Addressing Bias in AI - Toolkit for Fairness, Explainability and Privacy

Dilbert was dropped from hundreds of newspapers over Scott Adams’ racist comments. Multiple researchers have documented over the past few months how ChatGPT can be prompted to provide racist responses.

A three-decade globally famous comic strip has been canceled because of the creator’s racist comments in his YouTube show. ChatGPT, Bing Bot, and many such AI Bots are conversing with millions of users daily and have been documented to provide misleading, inaccurate, and biased responses. How can we hold AI to the same high standards we expect from society, especially when AI is now generative and scaled for global consumer use?

While no silver bullet exists, multiple aspects can make AI more responsible. Having open AI models is a great start. Hugging Face, EleutherAI, and many others are championing an open approach to AI. Openness and collaboration can bring in diverse contributions, reviews, and rigorous testing of AI models and help reduce bias.

NIST’s AI risk management guidelines released recently provide a comprehensive view across the AI lifecycle consisting of collecting and processing Data & Input, the build, and validation of the AI model, its deployment, and monitoring in the context of usage. Acknowledging the possibility of bias, eliminating data capture biases, or unconscious biases when generating synthetic data, designing for counterfactual fairness, and human-in-loop designs can reduce the risk of bias.

Use the below tools for assessment and to improve the fairness and robustness of your models.

Use the below tools for Explainability, Interpretability, and Monitoring.

Google toolkit on Tensorflow for Privacy, Federated Learning, and Explainability.