---
_id: '37312'
abstract:
- lang: eng
  text: Optimal decision making requires appropriate evaluation of advice. Recent
    literature reports that algorithm aversion reduces the effectiveness of predictive
    algorithms. However, it remains unclear how people recover from bad advice given
    by an otherwise good advisor. Previous work has focused on algorithm aversion
    at a single time point. We extend this work by examining successive decisions
    in a time series forecasting task using an online between-subjects experiment
    (N = 87). Our empirical results do not confirm algorithm aversion immediately
    after bad advice. The estimated effect suggests an increasing algorithm appreciation
    over time. Our work extends the current knowledge on algorithm aversion with insights
    into how weight on advice is adjusted over consecutive tasks. Since most forecasting
    tasks are not one-off decisions, this also has implications for practitioners.
author:
- first_name: Dirk
  full_name: Leffrang, Dirk
  id: '51271'
  last_name: Leffrang
  orcid: 0000-0001-9004-2391
- first_name: Kevin
  full_name: Bösch, Kevin
  last_name: Bösch
- first_name: Oliver
  full_name: Müller, Oliver
  id: '72849'
  last_name: Müller
citation:
  ama: 'Leffrang D, Bösch K, Müller O. Do People Recover from Algorithm Aversion?
    An Experimental Study of Algorithm Aversion over Time. In: <i>Hawaii International
    Conference on System Sciences</i>. ; 2023.'
  apa: Leffrang, D., Bösch, K., &#38; Müller, O. (2023). Do People Recover from Algorithm
    Aversion? An Experimental Study of Algorithm Aversion over Time. <i>Hawaii International
    Conference on System Sciences</i>. Hawaii International Conference on System Sciences.
  bibtex: '@inproceedings{Leffrang_Bösch_Müller_2023, title={Do People Recover from
    Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time}, booktitle={Hawaii
    International Conference on System Sciences}, author={Leffrang, Dirk and Bösch,
    Kevin and Müller, Oliver}, year={2023} }'
  chicago: Leffrang, Dirk, Kevin Bösch, and Oliver Müller. “Do People Recover from
    Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time.” In
    <i>Hawaii International Conference on System Sciences</i>, 2023.
  ieee: D. Leffrang, K. Bösch, and O. Müller, “Do People Recover from Algorithm Aversion?
    An Experimental Study of Algorithm Aversion over Time,” presented at the Hawaii
    International Conference on System Sciences, 2023.
  mla: Leffrang, Dirk, et al. “Do People Recover from Algorithm Aversion? An Experimental
    Study of Algorithm Aversion over Time.” <i>Hawaii International Conference on
    System Sciences</i>, 2023.
  short: 'D. Leffrang, K. Bösch, O. Müller, in: Hawaii International Conference on
    System Sciences, 2023.'
conference:
  name: Hawaii International Conference on System Sciences
date_created: 2023-01-18T10:53:51Z
date_updated: 2024-01-10T09:52:59Z
department:
- _id: '196'
keyword:
- Algorithm aversion
- Time series
- Decision making
- Advice taking
- Forecasting
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://scholarspace.manoa.hawaii.edu/items/62b58ddc-895c-48c3-8194-522a1758a26f
oa: '1'
publication: Hawaii International Conference on System Sciences
status: public
title: Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm
  Aversion over Time
type: conference
user_id: '51271'
year: '2023'
...
---
_id: '50121'
abstract:
- lang: eng
  text: Many researchers and practitioners see artificial intelligence as a game changer
    compared to classical statistical models. However, some software providers engage
    in “AI washing”, relabeling solutions that use simple statistical models as AI
    systems. By contrast, research on algorithm aversion unsystematically varied the
    labels for advisors and treated labels such as "artificial intelligence" and "statistical
    model" synonymously. This study investigates the effect of individual labels on
    users' actual advice utilization behavior. Through two incentivized online within-subjects
    experiments on regression tasks, we find that labeling human advisors with labels
    that suggest higher expertise leads to an increase in advice-taking, even though
    the content of the advice remains the same. In contrast, our results do not suggest
    such an expert effect for advice-taking from algorithms, despite differences in
    self-reported perception. These findings challenge the effectiveness of framing
    intelligent systems as AI-based systems and have important implications for both
    research and practice.
author:
- first_name: Dirk
  full_name: Leffrang, Dirk
  id: '51271'
  last_name: Leffrang
  orcid: 0000-0001-9004-2391
citation:
  ama: 'Leffrang D. AI Washing: The Framing Effect of Labels on Algorithmic Advice
    Utilization. In: <i>International Conference on Information Systems</i>. ; 2023.'
  apa: 'Leffrang, D. (2023). AI Washing: The Framing Effect of Labels on Algorithmic
    Advice Utilization. <i>International Conference on Information Systems</i>, <i>10</i>.'
  bibtex: '@inproceedings{Leffrang_2023, title={AI Washing: The Framing Effect of
    Labels on Algorithmic Advice Utilization}, number={10}, booktitle={International
    Conference on Information Systems}, author={Leffrang, Dirk}, year={2023} }'
  chicago: 'Leffrang, Dirk. “AI Washing: The Framing Effect of Labels on Algorithmic
    Advice Utilization.” In <i>International Conference on Information Systems</i>,
    2023.'
