Designing metrics to enable trusted AI ecosystems in Asia: ASPI

A new report by the Asia Society Policy Institute has flagged a critical gap in Asia’s artificial intelligence (AI) journey: the absence of measurable trust frameworks that can support responsible and scalable adoption across the region’s diverse economies.Titled “Designing Metrics to Enable Trusted AI Ecosystems in Asia,” the report authored by Arun Teja Polcumpally and Faye Simanjuntak argues that while AI is widely recognised as a general-purpose technology, its adoption across Asia remains uneven due to structural barriers and, more fundamentally, a deficit of trust among governments, industry, and citizens.
The study draws on an analysis of AI policies across 15 Asian countries and insights from two high-level roundtables held on the sidelines of the AI Impact Summit 2026 in New Delhi. It identifies nine core factors that policymakers must address to build trusted AI ecosystems capable of balancing innovation with accountability.A key highlight of the report is a stark observation: fragmented governance, limited institutional capacity, and the scarcity of local-language datasets are slowing AI adoption in Asia. These gaps, the authors note, are not merely technical they reflect deeper systemic trust deficits that hinder collaboration and data sharing.“Structural barriers point to a deeper underlying deficit: insufficient trust among the public, government, and industry,” the authors write, underlining the urgency of creating measurable indicators to track and build trust.
Key Takeaways from the Report:
•Nine pillars of trusted AI ecosystems: Trusted datasets, AI infrastructure, AI skills and awareness, global value chain leverage, ethical AI development, misinformation governance, AI governance frameworks, environmental sustainability, and cybersecurity.
•Trust deficit is the core challenge: Weak data governance, lack of transparency, and limited coordination undermine confidence in AI systems.
•Uneven AI readiness across Asia: Advanced economies focus on supply chains and governance leadership, while developing nations grapple with infrastructure and skills gaps.
•Fragmented policy approaches: Countries are pursuing sovereign AI strategies with minimal regional coordination, limiting collective progress.
•Need for measurable metrics: Without common indicators, benchmarking progress and identifying gaps remains difficult.
•Global interdependence cannot be ignored: AI supply chains are inherently international, making collaboration essential for trust-building.
•Environmental and cybersecurity risks rising: AI’s growing energy footprint and vulnerability to cyber threats require stronger policy attention.
The report highlights that countries such as India, Singapore, and South Korea are advancing AI governance and infrastructure, albeit with differing priorities—from digital public infrastructure and talent development to semiconductor dominance and regulatory leadership. Meanwhile, several Southeast and South Asian economies continue to face foundational challenges in compute capacity, data readiness, and workforce skills.
Importantly, the authors caution against a one-size fits all approach. Instead, they advocate for a flexible metrics framework that allows countries to benchmark their progress while accommodating national priorities and development contexts.“A common metrics framework cannot resolve these differences—nor should it,” the report concludes. “Rather, it can provide an analytical foundation to identify where interventions are most needed and build an evidence base for governance investment.”The findings come at a time when countries across Asia are racing to harness AI’s economic potential. Estimates cited in the report suggest AI could add up to $1.9 trillion to India’s GDP by 2035, while significantly boosting growth trajectories in economies such as Malaysia and Indonesia.
Yet, the report makes it clear that without trust built through transparent systems, accountable governance, and measurable outcomes AI’s transformative potential in Asia could remain uneven and under-realised.As policymakers navigate competing priorities of innovation, sovereignty, and safety, ASPI’s framework offers a timely blueprint: not to standardise AI governance across Asia, but to make it comparable, accountable, and ultimately, trustworthy.