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Fair AI: bias correction, AI testing and AI governance

Is your AI fair and explainable? Fix embedded biases and create model explainability with synthetic data!
Fairness and explainability challenges in AI and machine learning
  • According to Gartner, by 2022, 85% of algorithms will be erroneous due to bias.
  • Biased data is bad for business. From discriminatory hiring algorithms to sexist credit scoring models, numerous fairness scandals prove that the bias damage is both social and financial in nature.
  • AI regulations are coming across the world. The European Union has already made its proposal to regulate AI and the datasets used to train AI to enforce fairness and safety standards.
  • Regulatory oversight is needed. However, companies using AI are not prepared to demonstrate compliance and offer explainability to regulators.
  • Biased algorithms lead to systemic bad decisions, which affect companies at scale.
The status quo in fairness and explainability

There are millions of AI algorithms already in production. Only a small portion of them were audited for fairness. Companies putting untested, biased algorithms into production run the risk of getting into serious trouble not only from a PR perspective, but by way of making bad business decisions. After all, biased data will lead to biased business decisions, underserved minority groups and inexplicable results. From faulty pricing models in insurance to suboptimal prediction outcomes in healthcare, algorithmic fairness is a long stretch away from reality. Instead of tackling bias head on at the data level, most model developers simply delete sensitive personal data connected to race, ethnicity, religion and the likes. This masking approach does not eliminate bias, since discriminatory patterns continue to exert their influence on model behavior via proxy variables. Deleting data only makes biased decisions difficult to explain. 

Synthetic data for algorithmic fairness and explainability

Good quality AI-generated synthetic data can reduce bias in datasets by representing data with appropriate balance, density, distribution, and other crucial parameters. Synthetic data also provides the foundations for explainable AI or XAI. Algorithmic audits need synthetic data, that is free to share with regulators and provides a window into the workings of AI algorithms. Where sensitive training data cannot be shared further, highly representative synthetic data can serve as a drop-in placement to provide model documentation, model validation, and model certification. Synthetic data generated by MOSTLY AI's synthetic data platform corrected a skew towards racial bias in crime prediction from 24% to just 1 % and narrowed the gap between high-earning men and women from 20% to 2% in the US census dataset.

Read the Fairness Series to learn how to generate fair synthetic data!

Case studies and guides

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