Paper Type

ERF

Abstract

Generative AI systems are rapidly entering professional domains — e.g., medicine, law, finance, auditing — where they produce judgment-embedded outputs carrying the structural markers of expert reasoning. Unlike earlier decision support and expert systems that presented computations or structured recommendations, generative AI delivers pre-formed professional judgments accompanied by natural-language argumentation. This paper deconstructs four foundational information processing (IP) assumptions and demonstrates their violation when AI delivers such outputs, revealing a qualitative paradigm transformation. I then propose a Dual-Path AI Influence on Professional Judgment (DAIP) framework comprising two analytically distinct, independently grounded paths: a cognitive path (anchored in cognitive psychology) analyzing how AI reconfigures professional reasoning through argumentative anchoring, and a governance path (anchored in stakeholder-agency theory) analyzing how the resulting information asymmetry creates structural governance failure. This architecture allows each path to stand independently while clarifying how individual-level cognitive effects translate, at scale, into structural governance concerns.

Paper Number

1931

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Aug 15th, 12:00 AM

Rethinking Professional Information Processing in the Age of Generative AI: How Judgment-Embedded AI Outputs Reconfigure Cognitive Processes

Generative AI systems are rapidly entering professional domains — e.g., medicine, law, finance, auditing — where they produce judgment-embedded outputs carrying the structural markers of expert reasoning. Unlike earlier decision support and expert systems that presented computations or structured recommendations, generative AI delivers pre-formed professional judgments accompanied by natural-language argumentation. This paper deconstructs four foundational information processing (IP) assumptions and demonstrates their violation when AI delivers such outputs, revealing a qualitative paradigm transformation. I then propose a Dual-Path AI Influence on Professional Judgment (DAIP) framework comprising two analytically distinct, independently grounded paths: a cognitive path (anchored in cognitive psychology) analyzing how AI reconfigures professional reasoning through argumentative anchoring, and a governance path (anchored in stakeholder-agency theory) analyzing how the resulting information asymmetry creates structural governance failure. This architecture allows each path to stand independently while clarifying how individual-level cognitive effects translate, at scale, into structural governance concerns.

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