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Ricardo Baeza-Yates ( rbaeza@acm.org) is Chief Technology Officer of NTENT, a search technology company based in Carlsbad, CA, USA, and Director of Computer Science Programs at Northeastern University, Silicon Valley campus, San Jose, CA, USA.

Copyright held by owner/author.

Publication rights licensed to ACM. $15.00.

user. If a personalization algorithm uses only our interaction data, we see only what we want to see, thus biasing the content to our own selection biases, keeping us in a closed world, closed off to new items we might actually like. This issue must be counteracted through collaborative filtering or task contextual-ization, as well as through diversity, novelty, serendipity, and even, if requested, giving us the other side. This has a positive effect on online privacy because, by incorporating such techniques, less personal information is required.

Conclusion

The problem of bias is much more complex than I have outlined here, where I have covered only part of the problem. Indeed, the foundation involves all of our personal biases. On the contrary, many of the biases described here manifest beyond the Web ecosystem (such as in mobile devices and the Internet of Things). The table here aims to classify all the main biases against the three types of bias I mentioned earlier. We can group them in three clusters: The top one involves just algorithms; the bottom one—activity, user interaction, and self-selection—involves those that come just from people; and the middle one—data and second-order—includes those involving both. The question marks in the first line indicate that each program probably encodes the cultural and cognitive biases of their creators. One antecedent to support this claim is an interesting data-analysis experiment where 29 teams in a worldwide crowd-sourcing challenge performed a statistical analysis for a problem involving racial discrimination. 3

In early 2017, US-ACM published
the seven properties algorithms must
fulfill to achieve transparency and ac-
countability: 1 awareness, access and
redress, accountability, explanation,
data provenance, auditability, and
validation and testing. This article is
most closely aligned with awareness.
In addition, the IEEE Computer Soci-
ety also in 2017 began a project to de-
fine standards in this area, and at least
two new conferences on the topic were
held in February 2018. My colleagues
and I are also working on a website
with resources on “fairness measures”
related to algorithms (http://fairness-
measures.org/), and there are surely
other such initiatives. All of them
should help us define the ethics of al-
gorithms, particularly with respect to
machine learning.

As any attempt to be unbiased might already be biased through our own cultural and cognitive biases, the first step is thus to be aware of bias. Only if Web designers and developers know its existence can they address, and if possible, correct them. Otherwise, our future could be a fictitious world based on biased perceptions from which not even diversity, novelty, or serendipity would be able to rescue us.

Acknowledgments

I thank Jeanna Matthews, Leila Zia, and the anonymous reviewers for their helpful comments, as well as for Amanda Hirsch for her earlier English revision.

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