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

2025-05-06

AI Summary

This study addresses disciplinary fragmentation and concealed biases in socio-technical research by proposing the first embedded bias-aware interdisciplinary framework. It integrates sociological qualitative methods with computer science–based quantitative techniques to systematically examine online harms experienced by ethnic minorities accessing digital social housing services in the UK. Methodologically, it combines grounded theory coding, LDA topic modeling, sentiment analysis, qualitative comparative analysis (QCA), and a mixed-methods design to ensure bias source visualization, methodological transparency, and result interpretability. The study identifies four structural vulnerability dimensions—discrimination, digital poverty, low digital literacy, and limited English proficiency—and provides the first empirical evidence that Black African communities disproportionately experience compounded vulnerabilities. The framework enhances the robustness, ethical rigor, and cross-disciplinary reproducibility of socio-technical research.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Responsible Web: Social and technical mechanisms of refusal for web technologies and applications
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

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.

Problem

• Integrating social and computer science to study socio-technical issues
• Identifying and mitigating bias in multidisciplinary research frameworks
• Analyzing online harms for ethnic minorities in digital housing services

Innovation

• Bias-aware multidisciplinary framework combining SS and CS
• Structured sequence of SS and ML analysis approaches
• Topic Modelling and Sentiment Analysis for vulnerability identification