Although artificial intelligence is a growing area of research, several problems remain. One such problem of particular importance is the low accuracy of predictions. This paper suggests that users' help is a practical approach to improve accuracy and it considers four factors that trigger users' willingness to help for an imperfect AI system. The two factors covered in Study 1 are utilitarian benefit based on egoistic motivation, and empathy based on altruistic motivation. In Study 2, utilitarian benefit is divided into explainable AI and monetary reward. The results indicate that two variables, namely empathy and monetary reward, have significant positive effects on willingness to help, and monetary reward is the strongest stimulus. In addition, explainable AI is shown to be positively associated with trust in AI. This study applies social studies of help motivation to the HCI field in order to induce users' willingness to help for an imperfect AI. The triggers of help motivation, empathy and monetary reward, can be utilized to induce the users’ voluntary engagement in the loop with an imperfect AI.
Bibliographical noteFunding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education ( NRF-2016R1D1A1B02015987 ).
All Science Journal Classification (ASJC) codes
- Arts and Humanities (miscellaneous)
- Human-Computer Interaction