Beyond the Glitch: Understanding and Mitigating AI Hallucinations

By 2030, 86% of businesses will be using AI, with its integration already permeating both public and private sectors. A frequently cited challenge that might slow this adoption are "hallucinations", where models generate fabricated or inaccurate outputs, increasing the risk of bias reinforcement and errors in application. One root cause of this phenomenon lies in AI training datasets, which, shaped by human design, inherently replicate biases. As AI becomes more embedded in our professional and personal lives, from education to healthcare, a course correction is needed. What role does AI play in shaping our access to information? Does it mitigate or amplify bias? Who holds the responsibility for addressing these concerns—dataset providers, developers, AI companies, or end users? How can we "fix" and prevent these biases? And could open-source models offer a more diverse and inclusive solution?