How to limit bias and discrimination in machine learning models?
To effectively limit bias and discrimination in machine learning models, it is essential to adopt a multi-faceted approach that encompasses the entire machine learning lifecycle, from data collection to model deployment and monitoring. Bias in machine learning can arise from various sources, including biased data, model assumptions, and the algorithms themselves. Addressing these biases requires
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
How to ensure transparency and understandability of decisions made by machine learning models?
Ensuring transparency and understandability in machine learning models is a multifaceted challenge that involves both technical and ethical considerations. As machine learning models are increasingly deployed in critical areas such as healthcare, finance, and law enforcement, the need for clarity in their decision-making processes becomes paramount. This requirement for transparency is driven by the necessity
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning