The vision of an all-knowing, omni-present intelligent being that forms the backbone of our everyday lives has been portrayed in movies that captivate the imagination of many. Today, that vision is not too far from reality, and we are seeing this at work through artificial intelligence (AI) – from AI-powered voice assistants like Alexa, to helping solve traffic issues, enabling the sequencing of DNA, tackling business problems and transforming industries such as tech, healthcare to logistics and fintech.

 

Current AI technologies are estimated to have the potential to automate about 50 percent of work activities in ASEAN’s four biggest economies – Indonesia, Malaysia, the Philippines and Thailand, according to McKinsey

 

Even as AI increasingly finds its way into our everyday lives, the transformative power of AI is underpinned by a deeper issue that threatens the very fabric of our society – the presence of bias within it.

 

Uncovering the roots of bias

 

Much of AI’s capabilities as an intelligent, cognitive system rely on it being programmed and trained. At its core, AI operates on algorithms and data sets, the driving force of the digital economy in the 21st century. However, AI also unfortunately inherits and reflects the existing bias of its creators through the data it is given.

 

For example, when used in recruitment, a biased AI could be trained to shortlist potential candidates based on selected profiles of high-performing employees, which may not be representative of the company’s workforce nor consider diversity and inclusion as a factor for hiring, and potentially skew the hiring demographic.

 

The capabilities of AI are only as objective as the quality of the data inputs, as well as the assumptions around this data. When this data is not carefully selected, AI may not only validate the biases we hold, but further perpetuate them.

 

Leaving this unaddressed could pose issues for society, given that AI has already found its way into sectors such as telecommunications, medical, legal and finance. For example, a biased AI system might deny a bank loan simply because the borrower is located in a poorer neighborhood.