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Automated decision support (ADS) can streamline research data access, improving fairness and efficiency. This computational governance approach enhances data subject rights and public trust in health data research.

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Area of Science:

  • Bioethics
  • Computational Governance
  • Health Informatics

Background:

  • Ethical considerations for artificial intelligence in clinical and research decision-making are well-studied.
  • Less attention has been given to automating decision-making within regulatory frameworks for human research data access, use, and exchange.

Purpose of the Study:

  • To explore the potential of automated decision support (ADS) systems to accelerate data access management in scientific research.
  • To examine the ethical implications of using ADS for managing access to human research data, particularly biospecimens and biobanked samples.

Main Methods:

  • Mapping the application of ADS systems, leveraging their binary logic and real-time capabilities, to the data access request process.
  • Discussing the Global Alliance for Genomics and Health Data Use Ontology standard as a case study for ADS in genomic data access management.

Main Results:

  • ADS systems can improve auditability, consistency, and efficiency in the data access request process.
  • This procedural improvement has the potential to lead to fairer outcomes for researchers seeking data.
  • ADS can reinforce data subject rights and protect informational dignities by ensuring data use aligns with participant values.

Conclusions:

  • Automating data access management with ADS offers a path toward more equitable and efficient scientific discovery.
  • Addressing ethical, legal, and social challenges is crucial for the practical implementation of ADS in health data governance.
  • Future research should focus on computational governance strategies to build public trust and maximize the scientific value of health-related human data.