The State of Algorithmic Fairness in Mobile Human-Computer Interaction

TL;DR

This paper reviews the current state of algorithmic fairness in mobile HCI research, revealing limited adherence to fairness reporting standards and a focus on Western, educated populations, highlighting the need for more inclusive and fair mobile technologies.

Contribution

It provides a comprehensive analysis of MobileHCI papers from 2017-2022, identifying gaps in fairness reporting and demographic diversity in research.

Findings

Only 5% of papers adhere to modern fairness reporting.

Most studies focus on Western, educated, employed populations.

The analysis highlights the need for more inclusive fairness considerations.

Abstract

This paper explores the intersection of Artificial Intelligence and Machine Learning (AI/ML) fairness and mobile human-computer interaction (MobileHCI). Through a comprehensive analysis of MobileHCI proceedings published between 2017 and 2022, we first aim to understand the current state of algorithmic fairness in the community. By manually analyzing 90 papers, we found that only a small portion (5%) thereof adheres to modern fairness reporting, such as analyses conditioned on demographic breakdowns. At the same time, the overwhelming majority draws its findings from highly-educated, employed, and Western populations. We situate these findings within recent efforts to capture the current state of algorithmic fairness in mobile and wearable computing, and envision that our results will serve as an open invitation to the design and development of fairer ubiquitous technologies.