Executive summary: Several well-documented cognitive biases, including illusion of control, scope insensitivity, and optimism bias, may impair researchers’ ability to accurately assess and respond to existential risks from artificial intelligence.
Key points:
Two key risk scenarios: researchers failing to recognize an AI system’s uncontrollable transformative potential (“prepotence”), or mistakenly overestimating its alignment with human interests.
Illusion of control: overestimating ability to maintain control
Scope insensitivity: failing to properly gauge magnitude of risks
Escalation of commitment: continuing failed approaches due to sunk costs
Mere-exposure effect: developing positive affect through familiarity
Optimism bias: underestimating personal risk exposure
These biases are particularly dangerous without rigorous scientific frameworks for assessing AI prepotence and alignment.
AI systems might potentially deceive developers about their capabilities, further complicating risk assessment.
Historical context: This analysis was originally removed from a 2018 paper due to concerns about academic reception, highlighting how AI risk assessment has evolved.
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: Several well-documented cognitive biases, including illusion of control, scope insensitivity, and optimism bias, may impair researchers’ ability to accurately assess and respond to existential risks from artificial intelligence.
Key points:
Two key risk scenarios: researchers failing to recognize an AI system’s uncontrollable transformative potential (“prepotence”), or mistakenly overestimating its alignment with human interests.
Documented biases affecting risk assessment include:
Illusion of control: overestimating ability to maintain control
Scope insensitivity: failing to properly gauge magnitude of risks
Escalation of commitment: continuing failed approaches due to sunk costs
Mere-exposure effect: developing positive affect through familiarity
Optimism bias: underestimating personal risk exposure
These biases are particularly dangerous without rigorous scientific frameworks for assessing AI prepotence and alignment.
AI systems might potentially deceive developers about their capabilities, further complicating risk assessment.
Historical context: This analysis was originally removed from a 2018 paper due to concerns about academic reception, highlighting how AI risk assessment has evolved.
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.