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EAGER: AI-DCL: Measuring and Mitigating Animosity toward Artificial Intelligence Systems and Science

EAGER：AI-DCL：衡量和减轻对人工智能系统和科学的敌意

基本信息

批准号：
1927227
负责人：
Jason Jones
金额：
$ 29.97万
依托单位：
SUNY at Stony Brook
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-15 至 2023-10-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1927227&HistoricalAwards=false
关键词：
EAGER AI DCL Measuring Mitigating

项目摘要

Artificial Intelligence (AI) applications are demonstrating undeniable success and widespread adoption, but the problem of algorithmic bias has arisen. Algorithmic bias occurs when AI systems produce unjust decisions. This problem is made worse by the fact that AI systems are opaque and unfamiliar to most people. Scientists and the public want to unlock the power of fast, intelligent computer systems, but they are understandably hesitant when they see these systems unfairly discriminate or simply make unexplained decisions. This research will answer fundamental questions to assist in understanding the public's trust and mistrust of AI: How does the public feel about AI systems? What do they think of the shapers of these systems? And do these attitudes change as they gain experience with AI systems?The aim of this research is to directly measure public opinion regarding artificial intelligence systems and scientists and test the hypothesis that exposure to interpretable AI will lead to more positive attitudes. The integration of AI systems into decision-making processes previously the sole domain of human judgement is still a relatively novel phenomenon. Public opinion is likely in a dynamic phase and measuring how public attitudes toward AI evolve over the next few years is crucially important. Thus, this project will compile monthly composite measures of trust in AI systems and scientists through surveying a representative sample of the US population. Additionally, it is the case that much effort is currently being expended to make AI systems more interpretable. This effort is predicated on the untested assumption that negative attitudes toward AI are due to the complex and opaque nature of its underlying algorithms. The investigators will test the effect of firsthand experience with AI systems while experimentally controlling the level of transparency. The goal is an explicit test of the theory that increased exposure to interpretable AI will decrease distrust and other negative attitudes toward AI. With this research, the investigators will measure and test a method to mitigate mistrust of AI and advance the conversation currently taking place across social science and engineering disciplines regarding how humanity shall relate to a powerful new tool.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.

人工智能（AI）应用程序正在展示不可否认的成功和广泛采用，但算法偏见的问题已经出现。当人工智能系统产生不公正的决定时，就会发生种族偏见。这个问题变得更糟，因为人工智能系统对大多数人来说是不透明和不熟悉的。科学家和公众都希望释放快速、智能计算机系统的力量，但当他们看到这些系统不公平地歧视或只是做出无法解释的决定时，他们的犹豫是可以理解的。这项研究将回答基本问题，以帮助理解公众对人工智能的信任和不信任：公众对人工智能系统的感觉如何？他们怎么看待这些系统的塑造者？随着他们获得人工智能系统的经验，这些态度会改变吗？这项研究的目的是直接衡量公众对人工智能系统和科学家的看法，并测试接触可解释的人工智能将导致更积极态度的假设。将人工智能系统集成到决策过程中，以前是人类判断的唯一领域，这仍然是一个相对新颖的现象。公众舆论可能处于动态阶段，衡量公众对人工智能的态度在未来几年如何演变至关重要。因此，该项目将通过调查美国人口的代表性样本，每月编制对人工智能系统和科学家信任的综合衡量标准。此外，目前正在花费大量努力使AI系统更具可解释性。这一努力是基于一个未经验证的假设，即对人工智能的负面态度是由于其底层算法的复杂性和不透明性。研究人员将测试人工智能系统的第一手经验的效果，同时通过实验控制透明度水平。我们的目标是明确测试这样一种理论，即增加对可解释人工智能的接触将减少对人工智能的不信任和其他负面态度。通过这项研究，研究人员将测量和测试一种方法，以减轻对人工智能的不信任，并推进目前正在社会科学和工程学科之间进行的关于人类如何与强大的新工具相关联的对话。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。