Can human-centered LLM design ever achieve universal solutions?
If harm and benefit depend on who you ask and how you measure them, can we design LLM systems that satisfy all stakeholders? This explores why broad values like safety and justice resist one-size-fits-all implementation.
Even granting that human-centered objectives belong throughout the LLM pipeline, the framework makes an uncomfortable admission: those objectives tend to resist universal solutions. The optimal path depends both on who you ask and on how you operationalize contested concepts like harm and benefit. Broad themes — transparency, privacy, safety, justice — recur across stakeholders, but there is significant variation in how each ideal should be implemented. Governments and non-profits may codify the dominant perspective into law, yet high-level guidelines fail to capture the nuance of real-world use and lag behind the rapid evolution of the models themselves.
This is the open question that pipeline-wide human-centering cannot dissolve by good engineering alone. If "harm" has no operationalization that satisfies every stakeholder, then "embed human values across the pipeline" underdetermines the actual gradient — the developer still has to choose whose values, measured how. The danger the framework flags is that in the face of this irreducible contestation, stakeholders go passive, and passivity simply endorses the status quo, which means whatever the capability-driven defaults already encode. So the operationalization-dependence of harm is not a reason to abandon human-centering but a reason to make the value-choices explicit and revisable rather than implicit and frozen. The unresolved part is procedural: what legitimate process aggregates or arbitrates between divergent operationalizations without collapsing back into majority preference or developer convenience.
Inquiring lines that use this note as a source 13
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- Can design choices reduce harm without resolving the consciousness question?
- Why does fixing harm require stakeholder input rather than universal developer definitions?
- Can citizen assemblies and value pluralism replace single utility optimization?
- What prevents human-centered objectives from being applied universally across all contexts?
- Why do leaderboard metrics fail to capture human flourishing in LLM evaluation?
- What makes a process for choosing between values legitimate and fair?
- Who decides which stakeholder perspective gets embedded in the pipeline?
- Should LLMs align with social roles instead of individual preferences?
- Can regulatory standards stay responsive without abandoning legal certainty entirely?
- What unique perspective do designers bring to LLM adaptation that engineers might miss?
- How can human-centered objectives be embedded earlier in the LLM pipeline?
- What role should stakeholders play in evaluating LLM fairness?
- How can developers balance multiple conflicting fairness goals simultaneously?
Related concepts in this collection 2
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Can AI systems preserve moral value conflicts instead of averaging them?
Current AI systems wash out value tensions through majority aggregation. Can we instead model how values like honesty and friendship genuinely conflict in moral reasoning?
proposes one procedural answer to the operationalization-dependence this note leaves open
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Should AI alignment target preferences or social role norms?
Current AI alignment approaches optimize for individual or aggregate human preferences. But do preferences actually capture what matters morally, or should alignment instead target the normative standards appropriate to an AI system's specific social role?
offers a role-based criterion as an alternative to who-you-ask preference variance
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- Reflections and New Directions for Human-Centered Large Language Models
- Conversational Alignment with Artificial Intelligence in Context
- Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data
- Can Machines Think Like Humans? A Behavioral Evaluation of LLM-Agents in Dictator Games
- Large Language Models Reflect the Ideology of their Creators
- LLM Strategic Reasoning: Agentic Study through Behavioral Game Theory
- Rethinking Large Language Models in Mental Health Applications
- The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows
Original note title
human-centered objectives resist universal solutions because harm and benefit depend on who you ask