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T05. Human review and approval workflows
Human-in-the-loop and approval research, including automation-induced complacency.
- T05-22Candidate approachSince 2025
What You Approve Is What Executes: Consent Integrity for Black-Box LLM Agents
- T05-23Candidate approachSince 2025
Oversight Has a Capacity: Calibrating Agent Guards to a Subjective, Fatiguing Human
- T05-21Similar problemSince 2025
Human-In-the-Loop Software Development Agents (HULA)
- T05-1Similar problem
Ironies of Automation
- T05-2Similar problem
The Out-of-the-Loop Performance Problem and Level of Control in Automation
- T05-3Similar problem
Humans and Automation: Use, Misuse, Disuse, Abuse
- T05-4Structural parallel
A Model for Types and Levels of Human Interaction with Automation
- T05-5Similar problem
Complacency and Bias in Human Use of Automation: An Attentional Integration
- T05-6Structural parallel
On Optimum Recognition Error and Reject Tradeoff
- T05-7Structural parallel
Learning with Rejection
- T05-8Structural parallel
Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer
- T05-9Structural parallel
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
- T05-10Structural parallel
Consistent Estimators for Learning to Defer to an Expert
- T05-11Structural parallel
Learning to Complement Humans
- T05-12Structural parallel
Machine Learning with a Reject Option: A survey
- T05-13Similar problem
Trust in Automation: Designing for Appropriate Reliance
- T05-14Similar problem
Guidelines for Human-AI Interaction
- T05-15Similar problem
Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making
- T05-16Similar problem
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
- T05-17Similar problem
To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making
- T05-18Similar problem
The flaws of policies requiring human oversight of government algorithms
- T05-19Structural parallel
Identifying the Risks of LM Agents with an LM-Emulated Sandbox (ToolEmu)
- T05-20Structural parallel
AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
- T05-24Candidate approach
Deep reinforcement learning from human preferences
- T05-25Candidate approach
Human-in-the-loop machine learning: a state of the art
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