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Transparency in Fair Machine Learning: the Case of Explainable Recommender Systems

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Human and Machine Learning

Part of the book series: Human–Computer Interaction Series ((HCIS))

Abstract

Machine Learning (ML) models are increasingly being used in many sectors, ranging from health and education to justice and criminal investigation. Therefore, building a fair and transparent model which conveys the reasoning behind its predictions is of great importance. This chapter discusses the role of explanation mechanisms in building fair machine learning models and explainable ML technique. We focus on the special case of recommender systems because they are a prominent example of a ML model that interacts directly with humans. This is in contrast to many other traditional decision making systems that interact with experts (e.g. in the health-care domain). In addition, we discuss the main sources of bias that can lead to biased and unfair models. We then review the taxonomy of explanation styles for recommender systems and review models that can provide explanations for their recommendations. We conclude by reviewing evaluation metrics for assessing the power of explainability in recommender systems.

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Acknowledgements

This research was partially supported by KSEF Award KSEF-3113-RDE-017 and NSF Grant NSF IIS-1549981.

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Authors and Affiliations

  1. Knowledge Discovery and Web Mining Lab, Computer Engineering and Computer Science Department, University of Louisville, KY, USA

    Behnoush Abdollahi & Olfa Nasraoui

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  1. Behnoush Abdollahi
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  2. Olfa Nasraoui
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Correspondence to Olfa Nasraoui .

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Editors and Affiliations

  1. DATA61, CSIRO, Eveleigh, New South Wales, Australia

    Jianlong Zhou

  2. DATA61, CSIRO, Eveleigh, New South Wales, Australia

    Fang Chen

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Abdollahi, B., Nasraoui, O. (2018). Transparency in Fair Machine Learning: the Case of Explainable Recommender Systems. In: Zhou, J., Chen, F. (eds) Human and Machine Learning. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-90403-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-90403-0_2

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