Problems with audits for bias in AI systems highlighted in research paper

Sasha Costanza-Chock, co-author of a research paper which looks into algorithmic audits, says there are many areas that require improvement in order to bolster the effectiveness of the process and reduce harms from bias in AI used in the real world, like facial recognition systems. Speaking about the Algorithmic Justice League paper on a recent episode of technology news podcast Marketplace, Costanza-Chock posits that it is very difficult to determine the effectiveness of algorithmic audits in the current dispensation because of non-disclosure agreements that bind first and second party auditors who have more access to the data and systems of companies they are auditing. Bias has been found in algorithms not only related to biometric matching, but adjacent areas like liveness detection, as well as unrelated AI applications. While putting together the research paper, which identifies emerging best practices as well as methods and tools for AI audits, the teams found out that a number of variations exist in the algorithmic auditing process as there is no harmonized standard or regulation on what auditors should look out for, said the co-author. While some of the audits focus on accuracy or fairness of training and sample data, some look at the privacy and security implications of the systems under audit, and only about half of the auditors they spoke to said they check to find out if companies have quality systems to enable users to channel complaints of AI bias harms in real-time.