EAGER: Pilot Study on Bias and Trust in AI Systems

EAGER：人工智能系统中的偏见和信任的试点研究

• 批准号: 1849101
• 负责人: Ayanna Howard
• 金额: $ 7.45万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1849101&HistoricalAwards=false
• 关键词: EAGER; Pilot; Study; Bias; Trust

Robots and other autonomous systems are proliferating rapidly, and are being used in the public domain for such purposes as companionship, facilitating interactive learning, and making recommendations to users. However, little is understood about how trust, including mistrust and over-trust, and bias develop as humans interact with these systems. The investigators of this project will use basic real-life scenarios to understand human-human interaction in the context of trust and bias, and then will translate these data into quantized attributes. This data will be used to develop general algorithms that will be useful in programming robots in ways that ensure human safety and well-being when interacting with robots. This project will conduct a series of pilot tests to explore fundamental research questions to find ways to minimize potential negative impacts and consequences of human-robot interaction. Additionally, the investigators will integrate the research with teaching and training of graduate and undergraduate students.The aim of this project is to gain knowledge that informs the design of future robot systems by understanding how trust is established and how bias impacts human perception and algorithmic performance. As part of this work, the team also aims to establish baseline algorithms that mitigate the impacts of bias in autonomous systems. The specific research objectives of this project are to quantify the impact of trust and bias in human-robot interaction scenarios where the algorithms are designed based on learned data from human experts; and to develop methods for objectively mitigating bias, while still optimizing for robot performance. The pilot tests are designed to contribute to understanding of social cognition in human-robot interaction. Direct societal befits will be gained as robots and other autonomous systems proliferate in the public domain.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

机器人和其他自主系统正在迅速扩散，并被用于公共领域，如陪伴，促进互动学习，并向用户提出建议。然而，人们对信任（包括不信任和过度信任）以及偏见如何在人类与这些系统互动时发展知之甚少。该项目的研究人员将使用基本的现实生活场景来理解信任和偏见背景下的人与人之间的互动，然后将这些数据转化为量化的属性。 这些数据将用于开发通用算法，这些算法将有助于对机器人进行编程，以确保人类在与机器人互动时的安全和福祉。该项目将进行一系列试点测试，探索基础研究问题，以找到最大限度地减少人机交互的潜在负面影响和后果的方法。此外，研究人员将把研究与研究生和本科生的教学和培训结合起来。该项目的目的是通过了解信任是如何建立的以及偏见如何影响人类感知和算法性能，从而获得为未来机器人系统设计提供信息的知识。作为这项工作的一部分，该团队还旨在建立基线算法，以减轻自治系统中偏差的影响。该项目的具体研究目标是量化信任和偏见在人机交互场景中的影响，其中算法是基于人类专家的学习数据设计的;并开发客观减轻偏见的方法，同时仍然优化机器人性能。 试点测试的目的是促进人类与机器人互动中的社会认知的理解。随着机器人和其他自主系统在公共领域的普及，将获得直接的社会福利。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Comparative Analysis of Emotion-Detecting AI Systems with Respect to Algorithm Performance and Dataset Diversity

• DOI: 10.1145/3306618.3314284
• 发表时间: 2019-01
• 期刊: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society
• 作者: De'Aira G. Bryant;A. Howard

Human Trust After Robot Mistakes: Study of the Effects of Different Forms of Robot Communication

机器人犯错后的人类信任：不同形式的机器人通信的影响研究

• DOI: 10.1109/ro-man46459.2019.8956424
• 发表时间: 2019
• 期刊: 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN
• 作者: Ye, Sean;Neville, Glen;Schrum, Mariah;Gombolay, Matthew;Chernova, Sonia;Howard, Ayanna
• 通讯作者: Howard, Ayanna