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Sunday, January 12, 2020

Bias in algorithmic filtering and personalization

Engin Bozdag
Ethics Inf Technol (2013) 15: 209.


Online information intermediaries such as Facebook and Google are slowly replacing traditional media channels thereby partly becoming the gatekeepers of our society. To deal with the growing amount of information on the social web and the burden it brings on the average user, these gatekeepers recently started to introduce personalization features, algorithms that filter information per individual. In this paper we show that these online services that filter information are not merely algorithms. Humans not only affect the design of the algorithms, but they also can manually influence the filtering process even when the algorithm is operational. We further analyze filtering processes in detail, show how personalization connects to other filtering techniques, and show that both human and technical biases are present in today’s emergent gatekeepers. We use the existing literature on gatekeeping and search engine bias and provide a model of algorithmic gatekeeping.

From the Discussion:

Today information seeking services can use interpersonal contacts of users in order to tailor information and to increase relevancy. This not only introduces bias as our model shows, but it also has serious implications for other human values, including user autonomy, transparency, objectivity, serendipity, privacy and trust. These values introduce ethical questions. Do private companies that are
offering information services have a social responsibility, and should they be regulated? Should they aim to promote values that the traditional media was adhering to, such as transparency, accountability and answerability? How can a value such as transparency be promoted in an algorithm?  How should we balance between autonomy and serendipity and between explicit and implicit personalization? How should we define serendipity? Should relevancy be defined as what is popular in a given location or by what our primary groups find interesting? Can algorithms truly replace human filterers?

The info can be downloaded here.

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