This survey article assesses and compares existing critiques of current fairness-enhancing technical interventions in machine learning (ML) that draw from a range of non-computing disciplines, including philosophy, feminist studies, critical race and ethnic studies, legal studies, anthropology, and science and technology studies. It bridges epistemic divides in order to offer an interdisciplinary understanding of the possibilities and limits of hegemonic computational approaches to ML fairness for producing just outcomes for society’s most marginalized. The article is organized according to nine major themes of critique wherein these different fields intersect: 1) how "fairness" in AI fairness research gets defined; 2) how problems for AI systems to address get formulated; 3) the impacts of abstraction on how AI tools function and its propensity to lead to technological solutionism; 4) how racial classification operates within AI fairness research; 5) the use of AI fairness measures to avoid regulation and engage in ethics washing; 6) an absence of participatory design and democratic deliberation in AI fairness considerations; 7) data collection practices that entrench “bias,” are non-consensual, and lack transparency; 8) the predatory inclusion of marginalized groups into AI systems; and 9) a lack of engagement with AI’s long-term social and ethical outcomes. Drawing from these critiques, the article concludes by imagining future ML fairness research directions that actively disrupt entrenched power dynamics and structural injustices in society.
more »
« less
Fairness and Abstraction in Sociotechnical Systems
A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as fairness, justice, and due process. Bedrock concepts in computer science---such as abstraction and modular design---are used to define notions of fairness and discrimination, to produce fairness-aware learning algorithms, and to intervene at different stages of a decision-making pipeline to produce "fair" outcomes. In this paper, however, we contend that these concepts render technical interventions ineffective, inaccurate, and sometimes dangerously misguided when they enter the societal context that surrounds decision-making systems. We outline this mismatch with five "traps" that fair-ML work can fall into even as it attempts to be more context-aware in comparison to traditional data science. We draw on studies of sociotechnical systems in Science and Technology Studies to explain why such traps occur and how to avoid them. Finally, we suggest ways in which technical designers can mitigate the traps through a refocusing of design in terms of process rather than solutions, and by drawing abstraction boundaries to include social actors rather than purely technical ones.
more »
« less
- NSF-PAR ID:
- 10121131
- Date Published:
- Journal Name:
- Proceedings of the Conference on Fairness, Accountability, and Transparency
- Page Range / eLocation ID:
- 59 to 68
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Machine Learning has become a popular tool in a variety of applications in criminal justice, including sentencing and policing. Media has brought attention to the possibility of predictive policing systems causing disparate impacts and exacerbating social injustices. However, there is little academic research on the importance of fairness in machine learning applications in policing. Although prior research has shown that machine learning models can handle some tasks efficiently, they are susceptible to replicating systemic bias of previous human decision-makers. While there is much research on fair machine learning in general, there is a need to investigate fair machine learning techniques as they pertain to the predictive policing. Therefore, we evaluate the existing publications in the field of fairness in machine learning and predictive policing to arrive at a set of standards for fair predictive policing. We also review the evaluations of ML applications in the area of criminal justice and potential techniques to improve these technologies going forward. We urge that the growing literature on fairness in ML be brought into conversation with the legal and social science concerns being raised about predictive policing. Lastly, in any area, including predictive policing, the pros and cons of the technology need to be evaluated holistically to determine whether and how the technology should be used in policing.more » « less
-
Roth, A (Ed.)It is well understood that classification algorithms, for example, for deciding on loan applications, cannot be evaluated for fairness without taking context into account. We examine what can be learned from a fairness oracle equipped with an underlying understanding of “true” fairness. The oracle takes as input a (context, classifier) pair satisfying an arbitrary fairness definition, and accepts or rejects the pair according to whether the classifier satisfies the underlying fairness truth. Our principal conceptual result is an extraction procedure that learns the underlying truth; moreover, the procedure can learn an approximation to this truth given access to a weak form of the oracle. Since every “truly fair” classifier induces a coarse metric, in which those receiving the same decision are at distance zero from one another and those receiving different decisions are at distance one, this extraction process provides the basis for ensuring a rough form of metric fairness, also known as individual fairness. Our principal technical result is a higher fidelity extractor under a mild technical constraint on the weak oracle’s conception of fairness. Our framework permits the scenario in which many classifiers, with differing outcomes, may all be considered fair. Our results have implications for interpretablity – a highly desired but poorly defined property of classification systems that endeavors to permit a human arbiter to reject classifiers deemed to be“unfair” or illegitimately derived.more » « less
-
null (Ed.)Although engineering graduates are well prepared in the technical aspects of engineering, it is widely acknowledged that there is a need for a greater understanding of the socio-economic contexts in which they will practice their profession. The National Academy of Engineering (NAE) reinforces the critical role that engineers should play in addressing both problems and opportunities that are technical, social, economic, and political in nature in solving the grand challenges. This paper provides an overview of a nascent effort to address this educational need. Through a National Science Foundation (NSF) funded program, a team of researchers at West Virginia University has launched a Holistic Engineering Project Experience (HEPE). This undergraduate course provides the opportunity for engineering students to work with social science students from the fields of economics and strategic communication on complex and open-ended transportation engineering problems. This course involves cross-disciplinary teams working under diverse constraints of real-world social considerations, such as economic impacts, public policy concerns, and public perception and outreach factors, considering the future autonomous transportation systems. The goal of the HEPE platform is for engineering students to have an opportunity to build non-technical—but highly in-demand—professional skills that promote collaboration with others involved in the socio-economic context of engineering matters. Conversely, the HEPE approach provides an opportunity for non-engineering students to become exposed to key concepts and practices in engineering. This paper outlines the initial implementation of the HEPE program, by placing the effort in context of broader trends in education, by outlining the overall purposes of the program, discussing the course design and structure, reviewing the learning experience and outcomes assessment process, and providing preliminary results of a baseline survey that gauges students interests and attitudes towards collaborative and interdisciplinary learning.more » « less
-
Graph is a ubiquitous type of data that appears in many real-world applications, including social network analysis, recommendations and financial security. Important as it is, decades of research have developed plentiful computational models to mine graphs. Despite its prosperity, concerns with respect to the potential algorithmic discrimination have been grown recently. Algorithmic fairness on graphs, which aims to mitigate bias introduced or amplified during the graph mining process, is an attractive yet challenging research topic. The first challenge corresponds to the theoretical challenge, where the non-IID nature of graph data may not only invalidate the basic assumption behind many existing studies in fair machine learning, but also introduce new fairness definition(s) based on the inter-correlation between nodes rather than the existing fairness definition(s) in fair machine learning. The second challenge regarding its algorithmic aspect aims to understand how to balance the trade-off between model accuracy and fairness. This tutorial aims to (1) comprehensively review the state-of-the-art techniques to enforce algorithmic fairness on graphs and (2) enlighten the open challenges and future directions. We believe this tutorial could benefit researchers and practitioners from the areas of data mining, artificial intelligence and social science.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3287560.3287598
