A Unifying Bias-aware Multidisciplinary Framework for Investigating Socio-Technical Issues

TL;DR

This paper presents a multidisciplinary, bias-aware framework combining social science and machine learning to investigate socio-technical issues, demonstrated through a study on online harms faced by minoritized ethnic communities in UK social housing.

Contribution

It introduces a novel, transparent methodology integrating social science and ML to identify and mitigate bias in socio-technical investigations.

Findings

Identified key vulnerabilities like discrimination and digital poverty among minoritized groups.

Found Black African communities face higher risks of vulnerabilities in digital social housing.

Demonstrated the framework's effectiveness in analyzing complex socio-technical issues.

Abstract

This paper aims to bring together the disciplines of social science (SS) and computer science (CS) in the design and implementation of a novel multidisciplinary framework for systematic, transparent, ethically-informed, and bias-aware investigation of socio-technical issues. For this, various analysis approaches from social science and machine learning (ML) were applied in a structured sequence to arrive at an original methodology of identifying and quantifying objects of inquiry. A core feature of this framework is that it highlights where bias occurs and suggests possible steps to mitigate it. This is to improve the robustness, reliability, and explainability of the framework and its results. Such an approach also ensures that the investigation of socio-technical issues is transparent about its own limitations and potential sources of bias. To test our framework, we utilised it in the multidisciplinary investigation of the online harms encountered by minoritised ethnic (ME) communities when accessing and using digitalised social housing services in the UK. We draw our findings from 100 interviews with ME individuals in four cities across the UK to understand ME vulnerabilities when accessing and using digitalised social housing services. In our framework, a sub-sample of interviews focusing on ME individuals residing in social housing units were inductively coded. This resulted in the identification of the topics of discrimination, digital poverty, lack of digital literacy, and lack of English proficiency as key vulnerabilities of ME communities. Further ML techniques such as Topic Modelling and Sentiment Analysis were used within our framework where we found that Black African communities are more likely to experience these vulnerabilities in the access, use and outcome of digitalised social housing services.