Bias Detection and Correction Techniques for OpenAI Models

import openai

# Load your AI model

model = openai.load("your-model")

# Analyze bias in embeddings

biased_words = openai.detect_bias(model, words=["race", "gender", "ethnicity"])

print("Biased words:", biased_words)

from sklearn.model_selection import train_test_split

from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import accuracy_score

# Prepare your labeled dataset (biased vs. unbiased)

X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2)

# Vectorize text using TF-IDF

vectorizer = TfidfVectorizer()

X_train = vectorizer.fit_transform(X_train)

X_test = vectorizer.transform(X_test)

# Train a bias detection classifier

classifier = LogisticRegression()

classifier.fit(X_train, y_train)

# Evaluate the classifier

y_pred = classifier.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)

print("Bias detection accuracy:", accuracy)

from imblearn.over_sampling import RandomOverSampler

# Apply over-sampling to balance classes

oversampler = RandomOverSampler()

X_resampled, y_resampled = oversampler.fit_resample(X_train, y_train)

# Train the AI model on the balanced data

model.fit(X_resampled, y_resampled)

from aif360.algorithms.inprocessing import AdversarialDebiasing

# Initialize the debiasing algorithm

debiasing = AdversarialDebiasing(unprivileged_groups, privileged_groups, num_epochs=50)

# Fit the debiasing algorithm to your data

debiasing.fit(X_train, y_train)

# Use the debiased model for predictions

y_pred_debiased = debiasing.predict(X_test)