When should human values enter the LLM development pipeline?

The dominant industry pattern treats human-centered concerns — safety, fairness, steerability, user values — as alignment problems handled in a "cursory post-training stage," downstream of the real work of capability scaling. The Human-Centered Large Language Models framework rejects this sequencing. It argues that human priorities must be embedded with rigor at every stage of the pipeline: data sourcing and filtering, model training, evaluation, deployment, and long-term maintenance. The distinction it draws is between post-hoc human factors design, which accounts for user needs in only a thin slice of the process, and genuine human-centered design, where stakeholders are central to ideating, building, evaluating, and deploying the system.

Why the placement matters: a value introduced only at post-training inherits whatever the pretraining data and objective already baked in, so the patch is forever fighting the foundation. If the data sourcing stage ignored privacy or representational harm, alignment cannot fully recover it; if evaluation optimizes leaderboard metrics, human flourishing is invisible to the gradient. Embedding objectives upstream means treating the LLM as a sociotechnical system with global influence rather than an isolated tool measured by static benchmarks. The counterpoint — that pipeline-wide human-centering is slower and harder to operationalize than a final-stage fix — is real, and the framework concedes that the optimal path resists universal solutions. But the analysis is that treating alignment as a patch is precisely what subordinates human concerns to the capability race. The architecture of the pipeline encodes the priority.