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

正义可以被视为“客观的”或通过权力调解[乔姆斯基和福柯，1971；Costanza-Chock, 2018]。在不同的法律和道德框架中寻找共同点[Floridi & Cowls, 2019；Jobin et al.， 2019]是前者的一个例子。在后一种情况下，正义是不公平社会的一种“要求”，确保对最受伤害的人提供保护[Cugueró-Escofet & Fortin， 2014]。通过人工智能实现这种正义的困难在于，人工智能主要用于分类和预测[Vinuesa et al., 2020]。越来越多的证据表明，人工智能加速并加剧了社会偏见，导致了权力的不平等分配[O'Neil, 2016, p. 3, Noble, 2018；本杰明]。提供准确和公平预测的“权衡”也会不成比例地影响亚群体[Yu等人，2020]，这意味着与具有规范特征的人相比，具有多种边缘化形式的人更有可能被人工智能误解[Costanza-Chock， 2018]。虽然应该有法律和道德框架来管理我们使用人工智能的方式，但少数群体的声音仍然没有得到充分代表[Buolamwini， J. and Gebru， T., 2018, Costanza-Chock, 2018；magalh<e:1> & Couldry, 2020]，并且很少有执行和问责的结构[Mittelstadt， 2019]。我们需要重新思考人工智能作为一个关系概念如何促进正义，其中包括权力和边缘化的维度。我的建议汇集了文化、技术和社会技术方面的专业知识，以扩展我们目前在人工智能社会公益（AI4SG）实证研究中的正义概念。首先，核心团队将开发一个人工智能和“正义”的概念模型，其中包括a)用于构建人工智能任务并评估其功效的不同正义定义，b)在该定义下可以回答的问题，以及c)在此过程中确定可接受的权衡。研究小组将把AI4SG的学术文献映射到支撑研究的伦理、法律或政治框架，与其他社会正义模型相比，确定AI4SG中正义如何运作的差距或冲突。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，其中少数群体的利益在特定的人工智能任务中被忽视：1)基于性别的性别歧视分析中的非二元人群2)通过内容审查歧视性工作者或艺术家，3)作为反恐方法的一部分，禁止影子活动人士。案例研究将探讨这些社区的正义概念与人工智能任务之间的冲突，以及存在哪些替代解决方案。它们还将有助于解决在线伤害的全球问题，并使用人工智能技术帮助识别和分类相关案例。最后，为了测试替代解决方案，一个由人工智能和社区专家组成的多部门咨询委员会将聚集在一起，为人工智能研究人员创造一个设计挑战。通过在顶级人工智能会议上的两个研讨会发布，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处理AI4SG中的错误和权衡提供一套指导方针。我们的贡献是a)揭示人工智能研究人员如何定义正义与我们在AI4SG中关注的正义问题之间的联系；b)反思人工智能给社会带来的好处；c)影响和激励研究人员质疑人工智能研究中关于可接受的权衡和错误的假设。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，通过专注于为边缘化群体最大化人工智能的好处，探索可以开辟哪些新的研究路线

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