The worldwide AI market is expanding at an accelerating pace, and recent innovations like ChatGPT have reaffirmed its great potential. As a result, businesses are trying to embrace artificial intelligence more than ever for building efficient and innovative solutions for their customers.

However, it is important to be cautious about relying on AI as there is evidence that it can sometime be biased in its decisions, which will have negative consequences on business. This bias can occur when the data fed into the AI system does not encompass all perspectives, or the rules that the AI system follows are skewed.

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This bias in the AI-based systems has been pointed out by many firms and organizations in the past few years. For instance, in 2021, an OpenAI audit found gender and age bias in its CLIP model. Similarly, Obermeyer et al. (2019) reported evidence of racial bias in health care algorithms in the U.S. Furthermore, the New York State Department of Finance acknowledged that there are risks in algorithmic lending, including “inaccuracy in assessing creditworthiness, discriminatory outcomes, and limited transparency.”

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Such AI bias can result in discriminatory and unfair outcomes that impact customers, employees, and stakeholders. These outcomes can also negatively affect end users' experiences or create an unequal playing field, leading to dissatisfaction and potential legal issues for businesses, which can damage the overall reputation of the associated organizations.

AI governance policies

It is the need of the hour to introduce AI governance and compliance policies, which include checking the AI outcomes for potential bias.

These policies should establish clear ethical guidelines for developing and deploying AI systems, including ensuring that the data utilized to train the AI systems is tested for diversity on different demographic and socioeconomic groups, ensuring the resultant AI systems are fair and reliable. As for compliance, policies should consider a risk assessment for AI outcomes and implement technical safeguards to mitigate risks.

The engineering groups should also be required to contribute to this goal by including best practices for the development of unbiased and reliable AI using tools including the following:

AI systems with potential social impact should have a Bias Index and also a Fairness index that should exceed standard guidelines. For example, the Fairness Score should come in between 0.9 and 1.0 as an indicator of a fair AI system.

Here is a step-by-step process to ensure fairness and accuracy on a model. I am using AIF360 but a similar process can be applied with Fairlearn.

By following this high-level approach, you can evaluate the fairness of a machine learning model using AIF360 and compare the performance of the original and transformed models based on various fairness metrics.

Helpful papers

Fairness Score and Process Standardization: Framework for Fairness Certification in Artificial Intelligence Systems

A Comparative Study of Fairness-enhancing Interventions in Machine Learning

Certifying and Removing Disparate Impact