# A Nutritional Label for Rankings

arXiv: https://arxiv.org/abs/1804.07890 · HTML: https://mlsanity.com/paper/1804.07890/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2018.

## Built on by (most cited first, 10 of 10)

- [The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligence](https://mlsanity.com/paper/2201.03954/) (2022)
- [SoK: Machine Learning Governance](https://mlsanity.com/paper/2109.10870/) (2021)
- [Towards Formalizing Spuriousness of Biased Datasets Using Partial Information Decomposition](https://mlsanity.com/paper/2407.00482/) (2024)
- [Multisource AI Scorecard Table for System Evaluation](https://mlsanity.com/paper/2102.03985/) (2021)
- [ShaRP: Explaining Rankings and Preferences with Shapley Values](https://mlsanity.com/paper/2401.16744/) (2024)
- [Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation](https://mlsanity.com/paper/2503.13463/) (2025)
- [A Framework for Understanding AI-Induced Field Change: How AI Technologies are Legitimized and Institutionalized](https://mlsanity.com/paper/2108.07804/) (2021)
- [Network Report: A Structured Description for Network Datasets](https://mlsanity.com/paper/2206.03635/) (2022)
- [A Tutorial On Intersectionality in Fair Rankings](https://mlsanity.com/paper/2502.05333/) (2025)
- [Fragments to Facts: Partial-Information Fragment Inference from LLMs](https://mlsanity.com/paper/2505.13819/) (2025)

## Abstract (arXiv)

Algorithmic decisions often result in scoring and ranking individuals to determine credit worthiness, qualifications for college admissions and employment, and compatibility as dating partners. While automatic and seemingly objective, ranking algorithms can discriminate against individuals and protected groups, and exhibit low diversity. Furthermore, ranked results are often unstable --- small changes in the input data or in the ranking methodology may lead to drastic changes in the output, making the result uninformative and easy to manipulate. Similar concerns apply in cases where items other than individuals are ranked, including colleges, academic departments, or products. In this demonstration we present Ranking Facts, a Web-based application that generates a "nutritional label" for rankings. Ranking Facts is made up of a collection of visual widgets that implement our latest research results on fairness, stability, and transparency for rankings, and that communicate details of the ranking methodology, or of the output, to the end user. We will showcase Ranking Facts on real datasets from different domains, including college rankings, criminal risk assessment, and financial services.