The Algorithm Audit: Scoring the Algorithms that Score Us

Big Data and Society (forthcoming)
Download Edit this record How to cite View on PhilPapers
Abstract
In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high-level to be put into practice without further guidance, or they focus on very specific notions of fairness or transparency that don’t consider multiple stakeholders or the broader social context. In this paper, we present an auditing framework to guide the ethical assessment of an algorithm. The audit model we propose starts by identifying the goals of the audit, and describing the context of the algorithm. The audit instrument itself is comprised of three elements: a list of possible interests of stakeholders affected by the algorithm, an assessment of metrics that describe key ethically salient features of the algorithm, and a relevancy matrix that connects the assessed metrics to stakeholder interests. The proposed audit instrument yields an ethical evaluation of an algorithm that could be used by regulators and others interested in doing due diligence.
PhilPapers/Archive ID
DAVTAA-21
Upload history
First archival date: 2020-12-05
Latest version: 3 (2021-01-28)
View other versions
Added to PP index
2020-12-05

Total views
101 ( #38,818 of 57,047 )

Recent downloads (6 months)
101 ( #5,848 of 57,047 )

How can I increase my downloads?

Downloads since first upload
This graph includes both downloads from PhilArchive and clicks on external links on PhilPapers.