Algorithmic Decision-Making, Fairness, and the Distribution of Impact: Application to Refugee Matching
Kirk Bansak () and
Linna Martén ()
Additional contact information
Kirk Bansak: University of California, Berkeley
Linna Martén: Swedish Institute for Social Research, Postal: SOFI, Stockholm University, SE-106 91 Stockholm, Sweden
No 6/2024, SOFI Working Papers in Labour Economics from Stockholm University, Swedish Institute for Social Research
Abstract:
This paper proposes an approach to evaluating the group-level fairness of an algorithmic decision-making system on the basis of the distribution of causal impact, with an application to a new area of algorithmic decision-making in public policy that has received little attention in the algorithmic fairness literature: the geographic assignment of refugees within host countries. The approach formalizes the algorithmic assignment procedure and causal impact using the potential outcomes framework, and it offers flexibility to accommodate a wide range of use cases. Specifically, it is flexible in allowing for the consideration of outcomes of different types (continuous or discrete), impact on multiple outcomes of interest, any number of policy options to which units can be assigned (extending beyond binary decisions), and various ways in which predictions map to actual decisions. The paper illustrates the approach, as well as highlights the limits of conventional fairness perspectives, with an application to the geographic assignment of refugee. Real-world data on refugees in Sweden are used to evaluate the implications if refugees were algorithmically assigned to labor market regions to improve their employment outcomes, compared to the quasi-random status quo assignment, focusing particularly on fairness of the impact across gender. In addition to considering the algorithmic target outcome (i.e. employment), the proposed framework also facilitates evaluation of unintended impacts on “cross-outcomes” (e.g. skill development) and their implications for fairness.
Keywords: algorithmic fairness; causal inference; refugee matching; refugee resettlement (search for similar items in EconPapers)
JEL-codes: J61 (search for similar items in EconPapers)
Pages: 13 pages
Date: 2024-12-07
New Economics Papers: this item is included in nep-ain and nep-mig
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://diva-portal.org/smash/get/diva2:1921508/FULLTEXT01.pdf Full text (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:hhs:sofile:2024_006
Access Statistics for this paper
More papers in SOFI Working Papers in Labour Economics from Stockholm University, Swedish Institute for Social Research SOFI, Stockholm University, SE-106 91 Stockholm, Sweden. Contact information at EDIRC.
Bibliographic data for series maintained by Lucas Tilley ( this e-mail address is bad, please contact ).