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Hi. As an expert in machine learning, I would really love to hear your thoughts on data-centric machine learning, especially as it relates to the concepts of fairness, security, and explainability. Thanks.
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New York City, United States
You can say that all ML is data-centric since this is the basic facet machine learning models need to function. Fairness is a growing topic that is fortunately receiving much needed attention. Companies and individuals are realizing the harms that ML can pose and are actively working to address this. Explainability is also a big factor in making ML models fair but is critically understudied, especially in the context of making ML "explainable" to those with lower technology or AI knowledge. Security can be a big issue but is usually handled on the networking side of things (data storage, transmission, etc.).
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