Automated Assessment of Bias and Fairness in Human-Computer Collaborative Design Iterations

**Abstract:** This paper introduces a novel framework, "HyperScore Evaluation Pipeline" (HEP), for rigorously assessing and mitigating bias and fairness concerns in human-computer collaborative design iterations within Human-Centered AI. HEP leverages a multi-layered evaluation approach combining logical consistency checks, dynamic code and simulation verification, novelty analysis, and impact forecasting to quantify potential biases arising from human input and algorithmic interventions. By feeding assessment results into a Meta-Self-Evaluation Loop with Reinforcement Learning (RL) Human Feedback, the system autonomously adapts its evaluation criteria and suggests design modifications to promote equitable outcomes, aiming for a demonstrable reduction in representational, allocational, and procedural biases. The system offers a 10x advantage in detecting subtle biases often missed by human reviewers, providing actionable feedback to facilitate fairer design development cycles.