Maximal Transparency for Online Recommender Systems

Online recommender systems, such as those found on newsfeeds or e-commerce websites, give users options specifically tailored for them, and nudge users toward certain options and away from others. Transparency in such systems matters. One reason is that platforms may deploy user data for certain ends, beyond making recommendations, that users arguably have a right to know about. Another is that such systems can encode biases favoring or disadvantaging certain stakeholders. Transparency exposes these biases and other forms of potential unfairness. But what is transparency in the context of recommender systems? This question is harder than meets the eye, given the complexity of online platforms, of the variables that determine the workings of recommender-system algorithms, and of the world itself that these systems distill into data and inferences. We give an account of maximal transparency – what transparency looks like supposing that technical implementation is no obstacle. Maximal transparency itself has many dimensions that admit of degree. This paper makes an indirect contribution to the ethics of transparency for online recommender systems – not because we argue that maximal transparency is ethically desirable (it almost certainly isn’t), but because we perform the logically prior task of clarifying what it is to begin with. This gives us a limiting case of transparency along multiple dimensions, providing multiple choice points from which an ethics of transparency can be built. We also identify stakeholders whose needs would play a role in determining which dimensions and degrees of transparency would be ethically optimal.