SIGMOD 2020 Tutorial on Fairness and Bias in Peer Review and Other Sociotechnical Intelligent Systems

Abstract

Questions of fairness and bias abound in all socially-consequential decisions pertaining to collection and management of data. Whether designing protocols for peer review of research papers, setting hiring policies, or framing research question in genetics, any data-management decision with the potential to allocate benefits or confer harms raises concerns about who gains or loses that may fail to surface in naively-chosen performance measures. Data science interacts with these questions in two fundamentally different ways: (i) as the technology driving the very systems responsible for certain social impacts, posing new questions about what it means for such systems to accord with ethical norms and the law; and (ii) as a set of powerful tools for analyzing existing data management systems, e.g., for auditing existing systems for various biases. This tutorial will tackle both angles on the interaction between technology and society visa -vis concerns over fairness and bias, particularly focusing on the collection and management of data. Our presentation will cover a wide range of disciplinary perspectives with the first part focusing on the social impacts of technology and the formulations of fairness and bias defined via protected characteristics and the second part taking a deep into peer review and distributed human evaluations, to explore other forms of bias, such as that due to subjectivity, miscalibration, and dishonest behavior.

Reviewer Paper Assignment Problem – A Brief Review

“…In contrast, reviewer availability has been well covered in RAP solutions oriented towards conference-like settings, as several studies included reviewer workload (i.e., limits on the number of assignments) as one of the constraints. Last but not the least important, considering the relevance of fairness in peer review and the fact that is not always at the desired level [43], it would be important to give more attention to fairness as a multifaceted reviewer selection criterion that extends beyond directly observable and/or self-reported COI, to also consider potential indirect connections between authors and CRs (e.g., identified through academic networks), equal number of reviewers per submission, as well as adequate submission coverage in terms of complementarity of the reviewers' expertise.…”

Building community together: towards equitable CSCL practices and processes

“…These are concerns raised within the popular press regarding increasing awareness of Artificial Intelligence (AI) applied in many every day experiences and connect in obvious ways with the issues regarding equity and diversity raised earlier. It should be well noted that these are issues very much at the forefront of research in AI and Machine Learning (ML), especially in the area of AI transparency, explainability, and fairness (Holstein et al 2019;Shah and Lipton 2020;Wang et al 2020), and the popular press portrayal as AI alternately as hero and foe can sometimes be taken up and amplified, even by experts of other fields, to the detriment of all. Taking instead a reasonable stance, Uttamchandani and colleagues end their use case discussion reaffirming the potential for these forms of collaboration support to bring positive change.…”