Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote Transparency-Reference-Cited by-同舟云学术

Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote Transparency

Published:2021-05-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
language:
Short-container-title:

Author:

Anik Ariful Islam¹,Bunt Andrea¹

Affiliation:

1. Computer Science Department, University of Manitoba, Canada

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3411764.3445736

Reference107 articles.

1. Intrigue: Personalized Recommendation of Tourist Attractions;Ardissono Liliana;Applied Artificial Intelligence: Special Issue on Artificial Intelligence for Cultural Heritage and Digital Libraries,2003

2. FactSheets: Increasing trust in AI services through supplier's declarations of conformity

3. Big Data's Disparate Impact;Barocas Solon;California Law Review,2016

4. Context-aware movie recommendation based on signal processing and machine learning

5. 'It's Reducing a Human Being to a Percentage'

Cited by 55 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Explainable AI for government: Does the type of explanation matter to the accuracy, fairness, and trustworthiness of an algorithmic decision as perceived by those who are affected?;Government Information Quarterly;2024-12

2. Representation Debiasing of Generated Data Involving Domain Experts;Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization;2024-06-27

3. An Explanatory Model Steering System for Collaboration between Domain Experts and AI;Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization;2024-06-27

4. A Review of Data-Centric Artificial Intelligence (DCAI) and its Impact on manufacturing Industry: Challenges, Limitations, and Future Directions;2024 IEEE Conference on Artificial Intelligence (CAI);2024-06-25

5. Towards Unlocking the Hidden Potentials of the Data-Centric AI Paradigm in the Modern Era;Applied System Innovation;2024-06-24