Ensuring Ethics in AI and Mitigating Bias

As AI solutions proliferate, ensuring they are not biased with respect to gender, religion, financial status, etc, has become of paramount importance. The good news is that there is a lot of work being done on that front.

- Dr Magesh Kasthuri and Dr Anand Nayyar

Today, there is a paradigm shift from traditional AI systems that use statistical and mathematical probabilistic algorithms to those that use machine learning (ML) and deep learning (DL) models. As per Gartner, there are 34 different branches of AI system design in existence today. AIOps in cloud operations, DataOps in data engineering, predictive analytics in data science, and MLOps in industrial applications are some of the examples of new age AI applications.

Ray Kurzweil coined the term 'singularity' in AI, which means bringing AI closer to human intelligence (or natural intelligence). To achieve the highest level of accuracy in ML training, modelling and functioning, it is of utmost important to ensure fairness and correct any bias in AIML implementation. Bias cannot occur on its own but is the result of human inputs during the various stages of developing the AIML-based solution. 

When collecting data for training the model in ML, one must ensure the data is distributed fairly and that there is no bias. In the same way, when we label and group the data, train the model to simulate human-like thinking, deploy the model and interpret the results, we must do away with any pre-judgement or biased interests.

For example, bias with respect to race, income, sexual orientation, gender, religion, must be avoided when preparing the training data, training the system and interpreting the results from the ML execution.

There are many popular tools like IBM’s AI Fairness 360, Microsoft’s Fairlearn and Google’s What-if that are very useful to identify any bias in the training model and data collection.

Addressing bias in AI systems