Measuring the importance of data quality to causal AI success
Dynatrace
JANUARY 4, 2024
Omissions can create wrong conclusions and contribute to bias. Another common impediment is manual data tagging and handling, an error-prone process that teams should minimize. Teams need to ensure the data is accurate and correctly represents real-world scenarios. Additionally, it’s important to consider all variables. Completeness.
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