- Focus: Emphasizing discernment in ML use, addressing risks like lack of ethical oversight, privacy violations, and untested models.
- Content: Explanations of ML’s nature (statistical models, not sentient), risks of developers testing new models without transparency, and societal impacts (e.g., guinea-pig testing without consent, ignoring moral values). Includes TUB’s comments with wisdom on speed of societal advances, even tech advances.
- Purpose: Equip readers to navigate ML’s dangers, ensuring it serves truth rather than deception. The growing ML safety movement. Responses to published views from UN students