Processes of Social Inclusion/Exclusion in Hybrid Teams

Researcher

Alexandra Florea

Collaboration

Clarissa Sabrina Arlinghaus

Research Theme

R2 Prosilience & Robustness

Tags

Data Literacy

Project Summary: This research project critically examines the ethics and sustainability of data processing in general, in research, and in the context of deploying autonomous technical agents, with a focus on the implications for social inclusion and exclusion. It is composed of three interconnected studies:

Scoping Review and Bibliometric Analysis of Data Practices and Ethical Considerations in Computational Social Science: a study that critically maps the methodological, data, and ethical practices within CSS literature, especially concerning human subject research. It aims to highlight current research practices and identify gaps in ethical considerations.

Critical Examination of the Iris Dataset: this sub-project investigates the historical and current use of the Iris dataset, a foundational tool in data science. Starting with a bibliometric systematic review, the study explores the dataset’s methodological, didactical, and ideological implications. It emphasizes the need for a critical reevaluation of the dataset’s use in data science education and practice, addressing its eugenicist roots and promoting a decolonized approach to data science.

Ethics and Sustainability of Data Processing in Autonomous Technologies: after elaborating onto ethics and foundations of data processing, I will turn my attention to the implications of data processing in autonomous technologies, focusing on the sustainability of data collection and processing practices. The goal here is to underscore the necessity for democratization of data and ethics literacy, as digitalization and data practices become more prevalent, and formulate guidelines/principles for citizen science empowerment on matters of data.

Concrete steps across the three studies include:

Employing bibliometric and content analyses, thematic analysis, historical tracking, and concept mapping, (maybe RAG if I could secure a cooperation with a LLM expert in the project) Utilizing interdisciplinary methodologies, drawing from social sciences, cultural studies, and technology ethics. Engaging with stakeholders, including technology developers, policymakers, and affected communities, to gather diverse perspectives. Provided I secure access to a “hybrid context” as a research field, some of the aspects of the proposed research may change.

The overarching aim is to provide a comprehensive understanding of the ethical and sustainability challenges associated with data-intensive technologies in society. The project seeks to contribute to the development of more responsible, inclusive, and sustainable data practices in the field of autonomous technical agents, data science education, and computational social science.