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Fair AI: bias correction, AI testing and AI governance

Fair AI and explainability need AI help. Fix embedded biases and create model explainability with synthetic data!

Fair AI and explainability challenges in AI and ML development

  • According to Gartner, by now 85% of algorithms are 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 for training to enforce the creation of fair AI and safety standards, especially for high-risk use cases.
  • 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 fair AI and AI explainability

There are millions of AI algorithms already in production. Only a small portion of them were audited for fairness. Fair AI is still only talked about in the future tense by most AI engineers. 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. 

The current landscape of fair AI and AI explainability is marked by a stark discrepancy between the growing recognition of their importance and the actual efforts undertaken to address them. While academic conferences, think tanks, and even some regulatory bodies are putting an increasing focus on the need for AI to be both fair and explainable, these discussions often don't translate into actionable steps within organizations.

Many companies are still in the early stages of understanding what it means to implement fair and explainable AI systems. The common practice of simply deleting sensitive attributes like race, ethnicity, or religion from datasets is a glaring example of the superficial approaches that fail to address the root cause of the problem. This not only perpetuates biases through proxy variables but also obfuscates the decision-making process, making it even harder to audit and explain the AI model's behavior.

The result is a landscape where algorithmic decisions, although increasingly critical in everything from loan approvals to medical diagnoses, lack both fairness and transparency. This undermines public trust in AI systems and exposes organizations to both ethical scrutiny and legal repercussions. And while there are tools and methods available for auditing algorithms, their adoption remains woefully limited, often considered as an afterthought rather than a fundamental part of AI development. Consequently, the industry is caught in a cycle of deploying algorithms that neither the creators nor the end-users fully understand or trust, perpetuating a status quo that is increasingly at odds with societal demands for fairness, accountability, and transparency.

Synthetic data for fair AI and explainability

Good quality AI-generated synthetic data can reduce bias in datasets and create fair AI systems 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!

As for explainable AI, synthetic data can play a critical role in the auditing process. Auditors and regulators often require access to the data that trained a given model to validate its performance and ethical considerations. Sharing original, sensitive data might not be feasible due to privacy and regulatory constraints. However, synthetic data can be freely shared, as it encapsulates the statistical properties of the original data without the sensitive details. This makes it easier for third parties to understand how a model makes decisions, enabling more transparent and fair AI systems.

Using synthetic data for these audit trails provides an effective and privacy-compliant way to document, validate, and certify AI models, which is vital in gaining public trust and meeting regulatory standards. Watch the tutorial on Explainable AI with synthetic data! 

Case studies and guides

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