dr. sc. Sabina Lacmanović, prof.

Teaching

undergraduate

• Business Decision-Making (23728) - Lecturer in charge

graduate

• Development of Organisation (201082) - Lecturer in charge
• Practicum (202488) - Lecturer in charge
• Preschool institution management and organisation (232838) - Lecturer in charge
• Project Management (202376) - Lecturer in charge
• Socioeconomic Management (202377) - Lecturer in charge

Consultations

Consultations: on Wednesdays from 11:35 to 12:35

- by e-mail  

- in office 31 (with prior notice via e-mail).

List of select publications

Lacmanovic, Sabina; Skare, Marinko: “Artificial Intelligence Bias Auditing – Current Approaches, Challenges and Lessons from Practice”, Review of Accounting and Finance, Volume 24, Issue 3, Accepted for Publication: 18-Feb-2025. DOI: 10.1108/RAF-01-2025-0006, available at: https://www.emerald.com/raf/article-abstract/24/3/375/1274302/Artificial-intelligence-bias-auditing-current?redirectedFrom=fulltext 

Abstract:

This study explores current approaches, challenges, and practical lessons in auditing artificial intelligence (AI) systems for bias, focusing on legal compliance audits in the United States and the European Union. This emphasizes the need for standardized methodologies to ensure trustworthy AI systems that align with ethical and regulatory expectations.
A qualitative analysis compared bias audit practices, including U.S. bias audit report summaries under New York City’s Local Law 144 and conformity assessments required by the EU AI Act. Data was gathered from publicly available reports and compliance guidelines to identify key challenges and lessons.
The findings revealed that AI systems are susceptible to various biases stemming from data, algorithms, and human oversight. While valuable, legal compliance audits lack standardization, leading to inconsistent reporting practices. The EU’s risk-based conformity assessment approach offers a comprehensive framework; however, its effectiveness depends on developing practical standards and consistent application.
This study is limited by the early implementation stage of regulatory frameworks, particularly the EU AI Act, and restricted access to comprehensive audit reports. A geographic focus on US and EU jurisdictions may limit the generalizability of our findings. Data availability constraints and the lack of standardized reporting frameworks affect the comparative analysis. Future research should focus on longitudinal studies of audit effectiveness, the development of standardized methodologies for intersectional bias assessment, and the investigation of automated audit tools that can adapt to emerging AI technologies while maintaining practical feasibility across different organizational contexts.
This research underscores the necessity of adopting socio-technical perspectives and standardized methodologies in AI auditing. It provides actionable insights for firms, regulators, and auditors into implementing robust governance and risk assessment practices to mitigate AI biases.
Effective AI bias auditing practices ensure algorithmic fairness and prevent discriminatory outcomes in critical domains like employment, healthcare, and financial services. The findings emphasize the need for enhanced stakeholder engagement and community representation in audit processes. Implementing robust auditing frameworks can help close socioeconomic gaps by identifying and mitigating biases disproportionately affecting marginalized groups. This research contributes to developing equitable AI systems that respect diversity and promote social justice while maintaining technological advancement.
This study contributes to the discourse on AI governance by comparing two regulatory approaches, bias audits, and conformity assessments, and offers practical lessons from current implementation. It highlights the critical role of standardization in advancing trustworthy and ethical AI systems in the finance and accounting contexts.
 

This author accepted manuscript is deposited under a Creative Commons Attribution Non-commercial 4.0 International (CC BY-NC) licence. This means that anyone may distribute, adapt, and build upon the work for non-commercial purposes, subject to full attribution. If you wish to use this manuscript for commercial purposes, please contact permissions@emerald.com.