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Precision vs. Recall — Choosing the Right Metric for the Job
Second in a Series: Mastering Data Quality for Safe & Scalable AI Saibal Banerjee, PhD and Steven Atneosen, JD IntroductionIn order to master data quality for safe and scalable AI, precision and recall go beyond the simplicity of accuracy by helping quantify the nature of classification errors. These metrics are essential for professionals aiming to…
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Accuracy Isn’t Enough: Why You Need to Rethink AI Model Metrics
First in a Series: Mastering Data Quality for Safe & Scalable AI Saibal Banerjee, PhD and Steven Atneosen, JD Introduction Accuracy is the most commonly cited metric in machine learning. But if your dataset is imbalanced, accuracy can be dangerously misleading. For professionals working with sensitive PII, this misdirection can be catastrophic. Understanding how your…
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If you can’t measure data privacy, you can’t manage data privacy.
Data privacy is important to consumers and a material compliance obligation of enterprises who serve them, but difficult to achieve – and prove – due to the absence of clear standards. Pew Research reports that over 80% of Americans highly value their data privacy and don’t trust enterprises to properly protect it. “Privacy” is defined…
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Ethical AI begins with trusted data that has fidelity and privacy
As I prepared to speak with my fellow ethical AI panelists for the April 24, 2023 EAIGG discussion “New Wave of Data Centric AI Startups”, I thought further about how to articulate the mandate and opportunity presented by AI to solve critical problems faced by humanity. We are in the infancy of enterprise AI. The…
