Progressive Autonomy as Preference Learning: A Formalization of Trust Calibration for Agentic Tool Use

1. 1Formalize trust calibration as a preference-learning problem using Gaussian processes
2. 2Maintain a posterior over a latent human risk-tolerance function using probit likelihood on binary feedback
3. 3Escalate to human approval when the approval outcome is most uncertain
4. 4Implement a policy gateway to manage autonomous decision-making
5. 5Test and refine the trust calibration model using real-world scenarios

AI researchers and engineers working on autonomous systems can benefit from this formalization to develop more reliable and trustworthy agentic tools

💡 Trust calibration for agentic tool use can be formalized as a preference-learning problem using Gaussian processes and probit likelihood

🤖💡 Formalize trust calibration for agentic tool use as a preference-learning problem to improve autonomous decision-making #AI #Autonomy