A Practical Guide to Building Ethical AI

Companies are leveraging data and artificial intelligence to create scalable solutions — but they’re also scaling their reputational, regulatory, and legal risks. 

• Los Angeles is suing IBM for allegedly misappropriating data it collected with its ubiquitous weather app.
• Optum is being investigated by regulators for creating an algorithm that allegedly recommended that doctors and nurses pay more attention to white patients than to sicker black patients.
• Goldman Sachs is being investigated by regulators for using an AI algorithm that allegedly discriminated against women by granting larger credit limits to men than women on their Apple cards.
• Facebook infamously granted Cambridge Analytica, a political firm, access to the personal data of more than 50 million users.

Just a few years ago discussions of “data ethics” and “AI ethics” were reserved for nonprofit organizations and academics. Today the biggest tech companies in the world are putting together fast-growing teams to tackle the ethical problems that arise from the widespread collection, analysis, and use of massive troves of data, particularly when that data is used to train machine learning models, aka AI.

These companies realized one simple truth: failing to operationalize data and AI ethics is a threat to the bottom line. Missing the mark can expose companies to reputational, regulatory, and legal risks, but that’s not the half of it. Failing to operationalize data and AI ethics leads to wasted resources, inefficiencies in product development and deployment, and even an inability to use data to train AI models at all.