# Measurement and Fairness

**Authors:** Abigail Z. Jacobs, Hanna Wallach  
**arXiv:** [1912.05511](https://arxiv.org/abs/1912.05511)  
**Date:** 2019-12-11  
**Summary page:** https://www.summarizepaper.com/en/paper/1912.05511v1/

## Key points

- Measurement modeling from quantitative social sciences introduced as a framework for understanding fairness in computational systems
- Computational systems deal with unobservable theoretical constructs like "creditworthiness," "teacher quality," or "risk to society"
- Mismatch between theoretical understanding of constructs and their operationalization can lead to various harms
- Fairness is an unobservable theoretical construct that is essentially contested, with multiple theoretical understandings depending on context
- Recent debates about fairness definitions are disagreements about different theoretical understandings rather than contradictory operationalizations
- Introduction of measurement modeling provides a systematic approach to test assumptions about unobservable constructs in computer science community
- Methodology aims to identify, characterize, and mitigate fairness-related harms within computational systems
- Addressing mismatches between theoretical constructs and their operationalization can contribute to more equitable outcomes in algorithmic decision-making

## Summary

In their paper titled "Measurement and Fairness," authors Abigail Z. Jacobs and Hanna Wallach introduce the concept of measurement modeling from the quantitative social sciences as a framework for understanding fairness in computational systems. They highlight that computational systems often deal with unobservable theoretical constructs like "creditworthiness," "teacher quality," or "risk to society," which cannot be directly measured and must be inferred through observable properties, operationalized via a measurement model. This process can lead to a potential mismatch between the theoretical understanding of the construct being measured and its operationalization, resulting in various harms discussed in the literature on fairness in computational systems. Furthermore, the authors point out that fairness itself is an unobservable theoretical construct that is essentially contested. This means it has multiple theoretical understandings depending on the context. They argue that recent debates about fairness definitions are not necessarily about contradictory operationalizations but rather disagreements about different theoretical understandings of the construct itself. By introducing the language of measurement modeling, Jacobs and Wallach provide the computer science community with a systematic approach to explicitly test assumptions about unobservable theoretical constructs. This methodology aims to facilitate the identification, characterization, and mitigation of fairness-related harms within computational systems. Through their work, they shed light on how understanding and addressing mismatches between theoretical constructs and their operationalization can contribute to more equitable outcomes in algorithmic decision-making processes.