---
_id: '33490'
abstract:
- lang: eng
  text: Algorithmic fairness in Information Systems (IS) is a concept that aims to
    mitigate systematic discrimination and bias in automated decision-making. However,
    previous research argued that different fairness criteria are often incompatible.
    In hiring, AI is used to assess and rank applicants according to their fit for
    vacant positions. However, various types of bias also exist for AI-based algorithms
    (e.g., using biased historical data). To reduce AI’s bias and thereby unfair treatment,
    we conducted a systematic literature review to identify suitable strategies for
    the context of hiring. We identified nine fundamental articles in this context
    and extracted four types of approaches to address unfairness in AI, namely pre-process,
    in-process, post-process, and feature selection. Based on our findings, we (a)
    derived a research agenda for future studies and (b) proposed strategies for practitioners
    who design and develop AIs for hiring purposes.
author:
- first_name: Jonas
  full_name: Rieskamp, Jonas
  id: '77643'
  last_name: Rieskamp
- first_name: Lennart
  full_name: Hofeditz, Lennart
  last_name: Hofeditz
- first_name: Milad
  full_name: Mirbabaie, Milad
  id: '88691'
  last_name: Mirbabaie
- first_name: Stefan
  full_name: Stieglitz, Stefan
  last_name: Stieglitz
citation:
  ama: 'Rieskamp J, Hofeditz L, Mirbabaie M, Stieglitz S. Approaches to Improve Fairness
    when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature Review
    to Guide Future Research. In: <i>Proceedings of the Annual Hawaii International
    Conference on System Sciences (HICSS)</i>. ; 2023.'
  apa: Rieskamp, J., Hofeditz, L., Mirbabaie, M., &#38; Stieglitz, S. (2023). Approaches
    to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic
    Literature Review to Guide Future Research. <i>Proceedings of the Annual Hawaii
    International Conference on System Sciences (HICSS)</i>. Proceedings of the Annual
    Hawaii International Conference on System Sciences (HICSS).
  bibtex: '@inproceedings{Rieskamp_Hofeditz_Mirbabaie_Stieglitz_2023, title={Approaches
    to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic
    Literature Review to Guide Future Research}, booktitle={Proceedings of the Annual
    Hawaii International Conference on System Sciences (HICSS)}, author={Rieskamp,
    Jonas and Hofeditz, Lennart and Mirbabaie, Milad and Stieglitz, Stefan}, year={2023}
    }'
  chicago: Rieskamp, Jonas, Lennart Hofeditz, Milad Mirbabaie, and Stefan Stieglitz.
    “Approaches to Improve Fairness When Deploying AI-Based Algorithms in Hiring –
    Using a Systematic Literature Review to Guide Future Research.” In <i>Proceedings
    of the Annual Hawaii International Conference on System Sciences (HICSS)</i>,
    2023.
  ieee: J. Rieskamp, L. Hofeditz, M. Mirbabaie, and S. Stieglitz, “Approaches to Improve
    Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature
    Review to Guide Future Research,” presented at the Proceedings of the Annual Hawaii
    International Conference on System Sciences (HICSS), 2023.
  mla: Rieskamp, Jonas, et al. “Approaches to Improve Fairness When Deploying AI-Based
    Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research.”
    <i>Proceedings of the Annual Hawaii International Conference on System Sciences
    (HICSS)</i>, 2023.
  short: 'J. Rieskamp, L. Hofeditz, M. Mirbabaie, S. Stieglitz, in: Proceedings of
    the Annual Hawaii International Conference on System Sciences (HICSS), 2023.'
conference:
  end_date: 2023-01-06
  name: Proceedings of the Annual Hawaii International Conference on System Sciences
    (HICSS)
  start_date: 2023-01-03
date_created: 2022-09-27T12:39:12Z
date_updated: 2023-02-06T14:39:51Z
keyword:
- fairness in AI
- SLR
- hiring
- AI implementation
- AI-based algorithms
language:
- iso: eng
main_file_link:
- url: https://hdl.handle.net/10125/102654
publication: Proceedings of the Annual Hawaii International Conference on System Sciences
  (HICSS)
status: public
title: Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring
  – Using a Systematic Literature Review to Guide Future Research
type: conference
user_id: '77643'
year: '2023'
...