ACADEMIC ARTICLE SUMMARY
Algorithms and Decision-Making in the Public Sector
Article Source: Annual Review of Law and Social Science, Vol. 17, pp. 319-334, 2021
Publication Date:
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ARTICLE SUMMARY
Summary:
This article provides a road map for the study of algorithmic systems used by local governments in the United States, including issues relating to procurement, bias, transparency, and regulation.
POLICY RELEVANCE
Policy Relevance:
The design of algorithms used by government should support accountability.
KEY TAKEAWAYS
Key Takeaways:
- Increasingly, local governments within the United States use algorithmic systems to make decisions relating to criminal justice, benefits, and education; "algorithm" refers to technologies that use machine learning or programmed rules to inform or execute government actions.
- Municipalities are the leading provider of public services in the United States; vulnerable constituents, including people of color, are disproportionately affected by municipal algorithms.
- Algorithmic tools are increasingly central to local government.
- Local governments struggle to raise revenues, and algorithmic tools promise efficiency.
- Local government services are highly visible and involve critical decisions.
- Local governments struggle to raise revenues, and algorithmic tools promise efficiency.
- The availability of algorithms may focus attention only on aspects of the problem that algorithms can solve; for example, attention might shift from addressing the root causes of poverty to screening ineligible citizens out of benefit applications.
- The integration of data collected by different entities (such as education and law enforcement) can improve efficiency and access to services, but raises the prospect of far-reaching surveillance.
- Governments often acquire algorithmic tools from commercial vendors, leaving it unclear which entity is responsible for bad outcomes; systems that affect rights, values, and social outcomes should be subjected to close scrutiny in the procurement process.
- Transparency and accountability of algorithmic systems may be proactive (by design, as required by law) or reactive (a result of information requests after a problem has developed).
- Algorithmic impact assessments call for analysis before a system is developed.
- Citizen groups support accountability and oversight.
- Algorithmic impact assessments call for analysis before a system is developed.
- Government workers may have discretion to accept or reject an algorithms’ output, either correcting or introducing bias; workers resistant to algorithmic decisions may ignore recommendations or obfuscate data.
- Algorithmic systems need to be repeatedly evaluated, to ensure that bias or a shift in function over time is detected and addressed.