Recommender systems and their ethical challenges
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Recommender systems and their ethical challenges Silvia Milano1 · Mariarosaria Taddeo1,2 · Luciano Floridi1,2 Received: 13 October 2019 / Accepted: 24 January 2020 © The Author(s) 2020
Abstract This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review. The article identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the literature: currently user-centred approaches do not consider the interests of a variety of other stakeholders—as opposed to just the receivers of a recommendation—in assessing the ethical impacts of a recommender system. Keywords Algorithms · Artificial intelligence · Digital ethics · Ethical trade-offs · Ethics of recommendation · Machine learning · Recommender systems
1 Introduction We interact with recommender (or recommendation) systems (RS) on a regular basis, when we use digital services and apps, from Amazon to Netflix and news aggregators. They are algorithms that make suggestions about what a user may like, such as a specific movie. Slightly more formally, they are functions that take information about a user’s preferences (e.g. about movies) as an input, and output a prediction about the rating that a user would give of the items under evaluation (e.g., new movies available), and predict how they would rank a set of items individually or as a bundle. We shall say more about the nature of recommender systems in the following pages, but even this general description suffices to clarify that, to work effectively and efficiently, recommender systems collect, curate, and act upon vast amounts of personal data. Inevitably, they end up shaping individual experience of digital environments and social interactions (Burr et al. 2018; de Vries 2010; Karimi et al. 2018). RS are ubiquitous and there is already much technical research about how to develop ever more efficient systems * Silvia Milano [email protected] 1
Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford OX1 3JS, UK
The Alan Turing Institute, 96 Euston Road, London NW1 2DB, UK
2
(Adomavicius and Tuzhilin 2005; Jannach and Adomavicius 2016; Ricci et al. 2015). In the past 20 years, RS have been developed focusing mostly on business applications. Even if researchers often adopt a user-centred approach focusing on preference prediction, it is evident that the applications of RS have been driven by online commerce and services, where the emphasis has tended to be on commercial objectives. But RS have a wider impact on users and on society more broadly. After all, they shape user preferences and guide choices, both individually and socially. This impact is significant and deserves ethical scrutiny, not least because RS can also be deployed in contexts that are morally loaded, such as health care, lifestyle, insurance, and the labour market. Clearly, whatever the ethical issues may be, they need to be understood and addressed by ev
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