Communications of the ACM

Bias on User Interaction

One significant source of bias is user interaction, not only on the Web, but from two notable sources: the user interface and the user’s own self-se-lected, biased interaction. The first is “presentation bias,” whereby everything seen by the user can get clicks while everything else gets no clicks. This is particularly relevant in recommendation systems. Consider a video-streaming service in which users have hundreds of recommendations they can browse, though the number is abysmally small compared to the millions that could potentially be offered. This bias directly affects new items or items that have never been seen by users, as there is no usage data for them. The most common solution is called “explore and exploit,” as in Agarwal et al., 2 who studied a classical example applied to the Web. It exposes part of user traffic to new items randomly intermingled with top recommendations to explore and, if chosen, exploit usage data to reveal their true relative value. The paradox of such a solution is that exploration could imply a loss or an opportunity cost for exploiting information already known. In some cases, there is even a loss of revenue (such as from digital ads). However, the only way to learn and discover (new) good items is exploration.

“Position bias” is the second bias. Consider that in western cultures we read from top to bottom and left to right. The bias is thus to look first toward the top left corner of the screen, prompting that region to attract more eyes and clicks. “Ranking bias” is an important instance of such bias. Consider a Web search engine where results pages are listed in relevant order from top to bottom. The top-ranked result will thus attract more clicks than the others because it is both the most relevant and also ranked in the first position. To avoid ranking bias, Web developers need to de-bias click distribution so they can use click data to improve and evaluate ranking algorithms. 11, 12 Otherwise, the popular pages become even more popular.

Other biases in user interaction in-
clude those related to user-interaction
design; for example, any webpage
where a user needs to scroll to see ad-
ditional content will reflect bias like
mately 70% of influential journalists in
the U.S. were men, even though at U.S.
journalism schools, the gender propor-
tions are reversed. Algorithms learning
from news articles are thus learning
from texts with demonstrable and sys-
temic gender bias. Yet other research
has identified the presence of other
cultural and cognitive biases. 10, 22

On the other hand, some Web developers have been able to limit bias. “De-biasing” the gender-bias issue can be addressed by factoring in the gender subspace automatically. 9 Regarding geographical bias in news recommendations, large cities and centers of political power surely generate more news. If standard recommendation algorithms are used, the general public likely reads news from a capital city, not from the place where they live. Considering diversity and user location, Web designers can create websites that give a less centralized view that also shows local news. 15

“Tag recommendations,” or recommending labels or tags for items, is an extreme example of algorithmic bias. Imagine a user interface where a user uploads a photo and adds various tags, and a tag recommendation algorithm then suggests tags that people have used in other photos based on collaborative filtering. The user chooses the ones that seem correct, enlarging the set of tags. This sounds simple, but a photo-hosting website should not include such functionality. The reason is that the algorithm needs data from people to improve, but as people use recommended tags, they add fewer tags of their own, picking from among known tags while not adding new ones. In essence, the algorithm is doing prolonged hara-kiri on itself. If we have a “folksonomy,” or tags that come only from people, websites should not themselves recommend tags. On the other hand, many websites use this idea to provide the ability to search similar images through related tags.

Another critical class of algorithmic bias in recommender systems is related to what items the system chooses to show or not show on a particular web-page. Such bias affects user interaction, as explored next. There is ample research literature on all sorts of algorithmic bias; see the online appendix for more.