Skip to main content
Have a personal or library account? Click to login
The Illusion of Rationale: Plausible Unfaithfulness and the Erosion of Managerial Decision Provenance (2024-2026) Cover

The Illusion of Rationale: Plausible Unfaithfulness and the Erosion of Managerial Decision Provenance (2024-2026)

Open Access
|Jul 2026

References

  1. Wang, X., Liang, C., & Yin, M. (2023). The Effects of AI Biases and Explanations on Human Decision Fairness. IJCAI/CSCW. [https://doi.org/10.24963/ijcai.2023/343]
  2. Akbar, S. A., et al. (2024). HalluMeasure: Fine-grained Hallucination Measurement Using Chain-of-Thought Reasoning. EMNLP. [https://aclanthology.org/2024.emnlp-main.837.pdf]
  3. Lewis-Lim, S., et al. (2025). Analysing Chain of Thought Dynamics: Active Guidance or Unfaithful Post-hoc Rationalisation? EMNLP. [https://aclanthology.org/2025.emnlp-main.1516.pdf]
  4. Tutek, M., et al. (2025). Measuring Chain of Thought Faithfulness by Unlearning Reasoning Steps. EMNLP. [https://aclanthology.org/2025.emnlp-main.504.pdf]
  5. Jiang, F., et al. (2025). SAFECHAIN: Safety of Language Models with Long Chain-of-Thought Reasoning Capabilities. ACL Findings. [https://aclanthology.org/2025.findings-acl.1197.pdf]
  6. Chen, J., et al. (2024). Plug-and-Play Grounding of Reasoning in Multimodal Large Language Models. arXiv. [https://arxiv.org/pdf/2403.19322]
  7. Arcuschin, I., et al. (2025). Chain-of-Thought Reasoning In The Wild Is Not Always Faithful. arXiv. [https://arxiv.org/pdf/2503.08679]
  8. Wu, T., et al. (2025). Effectively Controlling Reasoning Models through Thinking Intervention. arXiv. [https://arxiv.org/pdf/2503.24370]
  9. Matton, K., et al. (2025). Walk the Talk? Measuring the Faithfulness of Large Language Model Explanations. ICLR. [https://arxiv.org/pdf/2504.14150]
  10. Chen, Y., et al. (2025). Reasoning Models Don’t Always Say What They Think. Anthropic. [arXiv:2505.05410]
  11. Raza, S., et al. (2025). TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems. arXiv. [https://arxiv.org/pdf/2506.04133]
  12. Emmons, S., et al. (2025). When Chain of Thought is Necessary, Language Models Struggle to Evade Monitors. Google DeepMind. [https://arxiv.org/pdf/2507.05246]
  13. Danry, V., et al. (2025). Deceptive Explanations by Large Language Models Lead People to Change their Beliefs. CHI. [https://doi.org/10.1145/3706598.3713408]
  14. Qu, J., et al. (2025). Understanding the Effects of Explaining Predictive but Unintuitive Features in Human-XAI Interaction. FAccT. [https://doi.org/10.1145/3715275.3732021]
  15. Nova, C. (2025). From Operator to Supervisor: How AI Changes Your Job from Doing the Work to Approving It. Axiacore. [https://axiacore.com/blog/from-operator-to-supervisor/]
Language: English
Page range: 5220 - 5226
Published on: Jul 23, 2026
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2026 Luca-Dan CICEU, Augustin SEMENESCU, Radu RUGIUBEI, published by Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.