Best Practices for Transparency in Machine Generated Personalization

Laura Schelenz · Avi Segal · Kobi Gal

15 citations0/0 files storedACM Conference on User Modeling, Adaptation and Personalization (UMAP)

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Ethics and Social Impacts of AI Recommender Systems and Techniques Explainable Artificial Intelligence (XAI)

Abstract

Machine generated personalization is increasingly used in online systems. Personalization is intended to provide users with relevant content, products, and solutions that address their respective needs and preferences. However, users are becoming increasingly vulnerable to online manipulation due to algorithmic advancements and lack of transparency. Such manipulation decreases users' levels of trust, autonomy, and satisfaction concerning the systems with which they interact. Increasing transparency is an important goal for personalization based systems and system designers benefit from guidance in implementing transparency in their systems.

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References

2018AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations 2016The ethics of algorithms: Mapping the debate 2018Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification 2016How the machine ‘thinks’: Understanding opacity in machine learning algorithms 2016Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability 2017Fair, Transparent, and Accountable Algorithmic Decision-making Processes 2018Explaining the privacy paradox: A systematic review of literature investigating privacy attitude and behavior 2009The ethics of information transparency

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