# Participatory Personalization in Classification

arXiv: https://arxiv.org/abs/2302.03874 · HTML: https://mlsanity.com/paper/2302.03874/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 68 references that are in the map.
- First posted on arXiv 8 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [From Predictive to Prescriptive Analytics](https://mlsanity.com/paper/1402.5481/) (2014)
- [From Parity to Preference-based Notions of Fairness in Classification](https://mlsanity.com/paper/1707.00010/) (2017)
- [Model Distillation for Revenue Optimization: Interpretable Personalized Pricing](https://mlsanity.com/paper/2007.01903/) (2020)
- [Online certification of preference-based fairness for personalized recommender systems](https://mlsanity.com/paper/2104.14527/) (2021)
- [An Algorithmic Framework for Bias Bounties](https://mlsanity.com/paper/2201.10408/) (2022)
- [Operationalizing the Legal Principle of Data Minimization for Personalization](https://mlsanity.com/paper/2005.13718/) (2020)
- [Preference-Informed Fairness](https://mlsanity.com/paper/1904.01793/) (2019)
- [Fair Policy Targeting](https://mlsanity.com/paper/2005.12395/) (2020)
- [Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization](https://mlsanity.com/paper/2107.08096/) (2021)
- [Visualizing Privacy-Utility Trade-Offs in Differentially Private Data Releases](https://mlsanity.com/paper/2201.05964/) (2022)
- [Privacy, ethics, and data access: A case study of the Fragile Families Challenge](https://mlsanity.com/paper/1809.00103/) (2018)
- [Personalized Privacy Auditing and Optimization at Test Time](https://mlsanity.com/paper/2302.00077/) (2023)
- [Data-Driven Online Decision Making with Costly Information Acquisition](https://mlsanity.com/paper/1602.03600/) (2016)

## Built on by (most cited first, 5 of 5)

- [Sufficient Context: A New Lens on Retrieval Augmented Generation Systems](https://mlsanity.com/paper/2411.06037/) (2024)
- [Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse](https://mlsanity.com/paper/2410.22598/) (2024)
- [The Data Minimization Principle in Machine Learning](https://mlsanity.com/paper/2405.19471/) (2024)
- [Knockout: A simple way to handle missing inputs](https://mlsanity.com/paper/2405.20448/) (2024)
- [Regretful Decisions under Label Noise](https://mlsanity.com/paper/2504.09330/) (2025)

## Abstract (arXiv)

Machine learning models are often personalized with information that is protected, sensitive, self-reported, or costly to acquire. These models use information about people but do not facilitate nor inform their consent. Individuals cannot opt out of reporting personal information to a model, nor tell if they benefit from personalization in the first place. We introduce a family of classification models, called participatory systems, that let individuals opt into personalization at prediction time. We present a model-agnostic algorithm to learn participatory systems for personalization with categorical group attributes. We conduct a comprehensive empirical study of participatory systems in clinical prediction tasks, benchmarking them with common approaches for personalization and imputation. Our results demonstrate that participatory systems can facilitate and inform consent while improving performance and data use across all groups who report personal data.