Machine learning is often portrayed as objective and neutral, but real-world applications tell a different story. From facial recognition systems misidentifying people of color to hiring algorithms favoring male applicants, bias in AI has emerged as a serious concern.

Massive AI models like GPT-4 and PaLM 2 dominate headlines—but small models are quietly powering healthcare, agriculture, and on-device apps with comparable performance at a fraction of the cost. From TinyML to DistilBERT, these efficient models are driving a shift toward ethical, inclusive, and sustainable AI.