Ethics of Personalized Information Filtering

Koene, Ansgar; Pérez, Esther; Carter, Christopher James; Statache, Ramona; Adolphs, Svenja; O’Malley, Claire; Rodden, Tom; McAuley, Derek

doi:10.1007/978-3-319-18609-2_10

Cited by 39 publications

(47 citation statements)

References 20 publications

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“…User behaviour profiles built for serving personalized recommendations may as well be utilised for malicious purposes, such as phishing or social engineering [7]. Moreover, disclosed user profiles may reveal sensitive private information.…”

Section: Towards Ethical Recommender Systemsmentioning

confidence: 99%

“…This time, we learn the lesson of ethics: two years after its public release, the Netflix dataset was de-anonymized via a linking attack [8], putting the privacy of 500,000 users at risk. This resulted in a lawsuit and put an end to the planned Netflix Prize sequel [7]. The problems of ethics in recommender systems certainly go far beyond anonymization and relate to areas such as data collection and filtering, algorithmic opacity and biases, behaviour manipulation, A/B testing, etc.…”

Section: Introduction: Echoes From the Netflix Prizementioning

confidence: 99%

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Recommender Systems from an Industrial and Ethical Perspective

Paraschakis

2016

Proceedings of the 10th ACM Conference on Recommender Systems

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Over the recent years, a plethora of recommender systems (RS) have been proposed by academics. The degree of adoptability of these algorithms by industrial e-commerce platforms remains unclear. To get an insight into real-world recommendation engines, we survey more than 30 existing shopping cart solutions and compare the performance of popular recommendation algorithms on proprietary e-commerce datasets. Our results show that deployed systems rarely go beyond trivial "best seller" lists or very basic personalized recommendation algorithms, which nevertheless exhibit superior performance to more elaborate techniques both in our experiments and other related studies. We also perform chronological dataset splits to demonstrate the importance of preserving the sequence of events during evaluation, and the recency of events during training. The second part of our research is still ongoing and focuses on various ethical challenges that complicate the design of recommender systems. We believe that this direction of research remains mostly neglected despite its increasing impact on RS' quality and safety.

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Section: Towards Ethical Recommender Systemsmentioning

confidence: 99%

Section: Introduction: Echoes From the Netflix Prizementioning

confidence: 99%

Recommender Systems from an Industrial and Ethical Perspective

Paraschakis

2016

Proceedings of the 10th ACM Conference on Recommender Systems

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“…Concerns over the lack of holistic approach have also been brought up by Friedman et al [65] who studied the privacy aspects of recommender systems -the subject that has drawn the most attention in the ethical discourse around the practices of big data. Another recent paper by Koene et al [107] points to the striking research imbalance in the area of personalized RS. The authors note that the strong emphasis on the commercial success of recommender systems contrasted with the considerable neglect of moral values, has a potential risk of a future public backlash against this research area.…”

Section: Future Workmentioning

confidence: 99%

“…Koene et al [107] point out that today's research practice in personalization and recommender systems is dangerously unbalanced, as it puts commercial success above considerations of the moral impact of this technology. The authors see it as a worrying sign that may eventually lead to the public backlash against this research area, similar to what happened to the Bioscience field in the 1990s because of the GMO crop controversy.…”

Section: From Algorithms To Ethicsmentioning

confidence: 99%

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Algorithmic and Ethical Aspects of Recommender Systems in e-Commerce

Paraschakis¹

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In memory of a beautiful soul ABSTRACTRecommender systems have become an integral part of virtually every e-commerce application on the web. The deployment of these expert systems has enabled users to quickly discover the products or services they need, at the same time increasing business revenues through better customer conversion. Remaining a very active research field since the mid-2000s, recommender systems have been modeled using a plethora of machine learning techniques. However, the adoptability of these models by industrial e-commerce platforms remains unclear.In this thesis, we assess the receptiveness of industrial platforms to algorithmic contributions of the research community by surveying more than 30 popular shopping cart solutions, and experimenting with various recommendation algorithms on proprietary e-commerce datasets.Another overlooked but important factor that complicates the design and use of recommender systems is their ethical implications. We provide a holistic view of these issues and summarize them in our ethical recommendation framework. This framework suggests new paradigm of ethics-awareness by design, and enables users to control sensitive moral aspects of recommendations via the proposed "ethical toolbox". The feasibility of this tool is supported by the results of our user study.Since the large part of moral implications stems from user profiling, we investigate algorithms capable of generating useragnostic recommendations based solely on a visited product page. We propose an ensemble learning scheme based on Thompson Sampling bandit policy, which models arms as base recommendation functions. We show how to adapt this algorithm to realistic situations when neither arm availability nor reward stationarity is guaranteed.

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