Measuring AI Success: Beyond the Hype | Golden Age

Measuring AI success is a complex and multifaceted challenge, with various stakeholders vying for influence. According to a report by McKinsey, AI adoption has increased by 55% since 2017, with 61% of companies using AI in at least one business function. However, a survey by Gartner found that only 53% of AI projects are deemed successful, highlighting the need for more effective evaluation metrics. The AI Now Institute has proposed a framework for evaluating AI systems, which includes metrics such as fairness, transparency, and accountability. Meanwhile, researchers like Dr. Kate Crawford and Dr. Solon Barocas are working to develop more nuanced and contextualized approaches to AI evaluation. As AI continues to evolve and permeate various aspects of our lives, the question remains: what does success really mean in the context of AI, and how can we measure it in a way that is both meaningful and actionable?