Behavioral AI Evaluation Framework
Developing behavioral evaluation methods to measure the real-world impact of AI on human decision-making, trust, and behavior.
Partners
Status
Status
Ongoing
Ongoing
Focus
Focus
AI Evaluation
AI Evaluation
Overview
Current AI evaluation focuses primarily on model performance, including accuracy, reasoning, and safety. However, these metrics often overlook a critical question: How does AI influence human behavior?
This project develops a behavioral evaluation framework that measures how AI systems affect human trust, judgment, decision-making, and actions in real-world environments. The goal is to complement existing technical benchmarks with human-centered evaluation methods.
Research Approach
The framework combines behavioral science, AI evaluation, and Trust & Safety to assess AI systems from the user's perspective. Rather than evaluating isolated responses, the research examines how interactions with AI shape user behavior over time, including trust formation, persuasion, over-reliance, cognitive bias, and decision quality.
The project integrates behavioral experiments, real-world user studies, and LLM-based evaluation methods to create practical tools for researchers, AI developers, and policymakers.
Expected Inpact
The Behavioral AI Evaluation Framework aims to establish a new standard for evaluating AI systems based not only on what models can do, but also on how they affect people.
The framework is designed to support AI developers, technology companies, governments, and researchers in building AI systems that promote informed decision-making, appropriate trust, and safer human-AI interactions. By placing human behavior at the center of AI evaluation, the project seeks to advance more responsible and human-centered AI deployment.
RESEARCH PROJECTS
Research at the Intersection of Human Behavior and AI.
Behavioral AI Evaluation Framework
Developing behavioral evaluation methods to measure the real-world impact of AI on human decision-making, trust, and behavior.
Partners
Status
Status
Ongoing
Ongoing
Focus
Focus
AI Evaluation
AI Evaluation
Overview
Current AI evaluation focuses primarily on model performance, including accuracy, reasoning, and safety. However, these metrics often overlook a critical question: How does AI influence human behavior?
This project develops a behavioral evaluation framework that measures how AI systems affect human trust, judgment, decision-making, and actions in real-world environments. The goal is to complement existing technical benchmarks with human-centered evaluation methods.
Research Approach
The framework combines behavioral science, AI evaluation, and Trust & Safety to assess AI systems from the user's perspective. Rather than evaluating isolated responses, the research examines how interactions with AI shape user behavior over time, including trust formation, persuasion, over-reliance, cognitive bias, and decision quality.
The project integrates behavioral experiments, real-world user studies, and LLM-based evaluation methods to create practical tools for researchers, AI developers, and policymakers.
Expected Inpact
The Behavioral AI Evaluation Framework aims to establish a new standard for evaluating AI systems based not only on what models can do, but also on how they affect people.
The framework is designed to support AI developers, technology companies, governments, and researchers in building AI systems that promote informed decision-making, appropriate trust, and safer human-AI interactions. By placing human behavior at the center of AI evaluation, the project seeks to advance more responsible and human-centered AI deployment.
RESEARCH PROJECTS