Machine Ethics

Opening — Why this matters now Public-sector AI has a credibility problem. Not because it cannot optimize—but because it optimizes too cleanly. In health system planning, decisions are rarely about pure efficiency. They are negotiated compromises shaped by terrain, politics, institutional memory, and hard-earned intuition. Classic optimization methods politely ignore all that. This paper tackles a question many planners quietly ask but rarely formalize: Can we let algorithms optimize without silencing human judgment—and still keep mathematical guarantees intact? ...

Opening — Why this matters now The AI industry has developed an unhealthy obsession with thinking longer. More tokens, deeper chains, bigger context windows—surely that must mean better reasoning. Except, increasingly, it doesn’t. Large Reasoning Models (LRMs) often reason past the point of usefulness, slipping into self-validation loops or overwriting correct answers with unnecessary exploration. This paper proposes a heretical idea in the age of scaling: maybe the model doesn’t need to think more—it needs to know when to stop. ...

Opening — Why this matters now Multi-agent LLM systems are having a moment. Software engineering agents argue with each other, math solvers debate proofs, and code reviewers nitpick outputs like caffeinated interns. The results are often impressive—and painfully expensive. Token budgets explode, latency compounds, and the coordination logic starts to look like an over-managed meeting that should have been an email. ...

Opening — Why this matters now Functional dependency (FD) discovery has quietly become a victim of its own success. Modern algorithms can enumerate everything—and that is precisely the problem. On realistic schemas, exhaustive FD discovery produces hundreds of thousands of valid dependencies, most of which are technically correct and practically useless. Computationally expensive. Cognitively overwhelming. Operationally irrelevant. ...

Opening — Why this matters now Everyone is chasing longer context windows. Million-token prompts. Endless chat logs. The assumption is simple: if the model can see everything, it will remember correctly. This paper shows why that assumption fails. In long-horizon, goal-driven interactions, errors rarely come from missing information. They come from retrieving the wrong information—facts that are semantically similar but contextually incompatible. Bigger windows amplify the problem. Noise scales faster than relevance. ...

Opening — Why this matters now Architectural studios are quietly changing. Not with robotic arms or parametric scripts, but with prompts. Text-to-image models now sit beside sketchbooks, offering instant massing ideas, stylistic variations, and visual shortcuts that once took hours. The promise is obvious: faster ideation, lower friction, fewer blank pages. The risk is less visible. When creativity is partially outsourced, what happens to confidence, authorship, and cognitive effort? ...

Opening — Why this matters now Genomics QA is no longer a toy problem for language models. It sits at the uncomfortable intersection of messy biological databases, evolving schemas, and questions that cannot be answered from static training data. GeneGPT proved that LLMs could survive here—barely. This paper shows why surviving is not the same as scaling. ...

Opening — Why this matters now Multi-agent AI is no longer a lab curiosity. Tool-using LLM agents already negotiate, cooperate, persuade, and sometimes sabotage—often without humans in the loop. What looks like “emergent intelligence” at first glance is, more precisely, a set of interaction effects layered on top of massive pre-trained priors. And that distinction matters. Traditional multi-agent reinforcement learning (MARL) gives us a language for agents that learn from scratch. LLM-based agents do not. They arrive already socialized. ...

Opening — Why this matters now Most AI control systems are still designed around a brittle assumption: either the agent satisfies everything, or the problem is declared unsolvable. That logic collapses quickly in the real world. Robots run out of battery. Services compete for shared resources. Environments act adversarially, not politely. In practice, goals collide. ...

Opening — Why this matters now Most LLM safety failures don’t look dramatic. They look fluent. A model doesn’t suddenly turn malicious. It drifts there — token by token — guided by coherence, momentum, and the quiet incentive to finish the sentence it already started. Jailbreak attacks exploit this inertia. They don’t delete safety alignment; they outrun it. ...