Artificial Intelligence and Tax Compliance in the Gig Economy: Implications for Compliance, Privacy, and Fairness

Underreported income driven by the rapid growth of the gig economy poses significant challenges to tax authorities, particularly in developing countries. This study explores how AI-based tax Detection Mechanisms (AITDMs) can enhance tax compliance among gig workers. Drawing on the Slippery Slope Framework, it also examines privacy concerns and perceived fairness as moderating factors. A survey of 130 Nigerian gig workers was analysed using Partial Least Squares Structural Equation Modelling. Automated anomaly detection and predictive audit targeting significantly increased tax compliance, whereas data integration and real-time reporting tools did not. Neither privacy concerns nor perceived fairness significantly moderated the AI–compliance relationship. The findings suggest that perceived detection risk, rather than fairness or privacy considerations, primarily drives compliance. The study contributes empirical evidence from a developing-country context and underscores the value of AITDMs for gig-economy tax administration

Artificial intelligence, Gig economy, Tax evasion, tax compliance

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