Learning in Human-AI Collaboration

Abstract

Human–artificial intelligence (AI) collaboration is increasingly prevalent in organizations. This study investigates how human decision makers learn during repeated interactions with AI---specifically, machine learning models---and how these learning processes are impacted by AI design, including the transparency and complexity of the model's decision rules. We conceptualize learning in human–AI collaboration as having two components: learning from AI to improve independent decision making and learning about AI to improve the weighting of AI advice in collaboration. These components are integrated to make collaborative decisions.

Through a laboratory experiment where 288 participants make housing price predictions, we find that transparent AI (particularly ones with sparse representations) helps participants learn from AI, improving their future independent decisions. In contrast, black-box or more complex transparent AI facilitates learning about AI by reducing participants' tendency to inappropriately discount AI advice, leading to more effective weighting of AI inputs.

Participants' prior decision-making ability moderates these effects and thereby affects the integrated collaborative decision-making performance. Low-ability participants benefit most from transparent AI with moderate complexity, which balances the two components of learning from AI and learning about AI.  High-ability participants perform best with black-box AI---their strong baseline performance reduces the need to learn from AI, allowing these AI designs to focus on mitigating their greater tendency to inappropriately discount AI advice. Notably, this latter finding diverges from the common belief that black-box AI is inherently problematic for users. These findings highlight the importance of aligning AI design with user capabilities in organizations.

Keywords: AI Transparency, AI Complexity, Human--AI Collaboration, Organizational Learning