---
res:
  bibo_abstract:
  - A machine-learned system that is fair in static decision-making tasks may have
    biased societal impacts in the long-run. This may happen when the system interacts
    with humans and feedback patterns emerge, reinforcing old biases in the system
    and creating new biases. While existing works try to identify and mitigate long-run
    biases through smart system design, we introduce techniques for monitoring fairness
    in real time. Our goal is to build and deploy a monitor that will continuously
    observe a long sequence of events generated by the system in the wild, and will
    output, with each event, a verdict on how fair the system is at the current point
    in time. The advantages of monitoring are two-fold. Firstly, fairness is evaluated
    at run-time, which is important because unfair behaviors may not be eliminated
    a priori, at design-time, due to partial knowledge about the system and the environment,
    as well as uncertainties and dynamic changes in the system and the environment,
    such as the unpredictability of human behavior. Secondly, monitors are by design
    oblivious to how the monitored system is constructed, which makes them suitable
    to be used as trusted third-party fairness watchdogs. They function as computationally
    lightweight statistical estimators, and their correctness proofs rely on the rigorous
    analysis of the stochastic process that models the assumptions about the underlying
    dynamics of the system. We show, both in theory and experiments, how monitors
    can warn us (1) if a bank’s credit policy over time has created an unfair distribution
    of credit scores among the population, and (2) if a resource allocator’s allocation
    policy over time has made unfair allocations. Our experiments demonstrate that
    the monitors introduce very low overhead. We believe that runtime monitoring is
    an important and mathematically rigorous new addition to the fairness toolbox.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Thomas A
      foaf_name: Henzinger, Thomas A
      foaf_surname: Henzinger
      foaf_workInfoHomepage: http://www.librecat.org/personId=40876CD8-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-2985-7724
  - foaf_Person:
      foaf_givenName: Mahyar
      foaf_name: Karimi, Mahyar
      foaf_surname: Karimi
      foaf_workInfoHomepage: http://www.librecat.org/personId=6e5417ba-5355-11ee-ae5a-94c2e510b26b
    orcid: 0009-0005-0820-1696
  - foaf_Person:
      foaf_givenName: Konstantin
      foaf_name: Kueffner, Konstantin
      foaf_surname: Kueffner
      foaf_workInfoHomepage: http://www.librecat.org/personId=8121a2d0-dc85-11ea-9058-af578f3b4515
    orcid: 0000-0001-8974-2542
  - foaf_Person:
      foaf_givenName: Kaushik
      foaf_name: Mallik, Kaushik
      foaf_surname: Mallik
      foaf_workInfoHomepage: http://www.librecat.org/personId=0834ff3c-6d72-11ec-94e0-b5b0a4fb8598
    orcid: 0000-0001-9864-7475
  bibo_doi: 10.1145/3593013.3594028
  dct_date: 2023^xs_gYear
  dct_identifier:
  - UT:001062819300057
  dct_isPartOf:
  - http://id.crossref.org/issn/9781450372527
  dct_language: eng
  dct_publisher: Association for Computing Machinery@
  dct_title: Runtime monitoring of dynamic fairness properties@
...