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Safe and responsible use of AI in evidence synthesis: Recommendations from the Evidence Synthesis Infrastructure
Ashrita Saran1, Malcolm Macleod2, Promise Nduku3
1Global Development Network, India.
Aim:
This paper aims to identify and prioritise actionable, system-level solutions for the safe and responsible use of artificial intelligence (AI) in evidence synthesis.
Background:
The increasing integration of AI Digital Evidence Synthesis Tools (AI-DEST) presents both opportunities and challenges. Concerns regarding ethical implications, biases in AI algorithms, and disparities in access to high-quality evidence necessitate a structured approach to ensure that AI is used responsibly and effectively.
Methods:
The group employed a systematic approach, guided by the SHOW ME the Evidence Consensus framework. The framework that emphasizes transparency, stakeholder engagement, and iterative validation across the evidence lifecycle through structured scoping exercise comprising literature review, stakeholder survey, expert interviews, and iterative group discussions. This was a two-phase process: the first phase focused on problem identification through literature reviews, surveys, and stakeholder interviews, while the second phase concentrated on identifying and prioritizing potential solutions. A total of 50 solutions were initially proposed, which were then refined through a structured prioritization process based on criteria such as innovation, feasibility, and potential impact.
Results:
The group identified several key solutions, including the development of an open-source Evidence Synthesis Studio (ESS) that integrates various Digital Evidence Synthesis Tools (DESTs) and a Comprehensive Evidence Synthesis Plug-In Architecture (CESPIA)- an interoperable framework for the validation, benchmarking, and integration of AI tools. These solutions aim to enhance the efficiency and transparency of evidence synthesis processes while ensuring equitable access for diverse user groups, particularly in the Global South.
Discussion:
The proposed solutions emphasize the importance of engagement with key interest holders, including evidence synthesis practitioners, policymakers, and tool developers, alongside broader transparency-oriented public engagement. By addressing the unique challenges faced by underrepresented communities, the group aims to mitigate biases and promote ethical AI use. The integration of citizen feedback and iterative design processes is crucial for developing tools that meet the needs of diverse interest holders.
Conclusions:
The recommendations from Working Group 3 provide a comprehensive framework for the responsible use of AI-DEST. By fostering collaboration, transparency, and inclusivity, these solutions aim to strengthen evidence synthesis as a public good, ensuring that high-quality evidence is accessible to all, regardless of background or resources.
