Across industries, businesses are expanding their use of artificial intelligence (AI) systems. AI isn't just for the tech giants like Meta and Google anymore; logistics firms leverage AI to streamline operations, advertisers use AI to target specific markets and even your online bank uses AI to power its automated customer service experience. For these companies, dealing with ethical risks and operational challenges related to AI is inevitable – but how should they prepare to face them? Poorly executed AI products can violate individual privacy and in the extreme, even weaken our social and political systems. In the U.S., an algorithm used to predict likelihood of future crime was revealed to be biased against Black Americans, reinforcing racial discriminatory practices in the criminal justice system.

The modern business world is littered with examples where organisations hastily rolled out artificial intelligence (AI) and machine learning (ML) solutions without due consideration of ethical issues, which has led to very costly and painful learning lessons. Internationally, for example, IBM is getting sued after allegedly misappropriating data from an app while Goldman Sachs is under investigation for using an allegedly discriminatory AI algorithm. A closer homegrown example was the Robodebt debacle, in which the federal government deployed ill-thought-through algorithmic automation to send out letters to recipients demanding repayment of social security payments dating back to 2010. The government settled a class action against it late last year at an eye-watering cost of $1.2 billion after the automated mailouts system targeted many legitimate social security recipients.

Artificial intelligence has the potential to better patient care while creating cost-efficiencies that would be impossible without it. But it could also worsen racial disparities, have profit outweighing patient care, or simply lead to mistakes that a human wouldn't make. In other news at the intersection of health care and technology: video games, virtual reality for nursing home patients and ways to identify bacteria's genetic makeup. Artificial intelligence can make diagnoses from digitized images such as mammograms and diabetic retinal scans. More sophisticated interventions might also be possible someday: algorithms that guide robots through surgery, for example, or even help restore motor control in paralyzed patients.