Explainable AI for Goal Recommendation: Effects on Trust, Effort, and Retention of Gig Workers

Abstract

The increasing use of algorithmic management on digital labor platforms has transformed how work is coordinated, evaluated, and motivated. In the gig economy, goal recommendation systems powered by artificial intelligence are deployed to nudge worker behavior, optimize performance, and sustain engagement. However, the opacity of such systems raises critical questions about worker trust, effort allocation, and long-term retention. This paper examines the role of explainable artificial intelligence in goal recommendation for gig workers through a sociotechnical systems lens. We dissect the architectural layers of goal recommendation engines, their integration into platform infrastructures, and the governance mechanisms that shape transparency. By analyzing structural trade-offs between predictive accuracy and interpretability, we reveal how explanation modalities influence perceived autonomy, procedural fairness, and the calibration of worker effort. The discussion draws on cross-domain comparisons from algorithmic management, human-computer interaction, and organizational psychology to illustrate how explainability can be designed as a relational infrastructure rather than a mere feature. Special attention is given to the durability of trust when explanations are adaptive, contested, or strategically withheld, and how retention emerges as a systemic property of feedback loops between worker sensemaking, platform reputation, and labor supply dynamics. The paper concludes by outlining policy implications for platform accountability, algorithmic auditing, and the development of governance frameworks that align explainability requirements with the precarious realities of contingent work. We argue that sustainable gig workforce management hinges on the institutionalization of transparent goal recommendation systems that are not only technically sound but also socially legitimate.