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


Conference paper


Ariful Islam Anik, Andrea Bunt
CHI '21, Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, New York, NY, USA, 2021


View PDF
Cite

Cite

APA   Click to copy
Anik, A. I., & Bunt, A. (2021). Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote Transparency. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3411764.3445736


Chicago/Turabian   Click to copy
Anik, Ariful Islam, and Andrea Bunt. “Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote Transparency.” In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. CHI '21. New York, NY, USA: Association for Computing Machinery, 2021.


MLA   Click to copy
Anik, Ariful Islam, and Andrea Bunt. “Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote Transparency.” Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, 2021, doi:10.1145/3411764.3445736.


BibTeX   Click to copy

@inproceedings{anik2021a,
  title = {Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote Transparency},
  year = {2021},
  address = {New York, NY, USA},
  publisher = {Association for Computing Machinery},
  series = {CHI '21},
  doi = {10.1145/3411764.3445736},
  author = {Anik, Ariful Islam and Bunt, Andrea},
  booktitle = {Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems}
}


Share



Follow this website


You need to create an Owlstown account to follow this website.


Sign up

Already an Owlstown member?

Log in