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?

迄今为止，社会系统性不平等的复杂性大多无法解决机器学习公平问题。解决这个问题的正确方向的一步是创建旨在识别和解决根本原因的统计方法。我建议这可以通过更具体、更复杂的因果和反事实模型来推断多种原因、结构并避免对社会类别的假设。我提出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，从而提供了分配稀缺社会资源的机会快速高效地获取资源。因此，机器学习迅速出现在社会技术系统的高风险决策中，这些系统涉及人类、机器和社会之间复杂的交互。随着机器学习在这一领域的进步，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，造成重大危害。这导致了机器学习的发展机器学习的一个新领域——公平性，正在创建公平、问责和透明度 (FAccT) 以及算法、机制和优化的公平和访问 (EAAMO) 等会议。因此，近年来，机器学习研究人员为减轻其技术加剧的偏见和歧视做出了巨大努力，但当前的研究领域存在重大差距和失败。公平算法不能在特定背景下推广，社会不平等是持久的、系统性的和复杂的，这在技术工作中没有得到体现。除了偏见的基本先验论证之外，几乎没有采取任何措施来整合社会科学，并且大多数公平工作只是质量保证的快速解决方案，而不是试图找出原因的根源。这项研究提案包括有关如何将社会不平等的复杂性（例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，以获得正确地与社会复杂性相结合的公平机器学习版本。虽然我的研究问题是由该领域之前的大量工作决定的，但我的具体问题主要基于旨在提高公平性因果模型的复杂性的工作，例如公平排名和影响补救中的交叉性。我想在博士工作中解决的主要问题是：能否通过特定的因果和反事实模型来开发和理解机器学习中更复杂的公平概念，以推断多种原因和结构？这可以与元学习相结合来学习吗？几种算法，从而了解社会中几种不同的歧视等级制度？