How do you deal with data bias and fairness in ML vs DL outcomes?
Data bias and fairness are crucial issues in any data-driven project, especially when using machine learning (ML) or deep learning (DL) techniques. These methods can amplify existing biases in the data or introduce new ones, affecting the quality, reliability, and ethics of the outcomes. How can you deal with data bias and fairness in ML vs DL outcomes? Here are some tips to help you.
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Kieran Gilmurray??♂?The Worlds 1st Chief Generative AI Officer ?? TTG's Chief AI Innovator ??? Key Note Speaker ?? 9x Global Award…
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Dr. Ivan Del VallePhD (Law), DBA, PgDip (RQF-L8), LLM, MBA, MDataSc, MCNeuroSc, MSR ? Head of Apsley Labs & Global AI Program Director ?…
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Muhammad NadeemConsultant - Data Science @Systems | LLM |Gen AI Engineer | Top LinkedIn ML Voice