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post-hoc-reasoning

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Post Hoc Reasoning

Post hoc reasoning refers to the practice of constructing explanations for decisions or outputs after the fact, rather than through transparent logical processes during decision-making. In the context of artificial intelligence systems, this phenomenon became particularly evident during the development and deployment of expert systems in the 1980s. These systems often produced outputs that appeared authoritative but lacked clear justification chains, leading developers and users to generate plausible-sounding explanations after observing results.

Connection to the Dreyfus Model

The Dreyfus and Dreyfus model of skill acquisition provided early theoretical insight into why expert systems faced fundamental limitations. The model distinguished between different levels of expertise, from novice rule-following to expert intuitive performance. Dreyfus and Dreyfus argued that genuine expert knowledge cannot be reduced to explicit rules and decision trees—the foundational assumption underlying 1980s expert systems. This critique anticipated that systems relying on explicit rules would struggle to capture the holistic judgment that characterizes skilled expertise, making post hoc rationalization a symptom of deeper architectural misalignment.

Implications for AI Development

The gap between expert systems’ logical transparency and actual decision-making processes highlighted how post hoc reasoning could obscure rather than illuminate AI behavior. When systems generated correct answers through brittle rule application or statistical correlation rather than principled reasoning, stakeholders often accepted accompanying explanations without scrutiny. This pattern demonstrated that explanations alone cannot substitute for genuine understanding of how systems arrive at their conclusions, a concern that remains relevant for modern AI systems.

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