How can we create a more just society with A.I.?

我们如何利用人工智能创造一个更加公正的社会？

• 批准号: MR/W011336/1
• 负责人: Tracie Farrell
• 金额: $ 161.58万
• 依托单位: The Open University
• 依托单位国家: 英国
• 项目类别: Fellowship
• 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=MR%2FW011336%2F1
• 关键词: How; create; more; just; society

Justice can be viewed as "objective" or mediated through power [Chomsky & Foucault, 1971; Costanza-Chock, 2018]. Finding commonalities across different legal and ethical frameworks [Floridi & Cowls, 2019; Jobin et al., 2019] is an example of the former. In the latter, justice is a "requirement" for non-equitable societies, ensuring protection for the most harmed [Cugueró-Escofet & Fortin, 2014]. The difficulty in achieving this type of justice through A.I. is that A.I. is used primarily for classification and prediction [Vinuesa et al., 2020]. Growing evidence indicates that A.I. accelerates and compounds social bias, contributing to unequal distributions of power [O'Neil, 2016, p. 3, Noble, 2018; Benjamin]. "Trade-offs" in providing accurate and fair predictions also impact sub-populations disproportionately [Yu et al. 2020], meaning that people with multiple forms of marginalisation are more likely to be misunderstood by A.I. than those with normative characteristics [Costanza-Chock, 2018]. While there are legal and ethical frameworks that should govern the way we use A.I., minority voices are still under-represented [Buolamwini, J. and Gebru, T., 2018, Costanza-Chock, 2018; Magalhães & Couldry, 2020] and there are few structures for enforcement and accountability [Mittelstadt, 2019]. We need to rethink how A.I. is contributing to justice as a relational concept, which includes dimensions of power and marginalisation. My proposal draws together the cultural, technical, and socio-technical expertise necessary to extend our current notions of justice in empirical research for A.I. for social good (AI4SG). To start with, the core team will develop a conceptual model of A.I. and "justice" that includes a) different definitions of justice used to frame the tasks of A.I. and evaluate their efficacy, b) the questions that can be answered under that definition and c) the trade-offs that are determined to be acceptable in the process. The research team will map scholarly literature from AI4SG to the ethical, legal or political frameworks that underpin the research, identifying gaps or conflicts in how justice is operationalised within AI4SG in comparison with other social justice models. In particular, we will explore the questions: are different positions on justice incompatible with A.I.? Can we identify new pathways for justice to emerge? To extend our conceptual model, we will conduct 3 case studies in which minority interests are ignored within specific A.I. tasks: 1) non-binary people in gender-based analysis of sexism 2) discriminatory deplatforming of sex workers or artists through content moderation and 3) shadow-banning activists as part of a counter-terrorism approach. The case studies will explore conflicts between these communities' concept of justice and the A.I. task, and which alternative solutions exist. They will also contribute to the global problem of tackling online harm and using A.I. techniques to help identify and classify relevant cases.Finally, to test alternative solutions, a multi-sectoral Advisory Board of A.I. and community experts will be brought together to create a design challenge for A.I. researchers. Issued through 2 workshops at top-level A.I. conferences, the challenge will be to prioritise marginalised perspectives. The outputs of the challenge and their evaluation will inform a set of guidelines for dealing with errors and trade-offs in AI4SG. Our contribution is to a) expose connections between how A.I. researchers define justice and which justice questions we attend to in AI4SG; b) reflect on the benefits of A.I. for which societies; and c) influence and inspire researchers to question assumptions of A.I. research around acceptable trade-offs and errors. This research will bring together social scientists, community experts and A.I. researchers to explore what new lines of inquiry can be opened by focusing on maximising the benefits in A.I. for marginalised groups

正义可以被视为“客观的”或通过权力来调解[Chomsky & Foucault，1971; Costanza-Chock，2018]。在不同的法律的和伦理框架中寻找共性[Floridi & Cowls，2019; Jobin等人，2019年，是第一个例子。在后一种情况下，正义是不平等社会的“要求”，确保保护最受伤害的人[Cugueró-Escofet & Fortin，2014年]。通过人工智能实现这种类型的正义的困难。是人工智能主要用于分类和预测[Vinuesa等人，2020年]。越来越多的证据表明，加速并加剧了社会偏见，导致权力分配不平等[O 'Neil，2016，第3页，Noble，2018; Benjamin]。在提供准确和公平的预测方面的“权衡”也不成比例地影响了亚人群[Yu et al. 2020]，这意味着具有多种形式边缘化的人更有可能被AI误解。而不是具有规范性特征的患者[Costanza-Chock，2018]。虽然有法律的和道德框架应该管理我们使用人工智能的方式，少数群体的声音仍然没有得到充分的代表[Buolamwini，J.和Gebru，T.，2018年，Costanza-Chock，2018年; Magalhenes & Couldry，2020年]，几乎没有执法和问责结构[Mittelstadt，2019年]。我们需要重新思考人工智能。作为一个关系概念，它包括权力和边缘化的维度。我的建议汇集了必要的文化，技术和社会技术专业知识，以扩展我们目前在人工智能实证研究中的正义概念。社会公益（AI 4SG）首先，核心团队将开发一个人工智能的概念模型。和“正义”，其中包括a）用于框定A.I.任务的不同正义定义。并评估其功效，B）根据该定义可以回答的问题，以及c）在该过程中确定为可接受的权衡。研究团队将从AI 4SG的学术文献映射到支持研究的伦理，法律的或政治框架，确定AI 4SG与其他社会正义模式相比如何在AI 4SG中运作的差距或冲突。特别是，我们将探讨以下问题：关于正义的不同立场是否与人工智能不相容？我们能否找到实现正义的新途径？为了扩展我们的概念模型，我们将进行3个案例研究，在这些案例中，少数人的利益在特定的人工智能中被忽视。任务：1）基于性别的性别歧视分析中的非二元人群; 2）通过内容审核对性工作者或艺术家进行歧视性的去平台化; 3）作为反恐方法的一部分，禁止影子活动家。案例研究将探讨这些社区的正义概念和人工智能之间的冲突。任务，以及存在哪些替代解决方案。它们还将为解决在线伤害和使用人工智能的全球问题做出贡献。最后，为了测试替代解决方案，人工智能的多部门咨询委员会，和社区专家将聚集在一起，为人工智能创造一个设计挑战。研究人员通过2个高级人工智能讲习班发布会议，挑战将是优先考虑边缘化的观点。挑战的输出及其评估将为AI 4SG中处理错误和权衡的一套指南提供信息。我们的贡献是a）揭示人工智能如何与人类的行为之间的联系。研究人员定义正义，以及我们在AI 4SG中关注的正义问题; B）反思AI的好处。c）影响和激励研究人员质疑人工智能的假设。围绕可接受的权衡和错误进行研究。这项研究将汇集社会科学家，社区专家和人工智能。研究人员探索通过专注于最大限度地发挥人工智能的优势，可以开辟哪些新的研究方向。边缘化群体