How to uncover and avoid structural biases in evaluating your Machine Learning/NLP projects

2024-06-16 · Source: Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, quick

Summary

A talk titled "How to uncover and avoid structural biases in evaluating your Machine Learning/NLP projects" addresses common pitfalls in evaluating Machine Learning and Natural Language Processing approaches. It aims to provide comprehensive advice for establishing a robust evaluation procedure. The presentation will also delve into specific use-cases to illustrate how artificial bias can inadvertently be introduced into ML and NLP projects during evaluation.

Key takeaway

For Machine Learning and NLP Engineers designing evaluation protocols, understanding potential structural biases is crucial. You should proactively identify common pitfalls and sources of artificial bias that can unknowingly affect your model assessments. Implement robust evaluation procedures to ensure fair and accurate performance metrics, preventing misleading results from subtle, inherent biases.

Key insights

Effective ML/NLP evaluation requires identifying and mitigating structural biases and common pitfalls.

Principles

• Evaluate ML/NLP for common pitfalls.
• Uncover subtle, artificial biases.

Topics

• Machine Learning Evaluation
• NLP Evaluation
• Structural Bias
• Evaluation Pitfalls
• Model Assessment

Best for: Machine Learning Engineer, NLP Engineer, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai.