Bias Monitoring
ML bias is a phenomenon where some aspects of datasets with equal significance are given more weight or representation than others, leading to skewed outcomes.
ML bias is a phenomenon where some aspects of datasets with equal significance are given more weight or representation than others, leading to skewed outcomes. In such cases, the errors are magnified in the final analytical results rendering the ML model inappropriate and ineffective.
In its simplest terms, bias is the situation where the model consistently predicts distorted results because of incorrect assumptions. When we train our model on a training set and evaluate it on a training set, a biased model produces significant losses or errors.
Liked the content? you'll love our emails!
See how AryaXAI improves
ML Observability
Learn how to bring transparency & suitability to your AI Solutions, Explore relevant use cases for your team, and Get pricing information for XAI products.