Causality, Counterfactuals and Meta-learning to Address the Complexity of Fairness in Data Science and Machine Learning

因果关系、反事实和元学习解决数据科学和机器学习中公平性的复杂性

• 批准号: 2751295
• 金额: --
• 依托单位: London School of Economics and Political Science
• 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-2751295
• 关键词: Causality; Counterfactuals; Meta; learning; Address

The complexity of systemic inequalities in society has mostly so far eluded the methods taken to address fairness in machine learning. A step in the right direction for solving this is to create statistical methods which aim to identify and counter the root causes. I propose this can be done with more specific and complex causal and counterfactual models to infer multiple causes, structures and to avoid assumptions about social categories. I propose the possibility of extending this to meta-learning to further understand structures of inequality, which can also act as a technical basis for policy making and audits.Machine learning is highly effective in predicting outcomes accurately, thus providing the opportunity to allocate scarce societal resources quickly and efficiently. Consequently, machine learning has rapidly acquired a presence in high stakes decisions in socio-technical systems, which are systems that involve complex interactions between humans, machines and society. As machine learning has advanced in this space, its presence in the criminal justice system, health care, and the education system, shows that these algorithms were readily reproducing and exaggerating discrimination that exists in the world, causing significant harm.This led to the development of a new field in machine learning - fairness, with conferences such as Fairness, Accountability and Transparency (FAccT), and Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) being created. Thus, in recent years, machine learning researchers have made significant effort to mitigate biases and discrimination exacerbated by its technologies, however there are significant gaps and failures within this current research area. Fairness algorithms are not generalisable beyond specific contexts and social inequalities are persistent, systemic and complex which is not reflected in the technical work. Little has been done to integrate the social sciences beyond the basic a priori argument of bias, and the majority of fairness work acts as a quick-fix in quality assurance, as opposed to trying to get to the root of the cause.This research proposal includes questions and ideas on how to integrate the complexity of social inequality, such as intersectional theory and infra-marginality, into statistics and machine learning, for a version of fair machine learning which correctly works with the complexes of society. While my research questions have been shaped by the wealth of previous work in this field, my specific questions are primarily based on work which aims for more complexity in causal models for fairness, such as, intersectionality in fair ranking and impact remediation. The main questions I want to address within my PhD work are:Can a more complex notion of fairness in machine learning be developed and understood with specific causal and counterfactual models to infer multiple causes and structures?Can this be combined with meta-learning to learn several algorithms, and thus learn several different discrimination hierarchies within the society?

到目前为止，社会中系统性不平等的复杂性大多回避了机器学习中解决公平性的方法。解决这一问题的正确方向是创建旨在识别和消除根源的统计方法。我建议这可以通过更具体和复杂的因果和反事实模型来推断多个原因，结构，并避免对社会类别的假设。我建议将其扩展到元学习的可能性，以进一步了解不平等的结构，这也可以作为政策制定和审计的技术基础。机器学习在准确预测结果方面非常有效，从而为快速有效地分配稀缺的社会资源提供了机会。因此，机器学习已经迅速在社会技术系统中的高风险决策中占据一席之地，这些系统涉及人类，机器和社会之间的复杂交互。随着机器学习在这一领域的发展，它在刑事司法系统、医疗保健和教育系统中的存在表明，这些算法很容易复制和夸大世界上存在的歧视，造成重大伤害。这导致了机器学习中一个新领域的发展-公平，如公平、问责制和透明度（FAccT），算法、机制和优化中的公平和访问（Equity and Access in Algorithms，Mechanisms，and Optimization，EAAMO）因此，近年来，机器学习研究人员做出了巨大努力，以减轻其技术加剧的偏见和歧视，但在目前的研究领域存在重大差距和失败。公平算法无法在特定背景之外推广，社会不平等是持久的、系统的和复杂的，这在技术工作中没有反映出来。除了偏见的基本先验论点之外，几乎没有做过什么来整合社会科学，大多数公平工作都是作为质量保证的快速解决方案，而不是试图找到原因的根源。这项研究提案包括关于如何将社会不平等的复杂性，如交叉理论和超边缘性，整合到统计和机器学习中的问题和想法，一个公平的机器学习版本，它可以正确地与社会复合体一起工作。虽然我的研究问题已经形成了丰富的以前在这一领域的工作，我的具体问题主要是基于工作，旨在更复杂的因果模型的公平性，如，交叉性在公平排名和影响补救。我想在我的博士工作中解决的主要问题是：机器学习中更复杂的公平性概念能否通过特定的因果和反事实模型来开发和理解，以推断多个原因和结构？这是否可以与元学习相结合，学习几种算法，从而学习社会中几种不同的歧视等级？