Unintended Bias Analysis

Tools and resources to help analyze and ameliorate unintended bias in text classification models, as well as datasets for evaluating and mitigating unintended bias.

This work is part of the Conversation AI project, a collaborative research effort exploring ML as a tool for better discussions online.

Relevant Links:

• Our overview of unintended bias in machine learning models
• Unintended bias analysis dataset - includes crowd-worker demographic information, and toxicity ratings to support analysis of potential demographic-correlated unintended bias.
• Ai.withthebest.com April 29, 2017 Keynote Talk on ML Fairness
• Wikimedia Research Showcase - November 2017: Conversation Corpora, Emotional Robots, and Battles with Bias

Training toxicity models

We provide notebooks to train CNN based models to detect toxicity in online comments. The notebook unintended_ml_bias/Train Toxicity Model.ipynb provides instructions on how to train models using the Unintended bias analysis dataset. The notebook unintended_ml_bias/Evaluate Model.ipynb provides an example of evaluating the performance of pre-trained models on an arbitrary dataset.

To run the Python notebooks:

1. Install the requirements with

pip install -r requirements.txt

2. Download the data from the Unintended bias analysis dataset to the data/ subdirectory.

curl -L https://ndownloader.figshare.com/files/7394542 -o data/toxicity_annotated_comments.tsv
curl -L https://ndownloader.figshare.com/files/7394539 -o data/toxicity_annotated.tsv

3. Download and extract the GloVe embeddings in the data subdirectory.

curl -L http://nlp.stanford.edu/data/glove.6B.zip -o data/glove.6B.zip
unzip -x data/glove.6B.zip -d data/glove.6B

Please note that if using a virtual environment, it may be necessary to manually set your PYTHONPATH environment variable in the shell to the correct version of python for the environment.

4. Now you can open and evaluate unintended_ml_bias/Train_Toxicity_Model.ipynb:

jupyter notebook unintended_ml_bias/Train_Toxicity_Model.ipynb

Dataset bias evaluation

TODO(jetpack): add notes, screenshots for the dataset bias analysis tool

Model bias evaluation

"Bias madlibs" eval dataset

This dataset is one tool in evaluating our de-biasing efforts. For a given template, a large difference in model scores when single words are substituted may point to a bias problem. For example, if "I am a gay man" gets a much higher score than "I am a tall man", this may indicate bias in the model.

The madlibs dataset contains 89k examples generated from templates and word lists. The dataset is eval_datasets/bias_madlibs_89k.csv, a CSV consisting of 2 columns. The generated text is in Text, and the label is Label, either BAD or NOT_BAD.

The script (unintended_ml_bias/bias_madlibs.py) and word lists (unintended_ml_bias/bias_madlibs_data/) used to generate the data are also included.

TODO(jetpack): add notes about future work / improvements.

Fuzzed test set

This technique involves modifying a test set by "fuzzing" over a set of identity terms in order to evaluate a model for bias.

Given a test set and a set of terms, we replace all instances of each term in the test data with a random other term from the set. The idea is that the specific term used for each example should not be the key feature in determining the label for the example. For example, the sentence "I had a friend growing up" should be considered non-toxic, and "All people must be wiped off the earth" should be considered toxic for all values of x in the terms set.

The code in unintended_ml_bias/Bias_fuzzed_test_set.ipynb reads the Wikipedia Toxicity dataset and builds an identity-term-focused test set. It writes unmodified and fuzzed versions of that test set. One can then evaluate a model on both test sets. Doing significantly worse on the fuzzed version may indicate a bias in the model. The datasets are eval_datasets/toxicity_fuzzed_testset.csv and eval_datasets/toxicity_nonfuzzed_testset.csv. Each CSV consists of 3 columns: ID unedr rev_id, the comment text under comment, and the True/False label under toxic.

This is similar to the bias madlibs technique, but has the advantage of using more realistic data. One can also use the model's performance on the original vs. fuzzed test set as a bias metric.

Dataset de-biasing

TODO(jetpack): add dataset of additional examples as TSV with columns rev_id, comment, and split. also add tool to recreate dataset from wikipedia dump.

TODO(nthain,jetpack): upload new model trained on new dataset.

This technique mitigates the dataset bias found in the Dataset bias evaluation section by determining the deficit of non-toxic comments for each of the bias terms split by comment lengths. This deficit is then addressed by sampling presumed non-toxic examples from Wikipedia articles.

These new examples can then be added to the original dataset (to all splits, training as well as test). This allows (1) training a new model on the augmented, de-biased dataset and (2) evaluating the new and old models on the augmented test set in addition to the original test set.

Qualitative model comparison

TODO(jetpack): add tools and some screenshots.

These tools provide qualitative comparisons of model results on a given dataset. We find these tools useful for understanding the behavior of similar models, such as when testing different de-biasing techniques.

The confusion matrix diff tool shows tables of the largest score changes for the same examples, segmented according to the different sections of the confusion matrix. It shows the "new" false positives/negatives and true positives/negatives that one would get if upgrading from one model to the other.

The score scatterplot tool plots the model's scores as a scatterplot. Each point represents an example, the original model's score is the x position and the new model's score is the y position. The points are colored according to the true label. For similar models, most points should fall close to the y=x line. Points that are far from that line are examples with larger score differences between the two models.

If the new model on the y-axis is a proposed update to the model on the x-axis, then one would hope to see mostly positive labels in the upper left corner (new true positives) and mostly negative labels in the bottom right corner (new true negatives).

Data Description

The Prep Wikipedia Data.ipynb notebook will generate the following datasets where SPLIT indicates whether the data is in the train, test, or dev splits:

wiki_SPLIT.csv: The original Wikipedia data from the Figshare dataset, processed and split. wiki_debias_SPLIT.csv: The above data which is additionally augmented with Wikipedia article comments to debias on a set of terms (see Dataset_bias_analysis.ipynb for details). wiki_debias_random_SPLIT.csv: The wiki_SPLIT.csv augmented with a random selection of Wikipedia article columns that are of roughly the same length as those used to augment wiki_debias_SPLIT.csv. This is used as a control in our experiments.