Overview

Preparing to train a model

  • Some tasks are not appropriate for machine learning due to ethical concerns.
  • Machine learning tasks should have a valid prediction target that maps clearly to the real-world goal.
  • Training data can be biased due to societal inequities, errors in the data collection process, and lack of attention to careful sampling practices.
  • “Bias” also refers to statistical bias, and certain algorithms can be biased towards some solutions.

Model evaluation and fairness

Model fairness: hands-on

  • It’s important to consider many dimensions of model performance: a single accuracy score is not sufficient.
  • There is no single definition of “fair machine learning”: different notions of fairness are appropriate in different contexts.
  • Representational harms and stereotypes can be perpetuated by generative AI.
  • The fairness of a model can be improved by using techniques like data reweighting and model postprocessing.

Interpretablility versus explainability

  • Model Explainability vs. Model Interpretability:
    • Interpretability: The degree to which a human can understand the cause of a decision made by a model, crucial for verifying correctness and ensuring compliance.
    • Explainability: The extent to which the internal mechanics of a machine learning model can be articulated in human terms, important for transparency and building trust.
  • Choosing Between Explainable and Interpretable Models:
    • When Transparency is Critical: Use interpretable models when understanding how decisions are made is essential.
    • When Performance is a Priority: Use explainable models when accuracy is more important, leveraging techniques like LIME and SHAP to clarify complex models.
  • Accuracy vs. Complexity:
    • The relationship between model complexity and accuracy is not always linear. Increasing complexity can improve accuracy up to a point but may lead to overfitting, highlighting the gray area in model selection. This is illustrated by the accuracy vs. complexity plot, which shows different models on these axes.

Explainability methods overview

Explainability methods: deep dive

Explainability methods: linear probe

Explainability methods: GradCAM

Estimating model uncertainty

  • TODO

OOD detection: overview, output-based methodsIntroduction to Out-of-Distribution (OOD) DataDetecting and Handling OOD DataExample 1: Softmax scoresExample 2: Energy-Based OOD DetectionLimitations of our approach thus far

OOD detection: distance-based and contrastive learningExample 3: Distance-Based MethodsLimitations of Threshold-Based OOD Detection Methods

OOD detection: training-time regularizationTraining-time regularization for OOD detection

Documenting and releasing a model

  • Model cards are the standard technique for communicating information about how machine learning systems were trained and how they should and should not be used.
  • Models can be shared and reused via the Hugging Face platform.