Abstract: Artificial intelligence (AI) and machine learning models are being increasingly deployed in real-world applications. In many of these applications, there is strong motivation to develop hybrid systems in which humans and AI algorithms can work together, leveraging their complementary strengths and weaknesses.
In the first part of the presentation, I will discuss results from a Bayesian framework where we statistically combine the predictions from humans and machines while taking into account the unique ways human and algorithmic confidence is expressed. The framework allows us to investigate the factors that influence complementarity, where a hybrid combination of human and machine predictions leads to better performance than combinations of human or machine predictions alone.
In the second part of the presentation, I will discuss some recent work on AI-assisted decision making where individuals are presented with recommended predictions from classifiers. Using a cognitive modeling approach, we can estimate the AI reliance policy used by individual participants. The results show that AI advice is more readily adopted if the individual is in a low confidence state, receives high-confidence advice from the AI and when the AI is generally more accurate.
In the final part of the presentation, I will discuss the question of “machine theory of mind” and “theory of machine”, how humans and machines can efficiently form mental models of each other. I will show some recent results on theory-of-mind experiments where the goal is for individuals and machine algorithms to predict the performance of other individuals in image classification tasks. The results show performance gaps where human individuals outperform algorithms in mindreading tasks. I will discuss several research directions designed to close the gap.