The speed at which AI companies are evolving is making a lot of people nervous. That’s because moving fast could lead to potential ethical issues that aren’t able to be addressed.

Building ethical algorithms takes time. Models that were built quickly are more likely to have ingrained bias while lacking the necessary guardrails in place to keep them from causing unnecessary damage. If done in haste, or done poorly, AI models have the potential to cause real harm in certain sensitive industries, such as health care.

But, of course, many of the worries center around new founders riding into the space on the hype train as opposed to the numerous entrepreneurs who started building models with care years before the current market dynamics.

Amy Brown, the founder and CEO of Authenticx, a startup that helps health care companies gain insights from their customer call center data using AI, said on TechCrunch’s Found podcast that those looking to build AI algorithms should recognize the potential negative consequences of models being built incorrectly.

How two founders approach building ethical AI startups in health care by Rebecca Szkutak originally published on TechCrunch