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Updated: May 15, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Open Science, Health Data and Epistemic Harms: A Multidisciplinary Reflection.

Tatenda Chatikobo1, Frances Griffiths1, Nikita Hayden2

  • 1University of Warwick, United Kingdom.

Data Science Journal
|May 14, 2026
PubMed
Summary
This summary is machine-generated.

Open Science (OS) can worsen inequalities for marginalized groups. Critically analyzing OS infrastructures reveals how they perpetuate harm, particularly in health data, necessitating a shift towards inclusive, community-centered approaches for social justice.

Keywords:
Open Scienceepistemic harmshealth datapublic healthresearch datasocial justice

Related Experiment Videos

Last Updated: May 15, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

  • Interdisciplinary research
  • Social justice studies
  • Health informatics
  • Open Science ethics

Background:

  • Open Science (OS) aims to democratize knowledge and reduce epistemic inequalities.
  • However, critical analyses suggest OS may exacerbate existing structural vulnerabilities, particularly for marginalized populations.
  • The dominance of specific norms within OS infrastructures can lead to epistemic harms.

Purpose of the Study:

  • To critically examine how Open Science (OS) infrastructures perpetuate epistemic harms.
  • To focus specifically on the implications of OS within the health data landscape.
  • To explore pathways for realizing the social justice potential of OS in healthcare.

Main Methods:

  • Interdisciplinary essay synthesizing critical theory and empirical observations.
  • Analysis of OS infrastructures, focusing on knowledge norms, legal regimes, and corporate influence.
  • Examination of health data practices within the context of OS.

Main Results:

  • OS infrastructures can amplify structural vulnerabilities and perpetuate epistemic harms.
  • The dominance of Eurocentric knowledge norms, legal frameworks, and corporate capture are key mechanisms of harm.
  • Existing OS models may inadvertently reinforce inequalities rather than reduce them, especially concerning health data.

Conclusions:

  • Realizing the social justice potential of OS, particularly in healthcare, requires moving beyond techno-optimism.
  • Centering plural epistemologies, relational accountability, and community empowerment is crucial.
  • Transformative approaches are needed to ensure OS benefits all communities equitably.