---
_id: '13228'
abstract:
- lang: eng
  text: 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.
acknowledgement: 'The authors would like to thank the anonymous reviewers for their
  valuable comments and helpful suggestions. This work is supported by the European
  Research Council under Grant No.: ERC-2020-AdG 101020093.'
article_processing_charge: Yes (via OA deal)
arxiv: 1
author:
- first_name: Thomas A
  full_name: Henzinger, Thomas A
  id: 40876CD8-F248-11E8-B48F-1D18A9856A87
  last_name: Henzinger
  orcid: 0000-0002-2985-7724
- first_name: Mahyar
  full_name: Karimi, Mahyar
  id: 6e5417ba-5355-11ee-ae5a-94c2e510b26b
  last_name: Karimi
  orcid: 0009-0005-0820-1696
- first_name: Konstantin
  full_name: Kueffner, Konstantin
  id: 8121a2d0-dc85-11ea-9058-af578f3b4515
  last_name: Kueffner
  orcid: 0000-0001-8974-2542
- first_name: Kaushik
  full_name: Mallik, Kaushik
  id: 0834ff3c-6d72-11ec-94e0-b5b0a4fb8598
  last_name: Mallik
  orcid: 0000-0001-9864-7475
citation:
  ama: 'Henzinger TA, Karimi M, Kueffner K, Mallik K. Runtime monitoring of dynamic
    fairness properties. In: <i>FAccT ’23: Proceedings of the 2023 ACM Conference
    on Fairness, Accountability, and Transparency</i>. Association for Computing Machinery;
    2023:604-614. doi:<a href="https://doi.org/10.1145/3593013.3594028">10.1145/3593013.3594028</a>'
  apa: 'Henzinger, T. A., Karimi, M., Kueffner, K., &#38; Mallik, K. (2023). Runtime
    monitoring of dynamic fairness properties. In <i>FAccT ’23: Proceedings of the
    2023 ACM Conference on Fairness, Accountability, and Transparency</i> (pp. 604–614).
    Chicago, IL, United States: Association for Computing Machinery. <a href="https://doi.org/10.1145/3593013.3594028">https://doi.org/10.1145/3593013.3594028</a>'
  chicago: 'Henzinger, Thomas A, Mahyar Karimi, Konstantin Kueffner, and Kaushik Mallik.
    “Runtime Monitoring of Dynamic Fairness Properties.” In <i>FAccT ’23: Proceedings
    of the 2023 ACM Conference on Fairness, Accountability, and Transparency</i>,
    604–14. Association for Computing Machinery, 2023. <a href="https://doi.org/10.1145/3593013.3594028">https://doi.org/10.1145/3593013.3594028</a>.'
  ieee: 'T. A. Henzinger, M. Karimi, K. Kueffner, and K. Mallik, “Runtime monitoring
    of dynamic fairness properties,” in <i>FAccT ’23: Proceedings of the 2023 ACM
    Conference on Fairness, Accountability, and Transparency</i>, Chicago, IL, United
    States, 2023, pp. 604–614.'
  ista: 'Henzinger TA, Karimi M, Kueffner K, Mallik K. 2023. Runtime monitoring of
    dynamic fairness properties. FAccT ’23: Proceedings of the 2023 ACM Conference
    on Fairness, Accountability, and Transparency. FAccT: Conference on Fairness,
    Accountability and Transparency, 604–614.'
  mla: 'Henzinger, Thomas A., et al. “Runtime Monitoring of Dynamic Fairness Properties.”
    <i>FAccT ’23: Proceedings of the 2023 ACM Conference on Fairness, Accountability,
    and Transparency</i>, Association for Computing Machinery, 2023, pp. 604–14, doi:<a
    href="https://doi.org/10.1145/3593013.3594028">10.1145/3593013.3594028</a>.'
  short: 'T.A. Henzinger, M. Karimi, K. Kueffner, K. Mallik, in:, FAccT ’23: Proceedings
    of the 2023 ACM Conference on Fairness, Accountability, and Transparency, Association
    for Computing Machinery, 2023, pp. 604–614.'
conference:
  end_date: 2023-06-15
  location: Chicago, IL, United States
  name: 'FAccT: Conference on Fairness, Accountability and Transparency'
  start_date: 2023-06-12
corr_author: '1'
date_created: 2023-07-16T22:01:09Z
date_published: 2023-06-12T00:00:00Z
date_updated: 2026-01-21T07:23:43Z
day: '12'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1145/3593013.3594028
ec_funded: 1
external_id:
  arxiv:
  - '2305.04699'
  isi:
  - '001062819300057'
file:
- access_level: open_access
  checksum: 96c759db9cdf94b81e37871a66a6ff48
  content_type: application/pdf
  creator: dernst
  date_created: 2023-07-18T07:43:10Z
  date_updated: 2023-07-18T07:43:10Z
  file_id: '13245'
  file_name: 2023_ACM_HenzingerT.pdf
  file_size: 4100596
  relation: main_file
  success: 1
file_date_updated: 2023-07-18T07:43:10Z
has_accepted_license: '1'
isi: 1
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
page: 604-614
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
  call_identifier: H2020
  grant_number: '101020093'
  name: Vigilant Algorithmic Monitoring of Software
publication: 'FAccT ''23: Proceedings of the 2023 ACM Conference on Fairness, Accountability,
  and Transparency'
publication_identifier:
  isbn:
  - '9781450372527'
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: Runtime monitoring of dynamic fairness properties
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...