Papers for

executive decision teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Executive judgement risks in AI support from weakened decision info

Executive Judgement in AI-Mediated Decision-Making Environments: A Process Theory of Formation, Qualification Attrition and Authorisation

Abstract: Generative artificial intelligence can contribute to the representations, alternatives and evaluations through which executive judgements are formed. Existing research already explains important aspects of hybrid cognition, reliance, managerial agency and accountability. This article develops a narrower process challenge: locally competent human and AI contributions can still culminate in an inadequately warranted organisational commitment when established operational assumptions, uncertainties or dependencies are lost or transformed before authorisation. It proposes qualification attrition as the process through which such epistemically consequential content is weakened across successive transformations, and a formation--authorisation gap as the resulting discrepancy between the grounds required for warranted commitment and those that remain accessible, intelligible and challengeable to the authorising role. Executive judgement governance is defined through four dimensions: boundary setting, interpretive challenge, reliance calibration, and authorisation with answerability. Four propositions connect transformation, qualification attrition and governance arrangements to epistemic risk, accountability risk and judgement quality. The contribution is an integrative process theory of how distributed judgement formation can fail as a composition even where individual contributions, local reliance decisions and formal accountability arrangements appear competent.

Wed 30 SeptComputers and Society
The gist
Decisions made by executives often involve input from both people and AI systems. The authors explain that even if each contributor is competent on their own, the overall decision can lose important information as it passes through different stages, leading to weaker justification for final choices. This weakening is called 'qualification attrition,' and it creates a gap between what is needed for a good decision and what the decision-makers actually understand or can question. They propose ways to manage this risk by setting clear boundaries, challenging interpretations, adjusting reliance on AI, and ensuring accountability during authorization.
Open → 2609.39442v1