AI Recommendations and Non-instrumental Image Concerns

We are witnessing a surge in the use of artificial intelligence (AI) systems across various work environments, a trend likely to intensify in the coming years. As algorithmic predictive capabilities advance and digitization and data collection efforts expand, new opportunities are emerging to observe how professionals make decisions with the assistance of these tools. Machine learning algorithms are eclipsing experts in many areas, including bail judges predicting pretrial misconduct (Kleinberg et al. 2018), radiologists predicting pneumonia from chest X-rays (Rajpurkar et al. 2017; Topol 2019), and workforce professionals predicting productivity for hiring and promotion (Chalfin et al. 2016). While AI has the potential to outperform many human professionals, there is hope that human-AI collaboration can yield even better results by leveraging the unique strengths of each. AI excels at processing large volumes of data and remains free from emotional biases, while humans may possess private information or be better equipped to handle edge cases. One of the most popular decision-making frameworks involves humans making choices based on AI recommendations, a structure that preserves decision-making authority in human hands.