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An Interactive Approach to Bias Identification in a Machine Teaching Task

Published: 17 March 2020 Publication History

Abstract

Supervised machine learning requires labelled data examples to train models, and those examples often come from humans who may not be experts in artificial intelligence (i.e., "AI"). Currently, many resources are devoted to these labelling tasks; a majority of which are outsourced by companies to reduce costs, and oversight on such tasks can be cumbersome. Concurrently, biases in machine learning models and human cognition are a growing concern in applications of AI.
In this paper, we present a machine teaching platform for non-AI experts that leverages interactive data exploration approaches to identify algorithmic and human (e.g., cognitive) biases. Our main objective is to understand how data exploration and explainability might impact the machine teacher (i.e., data labeller) and their understanding of AI, subsequently improving model performance, all while reducing potential bias concerns.

References

[1]
EU Commission, 2019. Ethics guidelines for trustworthy AI. Accessed 3 December 2019.
[2]
Jiang, B. and Canny, J., 2017. Interactive machine learning via a gpuaccelerated toolkit. In Proceedings of the 22nd International Conference on Intelligent User Interfaces. ACM Press, New York, NY, 535--546.
[3]
Sun, Y., Lank, E., and Terry, M. 2017. Label-and-Learn: Visualizing the Likelihood of Machine Learning Classifier's Success During Data Labeling. In Proceedings of the 22nd International Conference on Intelligent User Interfaces. ACM Press, New York, NY, 523--534.
[4]
Turner Lee, N. 2018. Detecting racial bias in algorithms and machine learning. Journal of Information, Communication and Ethics in Society, 16(3), 252--260.
[5]
Zhang, J., Wu, X., and Sheng, V.S. 2016. Learning from crowdsourced labeled data: a survey. Artificial Intelligence Review, 46(4), 543--576.

Cited By

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  • (2022)Is it time we get real? A systematic review of the potential of data-driven technologies to address teachers' implicit biasesFrontiers in Artificial Intelligence10.3389/frai.2022.9949675Online publication date: 11-Oct-2022

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  1. An Interactive Approach to Bias Identification in a Machine Teaching Task
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          cover image ACM Conferences
          IUI '20 Companion: Companion Proceedings of the 25th International Conference on Intelligent User Interfaces
          March 2020
          153 pages
          ISBN:9781450375139
          DOI:10.1145/3379336
          Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          Published: 17 March 2020

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          Author Tags

          1. Cognitive biases
          2. Data labeling
          3. Machine learning bias
          4. Machine teaching

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          View all
          • (2022)Is it time we get real? A systematic review of the potential of data-driven technologies to address teachers' implicit biasesFrontiers in Artificial Intelligence10.3389/frai.2022.9949675Online publication date: 11-Oct-2022

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