Algorithmic Accountability in Management Accounting Systems

Gábor Tóth

Algorithmic Accountability in Management Accounting Systems

Author(s)	Dr. Gábor Tóth
Country	United States
Abstract	Management accounting systems increasingly incorporate predictive models, optimization algorithms, robotic automation, business analytics, and machine-assisted recommendations into budgeting, forecasting, cost allocation, profitability analysis, performance monitoring, pricing, working-capital management, and investment evaluation. These systems can increase analytical capacity, yet they also create an accountability problem when managers receive algorithmically generated recommendations without sufficient knowledge of the data, assumptions, transformations, decision rules, limitations, or organizational responsibilities underlying those outputs. This study develops a framework for algorithmic accountability in management accounting systems by integrating accounting-control principles with transparency, explainability, traceability, human oversight, responsibility assignment, monitoring, and contestability. Because authenticated organizational data were not supplied, the quantitative component is explicitly structured as a simulation-based methodological study. Eighty synthetic algorithm-enabled management accounting system profiles are evaluated across four accountability-maturity conditions: opaque automation, documented analytics, governed algorithms, and accountable algorithmic systems. An Algorithmic Accountability Index is developed from six dimensions covering data traceability, model explainability, human oversight, responsibility attribution, continuous monitoring, and challenge or correction mechanisms. A complementary Decision Assurance Score evaluates the extent to which algorithm-supported managerial decisions remain reliable, reviewable, and organizationally defensible. Simulated results show mean decision-assurance scores increasing from 51.8 under opaque automation to 87.6 under the highest accountability condition. The findings indicate that sophisticated algorithms alone do not create effective management control. Their organizational value depends on whether decision makers can understand relevant limitations, trace important outputs, override inappropriate recommendations, identify accountable owners, monitor changing performance, and correct contested outcomes. The framework provides a basis for future empirical assessment of responsible algorithm use in management accounting without assuming that automation should replace professional managerial judgment.
Keywords	algorithmic accountability, management accounting systems, explainable analytics, management control, artificial intelligence, business analytics, human oversight, decision assurance
Field	Engineering
Published In	Volume 6, Issue 4, July-August 2025
Published On	2025-08-18

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American Journal of Advanced Multidisciplinary Innovation and Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Algorithmic Accountability in Management Accounting Systems

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