---
_id: '14454'
abstract:
- lang: eng
  text: As AI and machine-learned software are used increasingly for making decisions
    that affect humans, it is imperative that they remain fair and unbiased in their
    decisions. To complement design-time bias mitigation measures, runtime verification
    techniques have been introduced recently to monitor the algorithmic fairness of
    deployed systems. Previous monitoring techniques assume full observability of
    the states of the (unknown) monitored system. Moreover, they can monitor only
    fairness properties that are specified as arithmetic expressions over the probabilities
    of different events. In this work, we extend fairness monitoring to systems modeled
    as partially observed Markov chains (POMC), and to specifications containing arithmetic
    expressions over the expected values of numerical functions on event sequences.
    The only assumptions we make are that the underlying POMC is aperiodic and starts
    in the stationary distribution, with a bound on its mixing time being known. These
    assumptions enable us to estimate a given property for the entire distribution
    of possible executions of the monitored POMC, by observing only a single execution.
    Our monitors observe a long run of the system and, after each new observation,
    output updated PAC-estimates of how fair or biased the system is. The monitors
    are computationally lightweight and, using a prototype implementation, we demonstrate
    their effectiveness on several real-world examples.
acknowledgement: 'This work is supported by the European Research Council under Grant
  No.: ERC-2020-AdG 101020093.'
alternative_title:
- LNCS
article_processing_charge: No
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: 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, Kueffner K, Mallik K. Monitoring algorithmic fairness under
    partial observations. In: <i>23rd International Conference on Runtime Verification</i>.
    Vol 14245. Springer Nature; 2023:291-311. doi:<a href="https://doi.org/10.1007/978-3-031-44267-4_15">10.1007/978-3-031-44267-4_15</a>'
  apa: 'Henzinger, T. A., Kueffner, K., &#38; Mallik, K. (2023). Monitoring algorithmic
    fairness under partial observations. In <i>23rd International Conference on Runtime
    Verification</i> (Vol. 14245, pp. 291–311). Thessaloniki, Greece: Springer Nature.
    <a href="https://doi.org/10.1007/978-3-031-44267-4_15">https://doi.org/10.1007/978-3-031-44267-4_15</a>'
  chicago: Henzinger, Thomas A, Konstantin Kueffner, and Kaushik Mallik. “Monitoring
    Algorithmic Fairness under Partial Observations.” In <i>23rd International Conference
    on Runtime Verification</i>, 14245:291–311. Springer Nature, 2023. <a href="https://doi.org/10.1007/978-3-031-44267-4_15">https://doi.org/10.1007/978-3-031-44267-4_15</a>.
  ieee: T. A. Henzinger, K. Kueffner, and K. Mallik, “Monitoring algorithmic fairness
    under partial observations,” in <i>23rd International Conference on Runtime Verification</i>,
    Thessaloniki, Greece, 2023, vol. 14245, pp. 291–311.
  ista: 'Henzinger TA, Kueffner K, Mallik K. 2023. Monitoring algorithmic fairness
    under partial observations. 23rd International Conference on Runtime Verification.
    RV: Conference on Runtime Verification, LNCS, vol. 14245, 291–311.'
  mla: Henzinger, Thomas A., et al. “Monitoring Algorithmic Fairness under Partial
    Observations.” <i>23rd International Conference on Runtime Verification</i>, vol.
    14245, Springer Nature, 2023, pp. 291–311, doi:<a href="https://doi.org/10.1007/978-3-031-44267-4_15">10.1007/978-3-031-44267-4_15</a>.
  short: T.A. Henzinger, K. Kueffner, K. Mallik, in:, 23rd International Conference
    on Runtime Verification, Springer Nature, 2023, pp. 291–311.
conference:
  end_date: 2023-10-06
  location: Thessaloniki, Greece
  name: 'RV: Conference on Runtime Verification'
  start_date: 2023-10-03
date_created: 2023-10-29T23:01:15Z
date_published: 2023-10-01T00:00:00Z
date_updated: 2023-10-31T11:48:20Z
day: '01'
department:
- _id: ToHe
doi: 10.1007/978-3-031-44267-4_15
ec_funded: 1
external_id:
  arxiv:
  - '2308.00341'
intvolume: '     14245'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.48550/arXiv.2308.00341
month: '10'
oa: 1
oa_version: Preprint
page: 291-311
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
  call_identifier: H2020
  grant_number: '101020093'
  name: Vigilant Algorithmic Monitoring of Software
publication: 23rd International Conference on Runtime Verification
publication_identifier:
  eissn:
  - 1611-3349
  isbn:
  - '9783031442667'
  issn:
  - 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Monitoring algorithmic fairness under partial observations
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 14245
year: '2023'
...