%0 Journal Article %1 Bozdag:2013:BAF:2560622.2560640 %A Bozdag, Engin %C Hingham, MA, USA %D 2013 %I Kluwer Academic Publishers %J Ethics and Inf. Technol. %K AI accountability bias filtering %N 3 %P 209--227 %R 10.1007/s10676-013-9321-6 %T Bias in Algorithmic Filtering and Personalization %U http://dx.doi.org/10.1007/s10676-013-9321-6 %V 15 %X 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.

@article{Bozdag:2013:BAF:2560622.2560640, abstract = {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.}, acmid = {2560640}, added-at = {2016-10-01T10:41:54.000+0200}, address = {Hingham, MA, USA}, author = {Bozdag, Engin}, biburl = {https://www.bibsonomy.org/bibtex/2f366aea1e532088fe9aeda1a150181bd/mbockholt}, description = {Bias in algorithmic filtering and personalization}, doi = {10.1007/s10676-013-9321-6}, interhash = {d43e2080a10ddf38d7effef3e399a8f9}, intrahash = {f366aea1e532088fe9aeda1a150181bd}, issn = {1388-1957}, issue_date = {September 2013}, journal = {Ethics and Inf. Technol.}, keywords = {AI accountability bias filtering}, month = sep, number = 3, numpages = {19}, pages = {209--227}, publisher = {Kluwer Academic Publishers}, timestamp = {2016-10-01T10:41:54.000+0200}, title = {Bias in Algorithmic Filtering and Personalization}, url = {http://dx.doi.org/10.1007/s10676-013-9321-6}, volume = 15, year = 2013 }