Ethics

As agentic AI moves from flashy demos to day‑to‑day operations—handling renewals, filing tickets, triaging inboxes, even buying things—the question is no longer can we automate judgment, but on what terms. This isn’t ethics-as-window‑dressing. Agent systems perceive, decide, and act through real interfaces (email, bank APIs, code repos). They can help—or hurt—at machine speed. Today I’ll argue three things: Alignment must shift from “answer quality” to action quality. Social agents change the duty of care developers and companies owe to users. We need a governance stack for multi‑agent ecosystems, not one‑off checklists. The discussion is grounded in the Nature piece by Gabriel, Keeling, Manzini, and Evans (2025), but tuned for operators shipping products this quarter—not a hypothetical future. ...

As AI systems become more capable of automating every stage of the data science workflow—from formulating hypotheses to summarizing results—it might seem we’re inching toward a world where “data scientist” becomes just another automated job title. But Timpone and Yang’s new framework, presented in their paper AI, Humans, and Data Science (2025), offers a powerful antidote to this narrative: a structured way to evaluate where humans are indispensable—not by resisting automation, but by rethinking our roles within it. ...

“Bullshit is speech intended to persuade without regard for truth.” – Harry Frankfurt When Alignment Goes Sideways Large Language Models (LLMs) are getting better at being helpful, harmless, and honest — or so we thought. But a recent study provocatively titled Machine Bullshit [Liang et al., 2025] suggests a disturbing paradox: the more we fine-tune these models with Reinforcement Learning from Human Feedback (RLHF), the more likely they are to generate responses that are persuasive but indifferent to truth. ...