Computing Reviews
Today's Issue Hot Topics Search Browse Recommended My Account Log In
Review Help
Search
Algorithmic poverty
Kirkpatrick K. Communications of the ACM64 (10):11-12,2021.Type:Article
Date Reviewed: Nov 22 2022

The lack of transparency in algorithms and the data they use is a matter of current interest. In addition to a nontransparent process, results are also nontransparent, lacking explanation or justification. These problems lead to unfairness and bias, as illustrated by Amazon’s algorithmic resume screener that discriminated against women. Federal action has stalled though there are regulatory guidelines on fairness and nondiscrimination in artificial intelligence (AI) application outcomes.

Corporations can address these matters by implementing best practices in their algorithmic application areas. But Amazon has shown best practices may embody unfairness and bias. Relying on diversity in development and metrics can help expose and eliminate these problems, as well as improve inclusiveness by widening the potential market. While some corporations are leading, others are lagging, particularly those with large, embedded, and long-standing systems that may incorporate algorithmic and information bias. Dealing with corporate lag is another significant problem.

Ultimately, bias is a human problem independent of mechanisms realizing bias. Dealing with bias requires clear-eyed self-reflection by being observant and thoughtful about relevant histories. In this, algorithms will always be a lagging reaction to human activity.

This short article lays out these matters and the attempts at dealing with them. Although philosophical considerations appear in the first paragraph, the article is practical, which is useful but unfortunate. The thinking around algorithmic transparency is fuzzy and unclear, and it is hard to see how large-scale solutions are possible without clarity and agreement on what the problems are.

Reviewer:  R. Clayton Review #: CR147515
Bookmark and Share
Would you recommend this review?
yes
no
Other reviews under "Algorithm Design And Analysis": Date
 Adaptive Winograd’s matrix multiplications
D’Alberto P., Nicolau A. ACM Transactions on Mathematical Software 36(1): 1-23, 2009. Type: Article
Jul 2 2009
On factorizing the symbolic U-resultant
Murao H., Kobayashi H., Fujise T. Journal of Symbolic Computation 15(2): 123-142, 1993. Type: Article
Sep 1 1994
Maximizing polynomials subject to assignment constraints
Makarychev K., Sviridenko M. ACM Transactions on Algorithms 13(4): 1-15, 2017. Type: Article
Jul 13 2018
more...

E-Mail This Printer-Friendly
Send Your Comments
Contact Us
Reproduction in whole or in part without permission is prohibited.   Copyright 2004 Reviews.com™
Terms of Use
| Privacy Policy