  ieee: 'D. Leffrang, “AI Washing: The Framing Effect of Labels on Algorithmic Advice
    Utilization,” in <i>International Conference on Information Systems</i>, Hyderabad,
    India, 2023, no. 10.'
  mla: 'Leffrang, Dirk. “AI Washing: The Framing Effect of Labels on Algorithmic Advice
    Utilization.” <i>International Conference on Information Systems</i>, no. 10,
    2023.'
  short: 'D. Leffrang, in: International Conference on Information Systems, 2023.'
conference:
  location: Hyderabad, India
  name: International Conference on Information Systems (ICIS)
date_created: 2024-01-03T09:54:00Z
date_updated: 2024-01-10T09:53:41Z
department:
- _id: '196'
issue: '10'
keyword:
- Artificial Intelligence
- Algorithm Appreciation
- Framing
- Advice-taking
- Expertise
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/icis2023/aiinbus/aiinbus/10
publication: International Conference on Information Systems
status: public
title: 'AI Washing: The Framing Effect of Labels on Algorithmic Advice Utilization'
type: conference
user_id: '51271'
year: '2023'
...
---
_id: '50118'
abstract:
- lang: eng
  text: Despite the widespread use of machine learning algorithms, their effectiveness
    is limited by a phenomenon known as algorithm aversion. Recent research concluded
    that unobserved variables can cause algorithm aversion. However, the impact of
    an unobserved variable on algorithm aversion remains unclear. Previous studies
    focused on situations where humans had more variables available than algorithms.
    We extend this research by conducting an online experiment with 94 participants,
    systematically varying the number of observable variables to the advisor and the
    advisor type. Surprisingly, our results did not confirm that an unobserved variable
    had a negative effect on advice-taking. Instead, we found a positive impact in
    an algorithm appreciation scenario. This study provides new insights into the
    paradoxical behavior in which people weigh advice more despite having fewer variables,
    as they correct for the advisor's errors. Practitioners should consider this behavior
    when designing algorithms and account for user correction behavior.
author:
- first_name: Dirk
  full_name: Leffrang, Dirk
  id: '51271'
  last_name: Leffrang
  orcid: 0000-0001-9004-2391
citation:
  ama: 'Leffrang D. The Broken Leg of Algorithm Appreciation: An Experimental Study
    on the Effect of Unobserved Variables on Advice Utilization. In: <i>Wirtschaftsinformatik
    Conference</i>. ; 2023.'
  apa: 'Leffrang, D. (2023). The Broken Leg of Algorithm Appreciation: An Experimental
    Study on the Effect of Unobserved Variables on Advice Utilization. <i>Wirtschaftsinformatik
    Conference</i>, <i>19</i>.'
  bibtex: '@inproceedings{Leffrang_2023, title={The Broken Leg of Algorithm Appreciation:
    An Experimental Study on the Effect of Unobserved Variables on Advice Utilization},
    number={19}, booktitle={Wirtschaftsinformatik Conference}, author={Leffrang, Dirk},
    year={2023} }'
  chicago: 'Leffrang, Dirk. “The Broken Leg of Algorithm Appreciation: An Experimental
    Study on the Effect of Unobserved Variables on Advice Utilization.” In <i>Wirtschaftsinformatik
    Conference</i>, 2023.'
  ieee: 'D. Leffrang, “The Broken Leg of Algorithm Appreciation: An Experimental Study
    on the Effect of Unobserved Variables on Advice Utilization,” in <i>Wirtschaftsinformatik
    Conference</i>, Paderborn, 2023, no. 19.'
  mla: 'Leffrang, Dirk. “The Broken Leg of Algorithm Appreciation: An Experimental
    Study on the Effect of Unobserved Variables on Advice Utilization.” <i>Wirtschaftsinformatik
    Conference</i>, no. 19, 2023.'
  short: 'D. Leffrang, in: Wirtschaftsinformatik Conference, 2023.'
conference:
  location: Paderborn
  name: Wirtschaftsinformatik
date_created: 2024-01-03T09:50:06Z
date_updated: 2024-01-10T09:53:24Z
department:
- _id: '196'
issue: '19'
keyword:
- Algorithm aversion
- Data
- Decision-making
- Advice-taking
- Human-Computer Interaction
language:
- iso: eng
main_file_link:
- url: 'https://aisel.aisnet.org/wi2023/19 '
publication: Wirtschaftsinformatik Conference
status: public
title: 'The Broken Leg of Algorithm Appreciation: An Experimental Study on the Effect
  of Unobserved Variables on Advice Utilization'
type: conference
user_id: '51271'
year: '2023'
...
