ACM Article Examines Bias on the Web

An interesting and considerable look at many kinds of bias and how they are ultimately combined in algorithmic bias used in 'AI'  in subtle ways.   Good starting point.   Excerpt:

Bias on the Web  By Ricardo Baeza-Yates 
Communications of the ACM, Vol. 61 No. 6, Pages 54-61
10.1145/3209581

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. ....
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 accountability: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 Society also in 2017 began a project to define 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 algorithms, 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. .... "