Uncovering Bias in Personal Informatics

by   Sofia Yfantidou, et al.

Personal informatics (PI) systems, powered by smartphones and wearables, enable people to lead healthier lifestyles by providing meaningful and actionable insights that break down barriers between users and their health information. Today, such systems are used by billions of users for monitoring not only physical activity and sleep but also vital signs and women's and heart health, among others. particularly sensitive personal data, and their proximity to domains known to be susceptible to bias, such as healthcare, bias in PI has not been investigated systematically. Despite their widespread usage, the processing of sensitive PI data may suffer from biases, which may entail practical and ethical implications. In this work, we present the first comprehensive empirical and analytical study of bias in PI systems, including biases in raw data and in the entire machine learning life cycle. We use the most detailed framework to date for exploring the different sources of bias and find that biases exist both in the data generation and the model learning and implementation streams. According to our results, the most affected minority groups are users with health issues, such as diabetes, joint issues, and hypertension, and female users, whose data biases are propagated or even amplified by learning models, while intersectional biases can also be observed.


page 9

page 11

page 16

page 17

page 31


Proceedings of the KG-BIAS Workshop 2020 at AKBC 2020

The KG-BIAS 2020 workshop touches on biases and how they surface in know...

Causally Linking Health Application Data and Personal Information Management Tools

The proliferation of consumer health devices such as smart watches, slee...

A Survey on Bias and Fairness in Machine Learning

With the widespread use of AI systems and applications in our everyday l...

Bias in Conversational Search: The Double-Edged Sword of the Personalized Knowledge Graph

Conversational AI systems are being used in personal devices, providing ...

FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning

The growing capability and accessibility of machine learning has led to ...

Bias in Internet Measurement Platforms

Network operators and researchers frequently use Internet measurement pl...

NBIAS: A Natural Language Processing Framework for Bias Identification in Text

Bias in textual data can lead to skewed interpretations and outcomes whe...

Please sign up or login with your details

Forgot password? Click here to reset