Researcher from Queen’s University Belfast develops algorithm to make artificial intelligence fairer


Dr Deepak Padmanabhan, a researcher from Queen’s University Belfast has developed an innovative new algorithm that will help make artificial intelligence (AI) fairer and less biased when processing data. Dr Padmanabhan has been leading an international project, working with experts at the Indian Institute of Technology Madras (Savitha Abraham and Sowmya Sundaram), to tackle the discrimination problem within clustering algorithms.

Companies often use AI technologies to sift through huge amounts of data in situations such as an oversubscribed job vacancy or in policing when there is a large volume of CCTV data linked to a crime. AI sorts through the data, grouping it to form a manageable number of clusters, which are groups of data with common characteristics. It is then much easier for an organisation to analyse manually and either shortlist or reject the entire group. However, while AI can save on time, the process is often biased in terms of race, gender, age, religion and country of origin.

A researcher in the School of Electronics, Electrical Engineering and Computer Science and the Institute of Electronics, Communications and Information Technology at Queen’s, Dr Padmanabhan explains, “AI techniques for data processing, known as clustering algorithms, are often criticised as being biased in terms of ‘sensitive attributes’ such as race, gender, age, religion and country of origin. It is important that AI techniques be fair while aiding shortlisting decisions, to ensure that they are not discriminatory on such attributes.”

Over the last few years ‘fair clustering’ techniques have been developed and these prevent bias in a single chosen attribute, such as gender. However, Dr Padmanabhan has now created a method that, for the first time, can achieve fairness in many attributes. Dr Padmanabhan comments, “Our fair clustering algorithm, called FairKM, can be invoked with any number of specified sensitive attributes, leading to a much fairer process. “In a way, FairKM takes a significant step towards algorithms assuming the role of ensuring fairness in shortlisting, especially in terms of human resources. With a fairer process in place, the selection committees can focus on other core job-related criteria.