From documentary evidence to algorithmic auditing: epistemic, methodological and normative challenges for public accounting
DOI:
https://doi.org/10.58493/ecca.2025.4.1.24Keywords:
Financial auditing; Artificial intelligence; Accounting epistemology; Continuous auditing; Algorithmic ethics; Public accounting.Abstract
The incorporation of artificial intelligence (AI) in financial auditing is rapidly changing public accounting practices, not only in operational terms but also in how accounting knowledge is produced, validated, and certified. This phenomenon has generated a transition from audit models based on sampling and professional judgment to automated schemes for massive data analysis and continuous auditing supported by machine learning algorithms (Kokina & Davenport, 2017; Power, 2022). This article presents a systematic literature review following the PRISMA guidelines to analyze the epistemological, methodological, and ethical-regulatory challenges associated with the use of AI in financial auditing. Scopus, Web of Science, ScienceDirect, EBSCO, Redalyc, and SciELO were consulted, with a time frame of 2015–2025. After the screening process, 47 studies were included for comparative qualitative analysis. The findings are organized around three axes: (1) Epistemological: the validation of accounting knowledge faces the challenge of opaque algorithmic systems (black-box systems), which strains the traditional notion of auditor objectivity (Burrell, 2016; Doshi-Velez & Kim, 2017). (2) Methodological: sampling-based auditing is being displaced by total analytics and continuous auditing, which demands new professional skills in data science, anomaly mining, and financial visualization (Alles, 2015; IAASB, 2023). (3) Ethical-regulatory: there is a lack of clear regulatory frameworks regarding algorithmic responsibility, automated bias, traceability, and transparency in AI-assisted financial decision-making. AI not only transforms auditing practice but also the epistemology of public accounting and the auditor's social role. Advanced professional training, specific international regulations, and hybrid human-algorithmic auditing frameworks are required.
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