AI Ethics and Bias - AI systems are currently making decisions that impact lives, potentially closing doors to opportunities like grocery stores, healthcare, and home loans [2] - Facial recognition technology can fail to identify certain groups up to 35% more often than others, highlighting bias in AI [3] - The AI field lacks diversity, with only 25% of AI engineers identifying as a minority and 22% as women, and these numbers are decreasing [4] - AI trained on inequitable data reproduces and scales existing biases, impacting hiring, medical diagnostics, and criminal justice [6][7] - Ethical AI should be fit, fair, inclusive, and transparent, actively working to eliminate discrimination and ensuring accountability [8][9] Social Mobility and Economic Impact - AI has the potential to widen the wealth gap by $43 billion if not managed properly [11] - AI-driven automation is estimated to displace 85 million jobs but create 97 million new ones, resulting in a net increase of 12 million jobs globally [11] - Communities risk missing out on the future of work, wealth, and leadership if they are not involved in AI innovation [12] - Education, funding for tech founders, and policies that prioritize people over profit are needed to ensure equitable participation in the AI economy [12] Agentic AI and Human Oversight - Agentic AI systems, which operate with autonomy, require human-defined goals to ensure they align with human values [15] - Human oversight is crucial throughout the AI lifecycle to train, monitor, and correct AI decisions, especially when they impact real lives [16][17] - AI should center on human values and maintain human involvement to ensure context, compassion, and ethical judgment are considered [16][17]