# Data as a Lever: A Neighbouring Datasets Perspective on Predictive Multiplicity

arXiv: https://arxiv.org/abs/2510.21303 · HTML: https://mlsanity.com/paper/2510.21303/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 48 references that are in the map.
- First posted on arXiv 24 Oct 2025.

## Builds on (most cited first, 28 of 28)

- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021)
- [Understanding Dataset Difficulty with V-Usable Information](https://mlsanity.com/paper/2110.08420/) (2021)
- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [Predictive Multiplicity in Classification](https://mlsanity.com/paper/1909.06677/) (2019)
- [On the Accuracy of Influence Functions for Measuring Group Effects](https://mlsanity.com/paper/1905.13289/) (2019)
- [Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent](https://mlsanity.com/paper/2006.08157/) (2020)
- [Algorithmic Monoculture and Social Welfare](https://mlsanity.com/paper/2101.05853/) (2021)
- [Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision](https://mlsanity.com/paper/2109.08203/) (2021)
- [Exploring the Whole Rashomon Set of Sparse Decision Trees](https://mlsanity.com/paper/2209.08040/) (2022)
- [Amazing Things Come From Having Many Good Models](https://mlsanity.com/paper/2407.04846/) (2024)
- [Rashomon Capacity: A Metric for Predictive Multiplicity in Classification](https://mlsanity.com/paper/2206.01295/) (2022)
- [Predictive Multiplicity in Probabilistic Classification](https://mlsanity.com/paper/2206.01131/) (2022)
- [A Path to Simpler Models Starts With Noise](https://mlsanity.com/paper/2310.19726/) (2023)
- [Exploring and Interacting with the Set of Good Sparse Generalized Additive Models](https://mlsanity.com/paper/2303.16047/) (2023)
- [Dropout-Based Rashomon Set Exploration for Efficient Predictive Multiplicity Estimation](https://mlsanity.com/paper/2402.00728/) (2024)
- [The Dataset Multiplicity Problem: How Unreliable Data Impacts Predictions](https://mlsanity.com/paper/2304.10655/) (2023)
- [Systemizing Multiplicity: The Curious Case of Arbitrariness in Machine Learning](https://mlsanity.com/paper/2501.14959/) (2025)
- [Predictive Churn with the Set of Good Models](https://mlsanity.com/paper/2402.07745/) (2024)
- [Algorithmic Pluralism: A Structural Approach To Equal Opportunity](https://mlsanity.com/paper/2305.08157/) (2023)
- [Algorithmic Arbitrariness in Content Moderation](https://mlsanity.com/paper/2402.16979/) (2024)
- [Forward Stability and Model Path Selection](https://mlsanity.com/paper/2103.03462/) (2021)
- [Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints](https://mlsanity.com/paper/2306.13738/) (2023)
- [Cross-model Fairness: Empirical Study of Fairness and Ethics Under Model Multiplicity](https://mlsanity.com/paper/2203.07139/) (2022)
- [Non-Determinism and the Lawlessness of Machine Learning Code](https://mlsanity.com/paper/2206.11834/) (2022)
- [An Empirical Investigation into Benchmarking Model Multiplicity for Trustworthy Machine Learning: A Case Study on Image Classification](https://mlsanity.com/paper/2311.14859/) (2023)
- [Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized](https://mlsanity.com/paper/2404.08592/) (2024)
- [Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric Perspective](https://mlsanity.com/paper/2412.09712/) (2024)

## Abstract (arXiv)

Multiplicity, the existence of equally good yet competing models, has received growing attention in recent years. While prior work has emphasized modelling choices, the critical role of data in shaping multiplicity has been largely overlooked. In this work, we first introduce a neighbouring datasets framework, arguing that much of data processing can be reframed as choosing between neighbouring datasets. Under this framework, we find a counterintuitive theoretical relationship: neighbouring datasets with greater inter-class distribution overlap exhibit lower multiplicity. Building on this insight, we apply our framework to two domains: active learning and data imputation. For each, we establish natural extensions of the neighbouring datasets perspective, conduct the first systematic study of multiplicity in existing algorithms, and finally, propose novel multiplicity-aware methods, namely, multiplicity-aware data acquisition strategies for active learning and multiplicity-aware data imputation.