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

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?