Proceedings of the Conference on Fairness, Accountability, and Transparency 2019
DOI: 10.1145/3287560.3287565
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Who's the Guinea Pig?

Abstract: A/B/n testing has been adopted by many technology companies as a data-driven approach to product design and optimization. These tests are often run on their websites without explicit consent from users. In this paper, we investigate such online A/B/n tests by using Optimizely as a lens. First, we provide measurement results of 575 websites that use Optimizely drawn from the Alexa Top-1M, and analyze the distributions of their audiences and experiments. Then, we use three case studies to discuss potential ethic… Show more

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Cited by 8 publications
(1 citation statement)
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References 47 publications
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“…Both in academic and industrial literature, online controlled experiments have been widely commented and discussed (Govind, 2017;Kohavi et al, 2009;Lazarova, 2020;Ros and Runeson, 2018;Tang et al, 2010;Xu et al, 2015). However, some authors claim that this method, as traditionally used, is not suitable for the purpose of evaluating personalization (Chen et al, 2019;Das and Ranganath, 2013;Jiang et al, 2019) and propose adapted A/B testing techniques or alternative new methods (Athey and Imbens, 2016;Haupt et al, 2020;Pouget-Abadie et al, 2018;Tu et al, 2021). On the other hand, it is not always possible to run A/B tests as they might be expensive, time-consuming, at times unethical or even not feasible (e.g.…”
Section: Introductionmentioning
confidence: 99%
“…Both in academic and industrial literature, online controlled experiments have been widely commented and discussed (Govind, 2017;Kohavi et al, 2009;Lazarova, 2020;Ros and Runeson, 2018;Tang et al, 2010;Xu et al, 2015). However, some authors claim that this method, as traditionally used, is not suitable for the purpose of evaluating personalization (Chen et al, 2019;Das and Ranganath, 2013;Jiang et al, 2019) and propose adapted A/B testing techniques or alternative new methods (Athey and Imbens, 2016;Haupt et al, 2020;Pouget-Abadie et al, 2018;Tu et al, 2021). On the other hand, it is not always possible to run A/B tests as they might be expensive, time-consuming, at times unethical or even not feasible (e.g.…”
Section: Introductionmentioning
confidence: 99%