Can Reciprocal Wisdom Come True? Exploring Human Responses to AI Capability Augmentation
51 Pages Posted: 11 Dec 2022 Last revised: 18 Jan 2024
Date Written: January 18, 2024
Abstract
Artificial intelligence (AI) capability continuously evolves through interactions with accumulated data and domain experts. This is achieved through ongoing learning to enhance specific algorithms for decision-making from human individuals in diverse industries–e.g., trading strategies from financial traders, medical treatment from clinical specialists, and delivery logistics from food delivery workers. Would individuals be more inclined to follow AI advice if they understood that AI systems acquire wisdom from humans? Recent literature suggests that high-experienced individuals tend to resist AI guidance, a notion we challenge in our investigation. We posit that high experience correlates with recognizing AI's capacity augmentation through sensitive learning, which renders individuals more amenable to regulating their attitudes and following AI recommendations, especially when AI resembles and outperforms their decision-making. Testing our hypotheses in the on-demand food delivery domain, we find the following: (1) High-experienced human riders show increased compliance with more human-like AI augmentation. (2) Their short-term performance becomes more balanced, with improved hourly delivery productivity but decreased on-time delivery ratios. (3) Mechanism analysis reveals their proactive shifts from prioritizing personal preferences to a balanced approach recommended by AI. (4) Over the long term, high-experienced riders recover on-time delivery ratios through self-regulated learning. (5) Low-experienced riders who consistently adhere to AI suggestions also benefit from AI capability augmentation in food delivery performance. Our findings delineate a dynamic cycle of mutual learning and reinforcement to demonstrate reciprocal wisdom between AI and humans, which underscores the critical role of high-experienced humans in achieving superior collaborative task outcomes in human-AI system evolution.
Keywords: AI Capability Augmentation, Human Experience, Human-AI Collaboration, On-demand Food Delivery, Self-regulation
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