---
_id: '26812'
author:
- first_name: Dirk
  full_name: Leffrang, Dirk
  id: '51271'
  last_name: Leffrang
  orcid: 0000-0001-9004-2391
- first_name: Oliver
  full_name: Müller, Oliver
  id: '72849'
  last_name: Müller
citation:
  ama: 'Leffrang D, Müller O. Should I Follow this Model? The Effect of Uncertainty
    Visualization on the Acceptance of Time Series Forecasts. In: <i>IEEE Workshop
    on TRust and EXpertise in Visual Analytics</i>. ; 2021. doi:<a href="https://doi.org/10.1109/TREX53765.2021.00009">10.1109/TREX53765.2021.00009</a>'
  apa: Leffrang, D., &#38; Müller, O. (2021). Should I Follow this Model? The Effect
    of Uncertainty Visualization on the Acceptance of Time Series Forecasts. <i>IEEE
    Workshop on TRust and EXpertise in Visual Analytics</i>. 2021 IEEE Visualization
    conference. <a href="https://doi.org/10.1109/TREX53765.2021.00009">https://doi.org/10.1109/TREX53765.2021.00009</a>
  bibtex: '@inproceedings{Leffrang_Müller_2021, title={Should I Follow this Model?
    The Effect of Uncertainty Visualization on the Acceptance of Time Series Forecasts},
    DOI={<a href="https://doi.org/10.1109/TREX53765.2021.00009">10.1109/TREX53765.2021.00009</a>},
    booktitle={IEEE Workshop on TRust and EXpertise in Visual Analytics}, author={Leffrang,
    Dirk and Müller, Oliver}, year={2021} }'
  chicago: Leffrang, Dirk, and Oliver Müller. “Should I Follow This Model? The Effect
    of Uncertainty Visualization on the Acceptance of Time Series Forecasts.” In <i>IEEE
    Workshop on TRust and EXpertise in Visual Analytics</i>, 2021. <a href="https://doi.org/10.1109/TREX53765.2021.00009">https://doi.org/10.1109/TREX53765.2021.00009</a>.
  ieee: 'D. Leffrang and O. Müller, “Should I Follow this Model? The Effect of Uncertainty
    Visualization on the Acceptance of Time Series Forecasts,” presented at the 2021
    IEEE Visualization conference, 2021, doi: <a href="https://doi.org/10.1109/TREX53765.2021.00009">10.1109/TREX53765.2021.00009</a>.'
  mla: Leffrang, Dirk, and Oliver Müller. “Should I Follow This Model? The Effect
    of Uncertainty Visualization on the Acceptance of Time Series Forecasts.” <i>IEEE
    Workshop on TRust and EXpertise in Visual Analytics</i>, 2021, doi:<a href="https://doi.org/10.1109/TREX53765.2021.00009">10.1109/TREX53765.2021.00009</a>.
  short: 'D. Leffrang, O. Müller, in: IEEE Workshop on TRust and EXpertise in Visual
    Analytics, 2021.'
conference:
  end_date: 2021-10-19
  name: 2021 IEEE Visualization conference
  start_date: 2021-10-24
date_created: 2021-10-25T11:11:39Z
date_updated: 2024-01-10T09:55:48Z
department:
- _id: '196'
doi: 10.1109/TREX53765.2021.00009
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://trexvis.github.io/Workshop2021/papers/Leffrang.pdf
oa: '1'
publication: IEEE Workshop on TRust and EXpertise in Visual Analytics
status: public
title: Should I Follow this Model? The Effect of Uncertainty Visualization on the
  Acceptance of Time Series Forecasts
type: conference
user_id: '51271'
year: '2021'
...
---
_id: '75'
author:
- first_name: Dirk
  full_name: Leffrang, Dirk
  id: '51271'
  last_name: Leffrang
  orcid: 0000-0001-9004-2391
citation:
  ama: Leffrang D. <i>Online-Bewertung und Preissetzung auf Airbnb</i>. Universität
    Paderborn; 2017.
  apa: Leffrang, D. (2017). <i>Online-Bewertung und Preissetzung auf Airbnb</i>. Universität
    Paderborn.
  bibtex: '@book{Leffrang_2017, title={Online-Bewertung und Preissetzung auf Airbnb},
    publisher={Universität Paderborn}, author={Leffrang, Dirk}, year={2017} }'
  chicago: Leffrang, Dirk. <i>Online-Bewertung und Preissetzung auf Airbnb</i>. Universität
    Paderborn, 2017.
  ieee: D. Leffrang, <i>Online-Bewertung und Preissetzung auf Airbnb</i>. Universität
    Paderborn, 2017.
  mla: Leffrang, Dirk. <i>Online-Bewertung und Preissetzung auf Airbnb</i>. Universität
    Paderborn, 2017.
  short: D. Leffrang, Online-Bewertung und Preissetzung auf Airbnb, Universität Paderborn,
    2017.
date_created: 2017-10-17T12:41:06Z
date_updated: 2024-01-02T16:05:14Z
language:
- iso: ger
project:
- _id: '1'
  grant_number: '160364472'
  name: SFB 901
- _id: '8'
  grant_number: '160364472'
  name: SFB 901 - Subprojekt A4
- _id: '2'
  name: SFB 901 - Project Area A
publisher: Universität Paderborn
status: public
title: Online-Bewertung und Preissetzung auf Airbnb
type: bachelorsthesis
user_id: '14931'
year: '2017'
...