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.

随着数据集规模的增大，对大型人工智能模型进行训练的数据中包含的不同社群数量可能增加，而每个社群可能持有不同的价值观，这对于刻度律模型的性能评估可能存在风险。