Algorithmic bias: Difference between revisions

Revision as of 03:43, 16 March 2025

Revision as of 03:46, 16 March 2025

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{{further|Fairness (machine learning)}}

There have been several attempts to create methods and tools that can detect and observe biases within an algorithm. These emergent fields focus on tools which are typically applied to the (training) data used by the program rather than the algorithm's internal processes. These methods may also analyze a program's output and its usefulness and therefore may involve the analysis of its [[confusion matrix]] (or table of confusion).<ref>{{cite web|url=https://research.google.com/bigpicture/attacking-discrimination-in-ml/|title=Attacking discrimination with smarter machine learning|first1=Martin|last1=Wattenberg|first2=Fernanda|last2=Viégas|first3=Moritz|last3=Hardt|publisher=Google Research}}</ref><ref>{{cite arXiv |eprint=1610.02413|last1=Hardt|first1=Moritz|title=Equality of Opportunity in Supervised Learning|last2=Price|first2=Eric|last3=Srebro|first3=Nathan|class=cs.LG|year=2016}}</ref><ref>{{cite web|url=https://venturebeat.com/2018/05/25/microsoft-is-developing-a-tool-to-help-engineers-catch-bias-in-algorithms/|title=Microsoft is developing a tool to help engineers catch bias in algorithms|date=2018-05-25|first=Kyle|last=Wiggers|website=VentureBeat.com}}</ref><ref>{{cite web |title=Facebook says it has a tool to detect bias in its artificial intelligence |date=2018-05-03 |website=[[Quartz (publication)|Quartz]] |archive-url=https://web.archive.org/web/20230305194710/https://qz.com/1268520/facebook-says-it-has-a-tool-to-detect-bias-in-its-artificial-intelligence |archive-date=2023-03-05 |url-status=live |url=https://qz.com/1268520/facebook-says-it-has-a-tool-to-detect-bias-in-its-artificial-intelligence/}}</ref><ref>[https://github.com/pymetrics/audit-ai open source] Pymetrics audit-ai</ref><ref>{{cite web|url=https://venturebeat-com.cdn.ampproject.org/c/s/venturebeat.com/2018/05/31/pymetrics-open-sources-audit-ai-an-algorithm-bias-detection-tool/amp/ |title=Pymetrics open-sources Audit AI, an algorithm bias detection tool|date=2018-05-31|first=Khari|last=Johnson|website=VentureBeat.com}}</ref><ref>https://github.com/dssg/aequitas open source Aequitas: Bias and Fairness Audit Toolkit</ref><ref>https://dsapp.uchicago.edu/aequitas/ open-sources Audit AI, Aequitas at University of Chicago</ref><ref>https://www.ibm.com/blogs/research/2018/02/mitigating-bias-ai-models/ Mitigating Bias in AI Models</ref> Explainable AI to detect algorithm Bias is a suggested way to detect the existence of bias in an algorithm or learning model.<ref>S. Sen, D. Dasgupta and K. D. Gupta, "An Empirical Study on Algorithmic Bias", 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain, 2020, pp. 1189-1194, {{doi|10.1109/COMPSAC48688.2020.00-95}}.</ref> Using machine learning to detect bias is called, "conducting an AI audit", where the "auditor" is an algorithm that goes through the AI model and the training data to identify biases.<ref>{{Cite journal|last1=Zou|first1=James|last2=Schiebinger|first2=Londa|date=July 2018|title=AI can be sexist and racist — it's time to make it fair |journal=Nature|language=en|volume=559|issue=7714|pages=324–326|doi=10.1038/d41586-018-05707-8|pmid=30018439|bibcode=2018Natur.559..324Z|doi-access=free}}</ref>

Ensuring that an AI tool such as a classifier is free from bias is more difficult than just removing the sensitive information

from its input signals, because this is typically implicit in other signals. For example, the hobbies, sports and schools attended