Evaluation & Ethics
NLP evaluation metrics, bias, fairness, and ethical considerations.
Bias in NLP
NLP systems absorb, reproduce, and often amplify societal biases present in training data, annotation practices, and modeling decisions, leading to systematic disadvantages for underrepresented groups.
Evaluation Metrics for NLP
Automated evaluation metrics quantify NLP system performance using formulas that approximate human judgment, each capturing a different facet of quality – from exact-match precision to semantic embedding similarity.
Fairness in NLP
Fairness in NLP formalizes the requirement that language technologies perform equitably across demographic groups, using mathematical definitions that reveal fundamental trade-offs between competing notions of what “fair” means.
Human Evaluation for NLP
Human evaluation remains the gold standard for assessing NLP system quality, using structured protocols with trained annotators to judge dimensions – fluency, adequacy, coherence – that automated metrics cannot reliably capture.
Intrinsic vs. Extrinsic Evaluation
Intrinsic evaluation measures a model component’s quality in isolation (e.g., perplexity for a language model), while extrinsic evaluation measures its contribution to a downstream end-task (e.g., translation accuracy).
NLP for Social Good
NLP technologies can address critical societal challenges – from extracting life-saving information from clinical notes to preserving endangered languages – when designed with care for the communities they serve.
Privacy in NLP
Language models memorize and can regurgitate sensitive training data – including personal identifiers, phone numbers, and medical records – creating privacy risks that require techniques like differential privacy, federated learning, and de-identification to mitigate.
Responsible NLP Development
Responsible NLP development encompasses the practices, documentation standards, and ethical frameworks – from model cards to carbon footprint accounting – that ensure language technologies are built, evaluated, and deployed with transparency, accountability, and awareness of potential harms.