Investigating the Importance of First Impressions and Explainable AI with Interactive Video Analysis
Mahsan Nourani, Donald R. Honeycutt, Jeremy E. Block, Chiradeep Roy, Tahrima Rahman, Eric D. Ragan, Vibhav Gogate
Abstract
We present research on how the perception of intelligent systems can be influenced by early experiences of machine performance, and how explainability potentially helps users develop an accurate understanding of system capabilities. Using a custom video analysis system with AI-assisted activity recognition, we studied whether presenting explanatory information for system outputs affects user perception of the system. In this experiment, some participants encountered AI weaknesses early, while others encountered the same limitations later in the study. The difference in ordering had a significant impact on user understanding of the system and the ability to detect AI strengths and weaknesses, and the addition of explanations was not enough to counteract the strong effects of early impressions. The results demonstrate the importance of first impressions with intelligent systems and motivate the need for improved methods of intervention to combat automation bias.