Abstract
Data readiness analysis consists of methods that profile data and flag quality issues to determine the AI readiness of a given dataset. Such methods are being increasingly used to understand, inspect and correct anomalies in data such that their impact on downstream machine learning is limited. This often requires a human in the loop for validation and application of remedial actions. In this paper we describe a tool to assist data workers in this task by providing rich explanations to results obtained through data readiness analysis. The aim is to allow interactive visual inspection and debugging of data issues to enhance interpretability as well as facilitate informed remediation actions by humans in the loop.
The first two authors have contributed equally to this paper.
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Afzal, S., Chaudhary, A., Gupta, N., Patel, H., Spina, C., Wang, D. (2021). Data-Debugging Through Interactive Visual Explanations. In: Gupta, M., Ramakrishnan, G. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12705. Springer, Cham. https://doi.org/10.1007/978-3-030-75015-2_14
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DOI: https://doi.org/10.1007/978-3-030-75015-2_14
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