Forget p(Doom): Here’s How to Hold AI Accountable by Creating an Inference Economy
AI’s existential risk is no laughing matter: As japan whatsapp number data much as the tech world tries to make sense of it all, nobody definitively knows whether the dystopian “control the world” hype is legit or just sci-fi.
Before we even cross that bridge (real or not), however, problem. We’re already seeing the emergence of semi-autonomous AI agents responding to their environment and making decisions on their own. Think trading robots that monitor stock market conditions and change how they buy or sell accordingly, or even a work memo or school paper churned out by ChatGPT.
No matter the application, AI’s usual practical issues inevitably pop up. Hallucinated citations, lazy decisions, sudden misdirections, and overall unpredictability continue to hold back trust in these systems – and, diving deeper, trust in the machine learning (ML) algorithms powering critical inferences. ML inferences are often inscrutable to outsiders, and the consequences are seismic. Just look at Minnesota’s lawsuit against UnitedHealth Group for relying on a predictive algorithm that didn’t take COVID into account, or the creditworthiness assessment industry’s massive blunder of bad scores last year.