The paper studies human-AI teams in decision-making settings specifically focusing on updates made to the AI component and its subsequent influence on the decision-making process of the human. In an AI-advised human decision-making interaction, the AI system recommends actions to the human. Based on this recommendation, their past experience as well as domain knowledge, the human takes an informed decision. They can choose to go ahead with the action recommended by the AI or they can choose to disregard the recommendation. During their course of interaction with AI systems, humans develop a mental model of the system. This is developed based on mapping scenarios where the AI’s decision was correct versus when they were incorrect by means of rewards and feedback provided to the humans by the system. As part of the experiment, studies were conducted to establish relationships between updates to AI systems and team performance. User behavior was monitored using a custom platform, CAJA, built to gain insights about the influence of updates to AI models on the user’s mental model and consequently team performance. Consistency metrics were introduced and several real-world domains were analyzed including recidivism prediction, in-hospital mortality prediction, and credit risk assessment.
It was extremely surprising to note that updates to the AI’s performance that makes it better actually may hurt the team performance. My initial instinct was that with an increase in the AI’s performance, the team performance would increase proportionally but this is not always the case. In certain cases, despite there being an increase in the AI’s performance, the new results might not be consistent with the human’s mental model and as a result, incorrect decisions are taken based on past interactions with the AI and hence the overall team performance decreases. An interesting and relatable parallel is drawn to concepts of backward compatibility in software engineering with respect to updates. The concept of compatibility is introduced using this analogy to describe the ideal scenario where updates to the AI does not introduce further errors.
The platform developed to conduct the studies, CAJA, was an innovative way to overcome the challenges of testing in real-world settings. This platform abstract away the specifics of problem-solving by presenting a range of problems that distills the essence of mental models and trust in one’s AI teammate. It was very interesting to note that these problems were designed such that no human could be a task expert thereby maximizing the importance of mental models and their influence in decision making.
- What are some efficient means to share the summary of AI updates in a concise, human-readable form that captures the essence of the update along with the reason for the change?
- What are some innovative ideas that can be used to reduce the cost incurred by re-training humans in an AI-advised human decision-making ecosystem?
- How can developers be made more aware of the consequences of the updates made to the AI model on team performance? Would increasing awareness help improve team performance?
