Scaling Laws Do Not Scale
Fernando Diaz Michael Madaio
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.