Text Analysis Tutorial: Data Augmentation & Fairness

[chinaexpert1]

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Title & Overview

Template: Data Augmentation & Fairness: An Intermediate, End-to-End Analysis Tutorial
Overview (≤2 sentences): Learners will implement text augmentation techniques (back-translation, paraphrasing, noising) and fairness checks (bias evaluation across slices). It is intermediate because it combines augmentation experiments with structured fairness/error analysis, reproducibility, and defensible reporting.

Purpose

The value-add is showing learners how to improve robustness with augmentation while also evaluating fairness impacts. This emphasizes reproducibility, slice-based fairness checks, and documenting trade-offs between performance gains and bias risks.

Prerequisites

• Skills: Python, Git, pandas, ML basics.
• NLP: embeddings, augmentation, fairness concepts (bias, group performance).
• Tooling: pandas, scikit-learn, Hugging Face Transformers + Datasets, nlpaug or TextAttack, MLflow, FastAPI.

Setup Instructions

• Environment: Conda/Poetry (Python 3.11), deterministic seeds.

• Install: pandas, scikit-learn, Hugging Face Transformers + Datasets, nlpaug or TextAttack, MLflow, FastAPI.

• Datasets:

  • Small: SST-2 (binary sentiment).
  • Medium: Civil Comments or Jigsaw Toxicity (for bias/fairness slices).

• Repo layout:

  tutorials/t13-augmentation-fairness/
    ├─ notebooks/
    ├─ src/
    │   ├─ augment.py
    │   ├─ fairness.py
    │   ├─ eval.py
    │   └─ config.yaml
    ├─ data/README.md
    ├─ reports/
    └─ tests/
  

Core Concepts

• Augmentation strategies: back-translation, synonym/paraphrase replacement, noise injection.
• Fairness evaluation: slice metrics across sensitive attributes (gender, identity terms).
• Trade-offs: augmentation improves robustness but may amplify bias.
• Evaluation: macro-F1, calibration, per-slice fairness metrics.
• Reproducibility: log augmentation seeds, configs, and dataset versions.

Step-by-Step Walkthrough

1. Data intake & splits: load SST-2 and Civil Comments/Jigsaw; reproducible splits.

2. Augmentation baselines:

   • Synonym replacement with WordNet.
   • Back-translation (EN→FR→EN).
   • Noise injection (typos, character swaps).

3. Model baselines: Logistic Regression (TF-IDF) and DistilBERT fine-tune.

4. Fairness evaluation: compute slice metrics by sensitive attribute groups (e.g., male/female, identity terms).

5. Error analysis: identify which groups see metric gains vs losses under augmentation.

6. Reporting: metrics tables, augmentation impact by slice, fairness analysis in reports/t13-augmentation-fairness.md.

7. (Optional) Serve: FastAPI endpoint with toggle for augmentation during training/inference; log fairness slice metrics.

Hands-On Exercises

• Ablations: original vs augmented training; synonym vs back-translation vs noise.
• Robustness: evaluate macro-F1 drop under domain-shift test set.
• Slice analysis: fairness metrics across sensitive groups before/after augmentation.
• Stretch: combine augmentation + debiasing (adversarial training, reweighting).

Common Pitfalls & Troubleshooting

• Over-augmentation: noisy data may harm clean test performance.
• Bias amplification: careless augmentation replicates group biases.
• Metrics misuse: reporting only aggregate F1 hides slice disparities.
• Reproducibility gaps: augmentation randomness must be seeded.
• OOM: back-translation is compute heavy — subset or batch.

Best Practices

• Always log augmentation configs (percent augmented, method, seeds).
• Compare augmentation strategies side by side.
• Evaluate both aggregate and slice metrics.
• Unit tests: verify augmentation reproducibility on fixed corpus.
• Guardrails: enforce maximum augmentation ratio to avoid overfitting noise.

Reflection & Discussion Prompts

• When does augmentation actually help vs hurt?
• How do fairness metrics change when training with noisy or synthetic data?
• What are ethical implications of bias amplification through augmentation?

Next Steps / Advanced Extensions

• Explore paraphrase generation with T5/BART.
• Apply counterfactual data augmentation for fairness (e.g., swapping gender terms).
• Lightweight monitoring: slice-level fairness metrics over time.
• Domain adaptation with augmentation + fairness auditing.

Glossary / Key Terms

Augmentation, back-translation, paraphrasing, noise injection, fairness, slice metrics, bias amplification.

Additional Resources

• [TextAttack](https://textattack.readthedocs.io/)
• [nlpaug](https://github.com/makcedward/nlpaug)
• [Hugging Face Datasets](https://huggingface.co/datasets)
• [MLflow](https://mlflow.org/)
• [FastAPI](https://fastapi.tiangolo.com/)

Contributors

Author(s): TBD
Reviewer(s): TBD
Maintainer(s): TBD
Date updated: 2025-09-20
Dataset licenses: SST-2 (GLUE), Civil Comments/Jigsaw (CC).

Issues Referenced

Epic: HfLA Text Analysis Tutorials (T0–T14).
This sub-issue: T13: Data Augmentation & Fairness.

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