Self-Regulation on AI fails, companies race to market without ensuring proper safeguards and can’t solve AI innovation

If 2023 was the year of AI hype, 2024 is the year that trust in AI becomes mission-critical. A string of AI fails has shown that a self-regulatory, pro-innovation approach to AI can leave us less safe, without actually making AI better. Without effective guardrails, we may be speeding towards another AI winter. The EU AI Act and Biden’s AI Executive Order are raising the bar with comprehensive rules. Organizations that cut corners in the race to be first-to-market may find themselves locked out of lucrative government contracts and R&D opportunities. AI isn’t new. Neither is AI risk. AI experts have long warned of the real-world harms poorly-designed AI tools can cause. The AI Incident Database chronicles incidents dating back to 1983, where a human operator averted nuclear disaster by identifying a computer-generated false alarm. The database now contains 629 incidents. Best sellers like Weapons of Math Destruction and documentaries like Coded Bias exposed the public to a range of AI failures, making AI harms a mainstream topic well before ChatGPT entered the hype cycle. Throughout this time, concrete measures to mitigate AI risk were proposed. The EU’s GDPR introduced new rules in 2016 to address AI bias, biometrics, profiling and algorithmic decision-making. The ACM Conference on Fairness, Accountability, and Transparency has been running since 2018. Scores of AI frameworks and standards have been published. Yet too many organizations continue to move fast and break things. They develop and release AI products without assessing or mitigating risks or even confirming their products work as advertised. When they rely on ‘black box’ models, they’re often hard pressed to explain how their AI works, or if it works at all. Yet without a clear legal requirement that is likely to be enforced, they can reap profits with an MVP and deal with regulatory or consumer blowback after someone has been harmed and if they feel compelled by regulators, public pressure or a genuine desire to fix the problem. This lawful-but-awful approach turns us into human guinea pigs. And as stories of peoples harm by AI continue to make headlines, it erodes trust in AI and fuels AI fears.