contributed articles

I M A G E B Y S V I A T L A N A S H E I N A

OUR INHERENT HUMAN tendency of favoring one thing or opinion over another is reflected in every aspect of our lives, creating both latent and overt biases toward everything we see, hear, and do. Any remedy for bias must start with awareness that bias exists; for example, most mature societies raise awareness of social bias through affirmative-action programs, and, while awareness alone does not completely alleviate the problem, it helps guide us toward a solution. Bias on the Web reflects both societal and internal biases within ourselves, emerging in subtler ways. This article aims to increase awareness of the potential effects imposed on us all through bias present in Web use and content. We must thus consider and account for it in the design of Web systems that truly address people’s needs.

Bias has been intrinsically embedded in culture and
history since the beginning of time. However, due to
the rise of digital data, it can now
spread faster than ever and reach
many more people. This has caused
bias in big data to become a trending
and controversial topic in recent years.
Minorities, especially, have felt the
harmful effects of data bias when pur-
suing life goals, with outcomes gov-
erned primarily by algorithms, from
mortgage loans to advertising person-
alization. 24 While the obstacles they
face remain an important roadblock,
bias affects us all, though much of the
time we are unaware it exists or how it
might (negatively) influence our judg-
ment and behavior.

The Web is today’s most prominent communication channel, as well as a place where our biases converge. As social media are increasingly central to daily life, they expose us to influencers we might not have encountered previously. This makes understanding and recognizing bias on the Web more essential than ever. My main goal here is thus to raise the awareness level for all Web biases. Bias awareness would help us design better Web-based systems, as well as software systems in general.

Measuring Bias

The first challenge in addressing bias is how to define and measure it. From a statistical point of view, bias is a systemic deviation caused by an inaccurate estimation or sampling process. As a result, the distribution of a variable could be biased with respect to the original, possibly unknown, distribution. In addition, cultural biases can be found in our inclinations to our shared personal beliefs, while cognitive biases affect our behavior and the ways we make decisions.

Figure 1 shows how bias influences

Bias on

the Web
DOI: 10.1145/3209581

Bias in Web data and use taints the algorithms behind Web-based applications, delivering equally biased results.

BY RICARDO BAEZA-YATES

key insights

˽ Any remedy for bias starts with awareness of its existence.

˽ Bias on the Web reflects biases within ourselves, manifested in subtler ways.

˽ We must consider and account for bias in the design of Web-based systems that truly address the needs of users.