AI bias can be a significant problem that affects the accuracy and fairness of artificial intelligence systems, with examples including AI recruitment tools, health insurance programs, and facial recognition software. Bias in AI can take many forms, including algorithmic bias, sample bias, prejudice bias, measurement bias, and exclusion bias, which are often interconnected and can lead to inaccurate results. However, by exposing machine learning models to diverse data and using self-supervised learning, it is possible to reduce AI bias. Companies like Speechmatics are working to tackle this issue through their Autonomous Speech Recognition system, which has achieved significant improvements in accuracy, including a 50% improvement in recognition of African American voices and a smaller age gap between younger and older voices. The mission of "Understand Every Voice" aims to address diversity, equity, and inclusion in AI, recognizing that there is no silver bullet to fix all AI bias but that individual efforts can make a difference.