Recent work has proposed a power law relationship, referred to as ``scaling laws,'' between the performance of artificial intelligence (AI) models and aspects of those models' design (e.g., dataset size). In other words, as the size of a dataset (or model parameters, etc) increases, the performance of a given model trained on that dataset will correspondingly increase. However, while compelling in the aggregate, this scaling law relationship overlooks the ways that metrics used to measure performance may be precarious and contested, or may not correspond with how different groups of people may perceive the quality of models' output. In this paper, we argue that as the size of datasets used to train large AI models grows, the number of distinct communities (including demographic groups) whose data is included in a given dataset is likely to grow, each of whom may have different values. As a result, there is an increased risk that communities represented in a dataset may have values or preferences not captured by (or in the worst case, at odds with) the metrics used to evaluate model performance for scaling laws. We end the paper with implications for AI scaling laws -- that models may not, in fact, continue to improve as the datasets get larger -- at least not for all people or communities impacted by those models.

翻译：近期研究提出了人工智能模型性能与模型设计要素（如数据集规模）之间存在幂律关系，即所谓的"缩放定律"。换言之，随着数据集（或模型参数等）规模的增大，基于该数据集训练的给定模型性能将相应提升。然而，尽管从总体上看这一关系颇具说服力，但缩放定律忽视了用于衡量性能的指标可能存在的脆弱性和争议性，也可能无法反映不同群体对模型输出质量的感知差异。本文认为，随着训练大规模AI模型的数据集规模不断增大，数据集中包含的不同社群（包括人口统计群体）数量可能随之增加，而每个社群可能持有不同的价值取向。其结果是，数据集中所代表的社群很可能存在未被缩放定律中用于评估模型性能的指标所涵盖（或最坏情况下与之相悖）的价值偏好。本文最后探讨了AI缩放定律的启示——事实上，随着数据集规模扩大，模型性能可能不会持续提升——至少对受模型影响的所有人群或社群而言并非如此。