The Evaluation of the Black Box Problem for AI-Based Recommendations: An Interview-Based Study

Organizations are increasingly adopting artificial intelligence (AI) for business processes. AI-based recommendations aim at supporting users in decision-making, e.g., by pre-filtering options. However, users can often hardly understand how these recommendations are developed. This issue is called “black box problem”. In the context of Human Resources Management, this leads to new questions regarding the acceptance of AI-based recommendations in the recruiting process. Therefore, we develop a model based on the theory of planned behavior explaining the relation between the user’s perception of the black box problem and the attitude toward AI-based recommendations distinguishing between a mandatory and voluntary use context. We conducted 21 interviews with experts from recruiting and AI. Our results show that the perception of the black box problem conceptualized by the awareness and the evaluated relevance relates to the user’s attitude toward AI-based recommendations. Further, we show that the use context has a moderating effect on that relation.