Opening the black box: helping AI to persuade without bias

打开黑匣子：帮助人工智能无偏见地说服

• 批准号: 2752436
• 金额: --
• 依托单位: University of Aberdeen
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2752436
• 关键词: Opening; black; box; helping; AI

The overall aim of the project is to establish how AI can use natural language to persuade humans in transparent and bias-free ways. AI systems are becoming an integral part of our daily lives, from simple recommendations on YouTube or Netflix, to life-changing decisions such as shortlisting job applicants or recommending loans. However, these systems lack transparency and objective indicators of fairness, producing poorly understood 'advice' that could be fuelling biased decisions on a growing scale right across society. Biased advice that treats individuals differently based on their gender is a particular concern (e.g. in employment contexts). Detecting and removing this bias is extremely difficult. It arises from stereotyped human labelling and categorisation residing within huge, cobbled-together data sets typically used to train AI. Because this bias is 'hidden', its transmission to humans interacting with AI is correspondingly difficult to establish, and even more difficult to eliminate. In this Phd, we propose and test a novel solution which combines experimental psychology with computing science to offer a way of detecting when biased advice from AI passes into human judgements. The student will engineer their own AI into which known patterns of gender-bias, deriving from our work on cognitive stereotypes, can be 'injected'. Using advanced natural language models, the AI will have brief 'conversations' with human participants in a series of studies that will provide a rich source of data on acts of rejection or conformity to biased and unbiased advice. Using cutting-edge machine learning techniques, the student will analyse this new bespoke data set to identify language features that signal persuasion. Within these features the student will seek to isolate ones specifically linked to acceptance of biased content. Crucially, these features may be used to detect the transmission of hidden biases in real-life applications.

该项目的总体目的是建立AI如何使用自然语言以透明和无偏见的方式说服人类。 AI系统正在成为我们日常生活中不可或缺的一部分，从YouTube或Netflix上的简单建议到改变生活的决定，例如入围工作申请人或推荐贷款。但是，这些系统缺乏公平性的透明度和客观指标，从而产生了鲜为人知的“建议”，这可能会促进整个社会日益增长的规模的有偏见的决定。有偏见的建议，根据性别对个人的对待有所不同，这是一个特别关注的问题（例如，在就业背景下）。检测和消除这种偏见非常困难。它源于刻板印象的人类标记和分类，这些标签和分类位于通常用于训练AI的巨大的，拼凑的数据集中。因为这种偏见是“隐藏的”，所以它向人类与AI互动的传播相应地建立非常困难，甚至更难消除。在该博士学位上，我们提出并测试一种新颖的解决方案，该解决方案将实验心理学与计算科学相结合，以提供一种检测AI的偏见建议的方法。该学生将自己的AI设计自己的AI，从我们在认知刻板印象上的工作中得出的已知性别偏见模式可以被“注入”。使用先进的自然语言模型，AI将与人类参与者进行一系列研究的简短“对话”，这些研究将提供有关拒绝行为或符合偏见和无偏见建议的丰富数据来源。使用尖端的机器学习技术，学生将分析这个新的定制数据集，以识别信号说服力的语言特征。在这些功能中，学生将寻求隔离专门链接的偏见内容。至关重要的是，这些功能可用于检测现实生活应用中隐藏偏见的传播。