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Countering Bias in Personalized Rankings From Data Engineering to Algorithm Development

Mirko Marras
2021
Conference paper
Abstract

This tutorial presents recent advances on the assessment and mitigation of data and algorithmic bias in personalized rankings. We first introduce fundamental concepts and definitions associated with bias issues, covering the state of the art and describing real-world examples of how bias can impact ranking algorithms from several perspectives (e.g., ethics and system's objectives). Then, we continue with a systematic presentation of techniques to uncover, assess, and mitigate biases along the personalized ranking design process, with a focus on the role of data engineering in each step of the pipeline. Handson parts provide attendees with concrete implementations of bias mitigation algorithms, in addition to processes and guidelines on how data is organized and manipulated by these algorithms. The tutorial leverages open-source tools and public datasets, engaging attendees in designing bias countermeasures and in articulating impacts on stakeholders. We finally showcase open issues and future directions in this vibrant and rapidly evolving research area (Website: https://biasinrecsys.github.ioticde2021/).

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Related concepts (19)
Bias
Bias is a disproportionate weight in favor of or against an idea or thing, usually in a way that is closed-minded, prejudicial, or unfair. Biases can be innate or learned. People may develop biases for or against an individual, a group, or a belief. In science and engineering, a bias is a systematic error. Statistical bias results from an unfair sampling of a population, or from an estimation process that does not give accurate results on average. The word appears to derive from Old Provençal into Old French biais, "sideways, askance, against the grain".
Algorithmic bias
Algorithmic bias describes systematic and repeatable errors in a computer system that create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm. Bias can emerge from many factors, including but not limited to the design of the algorithm or the unintended or unanticipated use or decisions relating to the way data is coded, collected, selected or used to train the algorithm. For example, algorithmic bias has been observed in search engine results and social media platforms.
Media bias
Media bias is the bias of journalists and news producers within the mass media in the selection of many events and stories that are reported and how they are covered. The term "media bias" implies a pervasive or widespread bias contravening of the standards of journalism, rather than the perspective of an individual journalist or article. The direction and degree of media bias in various countries is widely disputed.
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