@Polysynq

Connecting digital health policy and insights to system change.

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Joined August 2025
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Responsible AI is gaining policy attention worldwide. But publishing a strategy is not the same as protecting people. The Global State of Responsible AI Report 2026 examines this gap across 135 countries to know what makes AI governance effective.
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- Protect workers and vulnerable groups.  • Ensure transparency and redress.  • Safeguard public-sector AI.
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Responsible AI isn’t just a policy document. It needs institutions, protections and accountability people can feel in daily life. Which gap is most urgent in your country? Follow Polysynq for AI insights. 🌍
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Responsible AI requires more than a framework. The Global State of Responsible AI Report 2026 identifies gaps between policy commitments and the institutions needed to put them into practice. Follow Polysynq for more evidence-led digital health and AI insights. 🌍
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According to EY’s latest report, the objective for most governments is developing greater control over critical AI capabilities while continuing to benefit from global innovation and partnerships. Read more: substack.com/@polysynq/note/…
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AI is changing how science is written. From literature reviews to drafting and editing, researchers are increasingly using generative AI. But one critical question remains: Who takes responsibility when AI helps produce scientific work?
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This raises bigger questions: ▪️ How should AI contributions be disclosed? ▪️ Who is accountable for AI-generated errors? ▪️ How do we recognise the human labour behind AI? ▪️ Can current authorship models keep pace with technological change?
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Perhaps AI is giving us an opportunity to rethink academic authorship, research hierarchies, and how scientific labour is recognised. AI can assist scientific work. But accountability must remain clear. What changes would you like to see in AI-assisted research publishing?
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Who is responsible when AI helps write science? 🤖🔬 As generative AI reshapes research, a critical question emerges: Who takes responsibility for the science AI helps produce? A thought-provoking commentary by Robert Braun explores authorship, accountability, and trust
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This debate goes beyond AI. It also challenges us to examine invisible labour, fair recognition, and transparency in scientific research. Responsible AI adoption must support innovation without compromising research integrity.
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As AI becomes embedded in science, how should institutions balance innovation with accountability? What should researchers disclose about AI use, and who should be responsible for errors? 💬 Join the conversation. Follow @Polysynq for insights on AI governance, research policy.
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