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Towards examining Human-AI collaboration across the AI pipeline

Thakkar, D. (2024). Towards examining Human-AI collaboration across the AI pipeline. (Unpublished Doctoral thesis, City, University of London)


The integration of Artificial Intelligence (AI) into decision-making workflows provides significant opportunities for productivity gains but also raises complex questions about its effectiveness and impact on society. Drawing upon rich qualitative studies from extensive fieldwork conducted in the Global South over five years, this thesis engages with the overarching question – how do humans and AI collaborate across the AI pipeline, from design to deployment?. This thesis provides an analysis of the Human-AI collaboration pipeline by examining it across stages and multiple stakeholders. I begin by addressing – Who are the creators behind computing?, focusing specifically on women’s representation in India’s computing industry. Next, I engage with growing challenges around data quality by exploring – How does data powering AI come to be?. This exploration is grounded in the study of datafication in India’s public health sector, highlighting the complexities and challenges in ensuring high-quality data for AI systems. Further, I examine: How does AI change the nature of work? by studying a first-of-its-kind large-scale AI deployment in India. Lastly, I uncover: How do people perceive and experience AI? by examining AI perceptions and experiences of vocational technicians in India, a historically underserved community vulnerable to job loss through automation. Through a reflective analysis, I develop an understanding of Human-AI collaboration by examining human and non-human actors across the AI development pipeline. This perspective recognizes the interconnectedness of human and non-human elements, including social, cultural, and organizational factors, in shaping the development and impact of AI technologies. This thesis emphasises the study of AI’s role in the Global South, particularly in high-stakes domains such as public health and future of work. It highlights the potential impacts of AI on historically underserved communities, underscoring the need for inclusive and context-sensitive approaches in AI development and deployment. By examining Human-AI collaboration across the entire pipeline and situating it within diverse contexts, this thesis contributes to a wider examination of AI’s role in society and a path for future research in this area. In conclusion, this thesis contributes to the field of Human- Computer Interaction by examining Human-AI collaboration as a part of a broader pipeline of AI design to deployment.

Publication Type: Thesis (Doctoral)
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Departments: School of Science & Technology > Computer Science
School of Science & Technology > School of Science & Technology Doctoral Theses
Doctoral Theses
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