🤔 If you're a fledgling AI startup, you might be wondering when you should implement AI governance in your MLOps pipeline. You're not alone! Since the issuance of President Biden's Executive Order on AI, many companies that use AI in their products and services have asked us at BreezeML about when they should start to worry about AI compliance. Should they start now? Or should they wait until the U.S. Congress passes a formal piece of AI-specific legislation? Our answer is NOW, and here's why: 💡 There's a popular perception that efforts to regulate AI are a recent development that has yet to tangibly impact companies utilizing the technology. But in reality, legislation formulated more than a decade ago during the big data boom already influences how AI can be utilized today in various industries such as financial services, insurance, healthcare and medical devices, and digital advertising and marketing. 💡 In other words, the EO should not be viewed as a seminal moment for AI regulation in the U.S. Rather, it should be understood as a logical extension of preceding regulations that have strived to prevent corporate actors from violating users’ rights to data privacy and engaging in automated, algorithm-based decision-making that carries out harmful or discriminatory practices against consumers. 💡 Existing regulations that have implications for the use of AI today ask the question of “how did a model come to be?” This means that companies need to provide concrete evidence that demonstrates the absence of risk throughout the entire developmental lifecycle of an AI model. To do this, companies need a governance framework that can meticulously track all data contributing to a model and all the operations performed on that data. 💡 BreezeML can act as this governance framework. As an AI governance platform that serves as a nexus between compliance/legal teams and data scientists, BreezeML employs a “governance by construction” methodology, enabling compliance teams to effortlessly specify and continually monitor governance policies over every AI workflow in their organization without relying on manual and tedious coordination with data science teams. You can find more insights in the full Medium article here!