Can Reciprocal Wisdom Come True? Exploring Human Responses to AI Capability Augmentation

51 Pages Posted: 11 Dec 2022 Last revised: 18 Jan 2024

See all articles by Tian Lu

Tian Lu

Department of Information Systems, Arizona State University

Xianghua Lu

Fudan University

Yiyu Huang

Fudan University

Hai Wang

Singapore Management University - School of Computing and Information Systems; Carnegie Mellon University - Heinz College of Information Systems and Public Policy

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

Suggested Citation

Lu, Tian and Lu, Xianghua and Huang, Yiyu and Wang, Hai, Can Reciprocal Wisdom Come True? Exploring Human Responses to AI Capability Augmentation (January 18, 2024). Available at SSRN: https://ssrn.com/abstract=4298793 or http://dx.doi.org/10.2139/ssrn.4298793

Tian Lu (Contact Author)

Department of Information Systems, Arizona State University ( email )

Tempe, AZ 85287
United States

HOME PAGE: http://isearch.asu.edu/profile/tianlu1

Xianghua Lu

Fudan University

Yiyu Huang

Fudan University

Hai Wang

Singapore Management University - School of Computing and Information Systems ( email )

80 Stamford Road
Singapore 178902, 178899
Singapore

Carnegie Mellon University - Heinz College of Information Systems and Public Policy ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
63
Abstract Views
746
Rank
631,514
PlumX Metrics