Improving Data Quality through Deep Learning and Statistical Models
By Wei Dai, Kenji Yoshigoe, William Parsley. 2018
36 citations recorded by the source corpus.
Traditional data quality control methods are based on users’ experience or previously established business rules, and this limits performance in addition to being a very time consuming process with lower than desirable accuracy. Utilizing deep learning, we can leverage computing resources and advanced techniques to overcome these challenges and provide greater value to users.
Subfields: Information Systems, Management Information Systems, Management Science and Operations Research
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Citations in the corpus
- Understanding artificial intelligence ethics and safety (2019, methodology): “Detecting concept drift in data streams using model explanation. Expert Systems with Applications, 92, 546–559. https://doi.org/10.1016/j.eswa.2017.10.003 Google. (2019). Perspectives on issues in AI governance. Retrieved from https://ai.google/static /documents/perspectives-on-issues-in-ai-governan”
- The ethics of AI in health care: A mapping review. (2020, background): “…some technical solutions have been put forward for mitigating issues with data bias (Gebru et al., 2018; Holland et al., 2018) and data quality (Dai et al., 2018) and ensuring social inclusion in decision-making (Balthazar et al., 2018; Friedman et al., 2017; Rahwan, 2018), these remain…”