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Tutorial: Use counterfactual texts to mitigate bias

In this notebook you will see how to use biaslyze to test a NLP model for possible bias and then use the counterfactual samples to mitigate it. We revisit the task from the previous tutorial on bias detection, make sure to check it out first.

Installation

First install the biaslyze python package from pypi using:

!pip install biaslyze

import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

Load and prepare data

df = pd.read_csv("../data/jigsaw-toxic-comment-classification/train.csv"); df.head()
id comment_text toxic severe_toxic obscene threat insult identity_hate
0 0000997932d777bf Explanation\nWhy the edits made under my usern... 0 0 0 0 0 0
1 000103f0d9cfb60f D'aww! He matches this background colour I'm s... 0 0 0 0 0 0
2 000113f07ec002fd Hey man, I'm really not trying to edit war. It... 0 0 0 0 0 0
3 0001b41b1c6bb37e "\nMore\nI can't make any real suggestions on ... 0 0 0 0 0 0
4 0001d958c54c6e35 You, sir, are my hero. Any chance you remember... 0 0 0 0 0 0

Now make the classification problem binary:

Apart from the descriptive multi-label toxicity labels, there is another target column with a binary class signifying if a comment text is toxic or non-toxic.

df["target"] = df[["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]].sum(axis=1) > 0

Train a BoW-model

train_df, test_df = train_test_split(df, test_size=0.33, random_state=42)

clf = make_pipeline(TfidfVectorizer(min_df=10, max_features=30000, stop_words="english"), LogisticRegression(C=10))

clf.fit(train_df.comment_text, train_df.target)

Pipeline(steps=[('tfidfvectorizer',
                 TfidfVectorizer(max_features=30000, min_df=10,
                                 stop_words='english')),
                ('logisticregression', LogisticRegression(C=10))])
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# measure the test performance
y_pred = clf.predict(test_df.comment_text)

score = accuracy_score(test_df.target, y_pred)
print("Test accuracy: {:.2%}".format(score))

Test accuracy: 95.90%

Counterfactual token based bias detection

Now that we have a model to test, lets evaluate it with the biaslyze tool and test for bias with regards to the concept 'religion'. 

from biaslyze.bias_detectors import CounterfactualBiasDetector

bias_detector = CounterfactualBiasDetector()

counterfactual_detection_results = bias_detector.process(
    texts=train_df.comment_text,
    labels=train_df.target.tolist(),
    predict_func=clf.predict_proba,
    concepts_to_consider=["religion"],
    max_counterfactual_samples_per_text=10,
)

2023-06-29 11:56:08.539 | INFO     | biaslyze.concept_detectors:detect:35 - Started keyword-based concept detection on 106912 texts...
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 106912/106912 [00:25<00:00, 4150.42it/s]
2023-06-29 11:56:34.306 | INFO     | biaslyze.concept_detectors:detect:51 - Done. Found 96239 texts with protected concepts.
2023-06-29 11:56:34.307 | INFO     | biaslyze.bias_detectors.counterfactual_biasdetector:process:138 - Processing concept religion...
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 96239/96239 [12:33<00:00, 127.73it/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 96239/96239 [00:30<00:00, 3170.44it/s]
2023-06-29 12:09:38.130 | INFO     | biaslyze.bias_detectors.counterfactual_biasdetector:_extract_counterfactual_concept_samples:251 - Extracted 36800 counterfactual sample texts for concept religion from 2514 original texts.
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 19/19 [00:17<00:00,  1.06it/s]
2023-06-29 12:09:58.565 | INFO     | biaslyze.bias_detectors.counterfactual_biasdetector:process:198 - DONE

Lets look at some results

Here we only look at the religion concept for demonstration purposes.

counterfactual_detection_results.dashboard()

png

Here, we see some serious negative bias towards keywords related to jews and muslims.

Use the counterfactual samples to reduce bias

The bias detection method creates a lot of counterfactual text samples to test for bias. These texts can also be used to retrain or fine-tune a model to reduce this specific bias.

counterfactual_samples = counterfactual_detection_results._get_counterfactual_samples_by_concept(concept="religion")
len(counterfactual_samples)

36800

# prepare texts and labels
counterfactual_texts = [sample.text for sample in counterfactual_samples]
counterfactual_labels = [sample.label for sample in counterfactual_samples]
counterfactual_weights = [2 for sample in counterfactual_samples]

# add sample weight by bias
sample_weights = [0.5] * len(train_df.comment_text.tolist()) + counterfactual_weights

# retrain the model with the augmented dataset
mitigated_clf = make_pipeline(TfidfVectorizer(min_df=10, max_features=30000, stop_words="english"), LogisticRegression(C=10))
mitigated_clf.fit(
    train_df.comment_text.tolist() + counterfactual_texts,
    train_df.target.tolist() + counterfactual_labels,
    logisticregression__sample_weight=sample_weights
)

Pipeline(steps=[('tfidfvectorizer',
                 TfidfVectorizer(max_features=30000, min_df=10,
                                 stop_words='english')),
                ('logisticregression', LogisticRegression(C=10))])
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# measure the test performance after the mitigation
y_pred = mitigated_clf.predict(test_df.comment_text)

score = accuracy_score(test_df.target, y_pred)
print("Test accuracy: {:.2%}".format(score))

Test accuracy: 94.82%

Notice, that the mitigation procedure reduced the accuracy a bit.

# evaulate the bias again
mitigated_counterfactual_detection_results = bias_detector.process(
    texts=train_df.comment_text.sample(10000),
    labels=None,
    predict_func=mitigated_clf.predict_proba,
    concepts_to_consider=["religion"],
    max_counterfactual_samples_per_text=10,
)

2023-06-29 12:11:14.291 | INFO     | biaslyze.concept_detectors:detect:35 - Started keyword-based concept detection on 10000 texts...
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10000/10000 [00:02<00:00, 4053.82it/s]
2023-06-29 12:11:16.766 | INFO     | biaslyze.concept_detectors:detect:51 - Done. Found 8991 texts with protected concepts.
2023-06-29 12:11:16.767 | INFO     | biaslyze.bias_detectors.counterfactual_biasdetector:process:138 - Processing concept religion...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8991/8991 [01:09<00:00, 130.11it/s]
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2023-06-29 12:12:28.538 | INFO     | biaslyze.bias_detectors.counterfactual_biasdetector:_extract_counterfactual_concept_samples:251 - Extracted 3490 counterfactual sample texts for concept religion from 226 original texts.
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 19/19 [00:01<00:00, 12.61it/s]
2023-06-29 12:12:30.234 | INFO     | biaslyze.bias_detectors.counterfactual_biasdetector:process:198 - DONE

mitigated_counterfactual_detection_results.dashboard()

png

As we can see, the positive values are reduced and most values are now really close to zero which indicates less bias.