Abraham Haskins

Generative AI inherently triggers a computational failure mode in human observers (a "generative crash") due to a lack of latent intentionality required for Inverse Reinforcement Learning (IRL) convergence. Artistic appreciation operates as the biological execution of this IRL process. To address the generative crash and broader AI alignment failures, I introduce the Ghost Scale (an HCI cognitive affordance for identifying intentionality) and propose Cooperative Inverse Reinforcement Learning (CIRL) to mimic biological value transmission. The Intent Extraction Limit is formalized to define the prior relationship. Applying this model addresses two major issues: generative AI's friction with the art community (via the Ghost Scale, a cognitive affordance and UX framework for signaling intentionality) and AI alignment [via a proposed shift from Reinforcement Learning from Human Feedback (RLHF) toward top-down value capture through Cooperative Inverse Reinforcement Learning (CIRL) informed by world models]. Six empirical hypotheses are proposed to test the framework.

Abraham Haskins, Ph.D., is a Lead Human-AI Systems Architect at Boeing with a decade of experience in safety-critical human factors engineering across aerospace and defense. He holds a Ph.D. in Human Factors and Industrial/Organizational Psychology from Wright State University (4.0 GPA), where he also taught Research Methods and Advanced Statistics. His current independent research develops the Ghost Scale, a cognitive affordance framework for AI transparency grounded in the Free Energy Principle and Maximum Entropy Inverse Reinforcement Learning, currently under review at Computers in Human Behavior. At Boeing, he led the human factors investigation into the Alaska Airlines 737 MAX 9 door plug incident and founded the enterprise AI Governance Sub-Council. Previously at Infoscitex, he led pilot-AI teaming interface design for defense programs. His work bridges cognitive science, human-computer interaction, and AI alignment, with a focus on how biological intent-extraction mechanisms interact with generative AI systems.