Executive summary: Aligning the risk attitudes of agentic AI systems with users and society raises complex ethical and technical challenges that require balancing user preferences, developer responsibilities, and societal impacts.
Key points:
Two models for agentic AI risk attitudes are proposed: Proxy Agent (mimicking user preferences) and Off-the-Shelf Tool (constrained for desirable outcomes).
AI developers must navigate legal, reputational, and ethical liabilities while creating systems of shared responsibility with users.
Calibrating AI risk attitudes to users involves eliciting behaviors/judgments, modeling underlying attitudes, and designing appropriate actions.
Actual behaviors and self-reported general risk attitudes are more reliable indicators than hypothetical choices or lottery preferences.
Pre-existing risk classes may outperform learning-based calibration methods due to limitations in available user risk preference data.
Balancing user satisfaction, developer duties, and societal impacts is crucial for responsible agentic AI development.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, andcontact us if you have feedback.
Executive summary: Aligning the risk attitudes of agentic AI systems with users and society raises complex ethical and technical challenges that require balancing user preferences, developer responsibilities, and societal impacts.
Key points:
Two models for agentic AI risk attitudes are proposed: Proxy Agent (mimicking user preferences) and Off-the-Shelf Tool (constrained for desirable outcomes).
AI developers must navigate legal, reputational, and ethical liabilities while creating systems of shared responsibility with users.
Calibrating AI risk attitudes to users involves eliciting behaviors/judgments, modeling underlying attitudes, and designing appropriate actions.
Actual behaviors and self-reported general risk attitudes are more reliable indicators than hypothetical choices or lottery preferences.
Pre-existing risk classes may outperform learning-based calibration methods due to limitations in available user risk preference data.
Balancing user satisfaction, developer duties, and societal impacts is crucial for responsible agentic AI development.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.