巨大的好处，但无法向人类用户解释其决策和行为。 DARPA 的可解释人工智能 (XAI) 计划致力于创建人工智能系统，其学习模型和决策可以被最终用户理解并适当信任。实现这一目标需要学习更多可解释模型、设计有效解释界面以及理解有效解释的心理要求的方法。 XAI 开发团队正在通过创建 ML 技术并开发原理、策略和人机交互技术来解决前两个挑战，以生成有效的解释。另一个 XAI 团队正在通过总结、扩展和应用心理学解释理论来解决第三个挑战，以帮助 XAI 评估者定义合适的评估框架，开发团队将使用该框架来测试他们的系统。 XAI 团队于 2018 年 5 月完成了这个为期 4 年的计划中的第一个。在一系列正在进行的评估中，开发团队正在评估其 XAM 系统的解释在提高用户理解、用户信任和用户任务性能方面的效果。
Dramatic success in machine learning has led to a new wave of AI applications (for example, transportation, security, medicine, finance, defense) that offer tremendous benefits but cannot explain their decisions and actions to human users. DARPA's explainable artificial intelligence (XAI) program endeavors to create AI systems whose learned models and decisions can be understood and appropriately trusted by end users. Realizing this goal requires methods for learning more explainable models, designing effective explanation interfaces, and understanding the psychologic requirements for effective explanations. The XAI developer teams are addressing the first two challenges by creating ML techniques and developing principles, strategies, and human-computer interaction techniques for generating effective explanations. Another XAI team is addressing the third challenge by summarizing, extending, and applying psychologic theories of explanation to help the XAI evaluator define a suitable evaluation framework, which the developer teams will use to test their systems. The XAI teams completed the first of this 4-year program in May 2018. In a series of ongoing evaluations, the developer teams are assessing how well their XAM systems' explanations improve user understanding, user trust, and user task performance.