Recommender systems and their ethical challenges

By S. Milano, Mariarosaria Taddeo, Luciano Floridi. 2020

417 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Safety Research, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jonathan Stray
2. Iason Gabriel
3. Po-Sen Huang
4. Jonathan Uesato
5. William S. Isaac
6. John F. J. Mellor
7. Borja Balle
8. Patrick Hall

In Information Systems

1. William S. Isaac
2. Iason Gabriel
3. Borja Balle
4. Patrick Hall
5. Myra Cheng
6. Po-Sen Huang
7. Jonathan Stray
8. Atoosa Kasirzadeh

In Safety Research

1. Jonathan Stray
2. William S. Isaac
3. Iason Gabriel
4. Patrick Hall
5. Geoffrey Irving
6. Myra Cheng
7. T. Stepleton
8. Zachary Kenton

In Safety, Robustness & Interpretability

1. Iason Gabriel
2. Jonathan Stray
3. Po-Sen Huang
4. William S. Isaac
5. John F. J. Mellor
6. Borja Balle
7. Patrick Hall
8. Geoffrey Irving

Citations in the corpus

• Ethical and social risks of harm from Language Models (2021, background): “Different ethical and social risks have been identified in relation to existing recommender systems, including polarisation of users and reduced user well-being (Milano et al., 2020).”
• Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (2022, background): “For example, the ML systems that underlie AI applications often model our implicit biases with the intent of creating positive experiences for online shopping or identifying content of interest [65, 66].”
• Six Human-Centered Artificial Intelligence Grand Challenges (2023, neutral): “The recommender system is essential to the success of the social media website which raises a question regarding the privacy of monitored individuals and other ethical challenges (Milano et al., 2020).”
• Aligning AI Optimization to Community Well-Being (2020, background): “These challenges of collective action have been explored in the context of AI systems from the perspective of social choice theory (Baum 2020) and multi-stakeholder recommendation systems (Milano et al. 2019a).”