ABSTRACT
This paper introduces fact-checking into Machine Learning (ML) explanation by referring training data points as facts to users to boost user trust. We aim to investigate what influence of training data points, and how they affect user trust in order to enhance ML explanation and boost user trust. We tackle this question by allowing users check the training data points that have the higher influence and the lower influence on the prediction. A user study found that the presentation of influences significantly increases the user trust in predictions, but only for training data points with higher influence values under the high model performance condition, where users can justify their actions with more similar facts.
- Or Biran and Kathleen McKeown. 2017. Human-centric Justification of Machine Learning Predictions. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17). 1461--1467. Google ScholarDigital Library
- Pang Wei Koh and Percy Liang. 2017. Understanding Black-box Predictions via Influence Functions. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017. Sydney, NSW, Australia, 1885--1894. Google ScholarDigital Library
- Travis Kriplean, Caitlin Bonnar, Alan Borning, Bo Kinney, and Brian Gill. 2014. Integrating On-demand Fact-checking with Public Dialogue. In Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW '14). 1188--1199. Google ScholarDigital Library
- Panagiotis Symeonidis, Alexandros Nanopoulos, and Yannis Manolopoulos. 2009. MoviExplain: A Recommender System with Explanations. In Proceedings of the Third ACM Conference on Recommender Systems. 317--320. Google ScholarDigital Library
- L. Richard Ye and Paul E. Johnson. 1995. The Impact of Explanation Facilities on User Acceptance of Expert Systems Advice. MIS Quarterly 19, 2 (June 1995), 157--172. Google ScholarDigital Library
- Jianlong Zhou and Fang Chen. 2018. DecisionMind: revealing human cognition states in data analytics-driven decision making with a multimodal interface. Journal of Multimodal User Interfaces 12, 2 (2018), 67--76.Google ScholarCross Ref
- Jianlong Zhou and Fang Chen (Eds.). 2018. Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent. Springer, Cham. Google ScholarDigital Library
- Jianlong Zhou, M. Asif Khawaja, Zhidong Li, Jinjun Sun, Yang Wang, and Fang Chen. 2016. Making Machine Learning Useable by Revealing Internal States Update -- A Transparent Approach. International Journal of Computational Science and Engineering 13, 4 (2016), 378--389. Google ScholarDigital Library
- Jianlong Zhou, Zelin Li, Weiming Zhi, Bin Liang, Daniel Moses, and Laughlin Dawes. 2017. Using Convolutional Neural Networks and Transfer Learning for Bone Age Classification. In 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2017). 1--6.Google Scholar
- Jianlong Zhou, Jinjun Sun, Yang Wang, and Fang Chen. 2017. Wrapping Practical Problems into a Machine Learning Framework: Using Water Pipe Failure Prediction As a Case Study. International Journal of Intelligent Systems Technologies and Applications 16, 3 (2017), 191--207. Google ScholarDigital Library
Index Terms
- Effects of Influence on User Trust in Predictive Decision Making
Recommendations
Examining Mobile Banking User Trust: A Tripartite Perspective
Building users' trust is crucial to alleviating their perceived risk and facilitating their usage of mobile banking. Drawing on a tripartite perspective of transference-based, personality-based and self-perception-based determinants, this research ...
Re-examining the influence of trust on online repeat purchase intention: The moderating role of habit and its antecedents
Customer loyalty or repeat purchasing is critical for the survival and success of any store. By focusing on online stores, this study investigates the moderating role of habit on the relationship between trust and repeat purchase intention. Prior ...
The Role of Satisfaction as a Moderation of the Influence of Trust on Consumer Loyalty
ICIEB '18: Proceedings of the 2018 1st International Conference on Internet and e-BusinessThis research was conducted to find out the role of satisfaction as moderation of influence of trust to loyalty at MatahariMall.com. Sampling technique that used in this research is non probability sampling technique and data analysis using moderated ...
Comments