How to avoid the ethical pitfalls of artificial intelligence and machine learning

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.