Accounting and auditing with blockchain technology and artificial Intelligence: A literature review

This paper surveys the published work on how blockchain technology will impact accounting in general, but AI-enabled auditing specifically. The purpose is to investigate how blockchain technology can improve transparency and trust in accounting practice and how professionals can use blockchain data to improve decision-making, based on the qualities of immutability, append-only, shared, verified, and agreed-upon (i.e., consensus-driven) blockchain data. The multi-party validation of blockchain protocols adds real-time trusted data for the AI systems used by auditors to improve assurance and efficiency. This review summarizes four themes emerging from the literature focusing on how blockchain technology has changed record-keeping in accounting: event approach to accounting; real-time accounting; triple entry-accounting and continuous auditing. The research interprets the findings using agency theory and stakeholder theory to advance how using blockchain to mitigate information asymmetry and improve stakeholder collaborations is understood. The investigation also summarizes the challenges and clarifies organizations’ reasons to be cautious about adopting blockchain. Lastly, the study suggests that future researchers use this study in two ways that enrich blockchain literature: first, to apply the themes and answer the questions identified within this review to improve the business methods of practitioners and policymakers; and second, to encourage stakeholders such as practitioners, system designers/developers, and policymakers to collaborate in designing blockchain ecosystems that suit accounting and auditing as they transform digitally.     

Ethical implications of text generation in the age of artificial intelligence

We are at a turning point in the debate on the ethics of Artificial Intelligence (AI) because we are witnessing the rise of general-purpose AI text agents such as GPT-3 that can generate large-scale highly refined content that appears to have been written by a human. Yet, a discussion on the ethical issues related to the blurring of the roles between humans and machines in the production of content in the business arena is lacking. In this conceptual paper, drawing on agenda setting theory and stakeholder theory, we challenge the current debate on the ethics of AI and aim to stimulate studies that develop research around three new challenges of AI text agents: automated mass manipulation and disinformation (i.e., fake agenda problem), massive low-quality content production (i.e., lowest denominator problem) and the creation of a growing buffer in the communication between stakeholders (i.e., the mediation problem).     

Mind the gender gap: Inequalities in the emergent professions of artificial intelligence (AI) and data science

The emergence of new prestigious professions in data science and artificial intelligence (AI) provide a rare opportunity to explore the gendered dynamics of technical careers as they are being formed. In this paper, we contribute to the literature on gender inequality in digital work by curating and analysing a unique cross-country data set. We use innovative data science methodology to investigate the nature of work and skills in these under-researched fields. Our research finds persistent disparities in jobs, qualifications, seniority, industry, attrition and even self-confidence in these fields. We identify structural inequality in data and AI, with career trajectories of professionals differentiated by gender, reflecting the broader history of computing. Our work is original in illuminating gendering processes within elite high-tech jobs as they are being configured. Paying attention to these nascent fields is crucial if we are to ensure that women take their rightful place at forefront of technological innovation.