TY - GEN
T1 - What is the Bureaucratic Counterfactual? Categorical versus Algorithmic Prioritization in U.S. Social Policy
AU - Johnson, Rebecca Ann
AU - Zhang, Simone
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/6/21
Y1 - 2022/6/21
N2 - There is growing concern about governments' use of algorithms to make high-stakes decisions. While an early wave of research focused on algorithms that predict risk to allocate punishment and suspicion, a newer wave of research studies algorithms that predict "need"or "benefit"to target beneficial resources, such as ranking those experiencing homelessness by their need for housing. The present paper argues that existing research on the role of algorithms in social policy could benefit from a counterfactual perspective that asks: given that a social service bureaucracy needs to make some decision about whom to help, what status quo prioritization method would algorithms replace? While a large body of research contrasts human versus algorithmic decision-making, social service bureaucracies target help not by giving street-level bureaucrats full discretion. Instead, they primarily target help through pre-algorithmic, rule-based methods. In this paper, we outline social policy's current status quo method - categorical prioritization - where decision-makers manually (1) decide which attributes of help seekers should give those help seekers priority, (2) simplify any continuous measures of need into categories (e.g., household income falls below a threshold), and (3) manually choose the decision rules that map categories to priority levels. We draw on novel data and quantitative and qualitative social science methods to outline categorical prioritization in two case studies of United States social policy: waitlists for scarce housing vouchers and K-12 school finance formulas. We outline three main differences between categorical and algorithmic prioritization: is the basis for prioritization formalized; what role does power play in prioritization; and are decision rules for priority manually chosen or inductively derived from a predictive model. Concluding, we show how the counterfactual perspective underscores both the understudied costs of categorical prioritization in social policy and the understudied potential of predictive algorithms to narrow inequalities.
AB - There is growing concern about governments' use of algorithms to make high-stakes decisions. While an early wave of research focused on algorithms that predict risk to allocate punishment and suspicion, a newer wave of research studies algorithms that predict "need"or "benefit"to target beneficial resources, such as ranking those experiencing homelessness by their need for housing. The present paper argues that existing research on the role of algorithms in social policy could benefit from a counterfactual perspective that asks: given that a social service bureaucracy needs to make some decision about whom to help, what status quo prioritization method would algorithms replace? While a large body of research contrasts human versus algorithmic decision-making, social service bureaucracies target help not by giving street-level bureaucrats full discretion. Instead, they primarily target help through pre-algorithmic, rule-based methods. In this paper, we outline social policy's current status quo method - categorical prioritization - where decision-makers manually (1) decide which attributes of help seekers should give those help seekers priority, (2) simplify any continuous measures of need into categories (e.g., household income falls below a threshold), and (3) manually choose the decision rules that map categories to priority levels. We draw on novel data and quantitative and qualitative social science methods to outline categorical prioritization in two case studies of United States social policy: waitlists for scarce housing vouchers and K-12 school finance formulas. We outline three main differences between categorical and algorithmic prioritization: is the basis for prioritization formalized; what role does power play in prioritization; and are decision rules for priority manually chosen or inductively derived from a predictive model. Concluding, we show how the counterfactual perspective underscores both the understudied costs of categorical prioritization in social policy and the understudied potential of predictive algorithms to narrow inequalities.
KW - fairness and transparency
KW - resource allocation
KW - social policy
UR - http://www.scopus.com/inward/record.url?scp=85132982731&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85132982731&partnerID=8YFLogxK
U2 - 10.1145/3531146.3533223
DO - 10.1145/3531146.3533223
M3 - Conference contribution
AN - SCOPUS:85132982731
T3 - ACM International Conference Proceeding Series
SP - 1671
EP - 1682
BT - Proceedings of 2022 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022
PB - Association for Computing Machinery
T2 - 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022
Y2 - 21 June 2022 through 24 June 2022
ER -