Moral Accountability in AI-Driven Corporate Decision-Making: A Comparative Case Analysis of Amazon, United Health Group, and Deutsche Bank
DOI:
https://doi.org/10.51325/3yx5qe22Keywords:
AI Ethics, Moral Accountability, Algorithmic Decision-Making, Corporate Governance, Responsible AI, Distributed AgencyAbstract
The use of artificial intelligence systems by corporations in making important decisions, such as who to hire, who gets health care coverage, or who qualifies for credit, raises fundamental moral accountability issues that current business ethics frameworks do not adequately address. Who is morally responsible for a decision if a job application is rejected, a health insurance claim is denied, or a credit request is turned down? In this paper, we present a comparative case study of three firms — Amazon (automated hiring), UnitedHealth Group (AI-based healthcare benefit determinations), and Deutsche Bank (algorithmic credit scoring) — to explore how moral accountability is constructed, distributed, and contested in AI-mediated corporate decision environments. Building on philosophical literature on collective moral responsibility, distributed agency, and algorithmic governance, we elaborate an Accountability Diffusion Framework (ADF) that describes four mechanisms through which the use of AI systematically undermines moral accountability in corporate contexts: opacity diffusion, temporal displacement, agential fragmentation, and normative laundering. In our analysis, all three companies have used these mechanisms, whether deliberately or not, to shield human decision-makers from responsibility for harmful AI-driven outcomes. We argue that restoring moral accountability in AI-driven organisations requires not only improved technical explainability, but fundamental changes to corporate governance, stakeholder engagement practices, and the regulatory architecture of AI deployment. The paper ends with a set of accountability design principles for ethically responsible AI governance.
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