mlsanityData to 14 Oct 2026

On the Influence of Explainable AI on Automation Bias

arXiv 2204.08859 ↗First posted 19 Apr 2022cs.HC cs.AIcited by 18

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Abstract (arXiv)

Artificial intelligence (AI) is gaining momentum, and its importance for the future of work in many areas, such as medicine and banking, is continuously rising. However, insights on the effective collaboration of humans and AI are still rare. Typically, AI supports humans in decision-making by addressing human limitations. However, it may also evoke human bias, especially in the form of automation bias as an over-reliance on AI advice. We aim to shed light on the potential to influence automation bias by explainable AI (XAI). In this pre-test, we derive a research model and describe our study design. Subsequentially, we conduct an online experiment with regard to hotel review classifications and discuss first results. We expect our research to contribute to the design and development of safe hybrid intelligence systems.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 11 of its 53 references that are in the map. First posted on arXiv 19 Apr 2022.

Builds on

11 of 53 references.

References · 11

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  2. 2020Underspecification Presents Challenges for Credibility in Modern Machine Learningcited by 496
  3. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 399
  4. 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
  5. 2018Metrics for Explainable AI: Challenges and Prospectscited by 260
  6. 2018On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detectioncited by 159
  7. 2020"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humanscited by 86
  8. 2021Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Makingcited by 58
  9. 2021Hybrid Intelligencecited by 55
  10. 2018The Challenge of Crafting Intelligible Intelligencecited by 47
  11. 2022Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Makingcited by 28

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 4; 2023: 4; 2024: 5; 2025: 4; 2026: 1.

18 papers.

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  1. 2023Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanationscited by 83
  2. 2022A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Makingcited by 29
  3. 2022Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Makingcited by 25
  4. 2024How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experimentcited by 22
  5. 2022On the Effect of Information Asymmetry in Human-AI Teamscited by 17
  6. 2024Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaborationcited by 8
  7. 2023A Case Study in Engineering a Conversational Programming Assistant's Personacited by 6
  8. 2024Human Delegation Behavior in Human-AI Collaboration: The Effect of Contextual Informationcited by 5
  9. 2022Painting the black box white: experimental findings from applying XAI to an ECG reading settingcited by 4
  10. 2024Transferring Domain Knowledge with (X)AI-Based Learning Systemscited by 3
  11. 2025Fine-Grained Appropriate Reliance: Human-AI Collaboration with a Multi-Step Transparent Decision Workflow for Complex Task Decompositioncited by 3
  12. 2025Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialoguecited by 3
6 more
  1. 2024Overcoming Anchoring Bias: The Potential of AI and XAI-based Decision Supportcited by 2
  2. 2023On the Perception of Difficulty: Differences between Humans and AIcited by 1
  3. 2023User Friendly and Adaptable Discriminative AI: Using the Lessons from the Success of LLMs and Image Generation Modelscited by 1
  4. 2025The Impact of Transparency in AI Systems on Users' Data-Sharing Intentions: A Scenario-Based Experimentcited by 0
  5. 2025Development of Mental Models in Human-AI Collaboration: A Conceptual Frameworkcited by 0
  6. 2026Using Learning Theories to Evolve Human-Centered XAI: Future Perspectives and Challengescited by 0