Policymakers’ Dialogue on AI, Policy Evolution, and the Rule of Law
Abstract
The session examined how India and Germany can shape AI governance that is inclusive, transparent, and anchored in the rule of law. Panelists discussed the rapid technical advances in AI, the need for “soft‑touch” regulation that protects innovation while safeguarding public values, and concrete mechanisms for turning high‑level principles into actionable policies. The dialogue moved from constitutional‑based approaches in India to Germany’s risk‑based AI Act, explored frameworks such as JCT (Justifiable, Contestable, Traceable) for financial‑sector AI, and highlighted the role of digital public infrastructure, smart‑meter roll‑outs, and cross‑border data sharing. The closing address reiterated the imperative that AI must serve democratic resilience, inclusive growth, and trustworthy rule‑making.
Detailed Summary
| Speaker | Content |
|---|---|
| Moderator (unnamed) | Requested the panel to gather for a group photo and introduced the session’s purpose: to explore the “Democratising AI Resources” pillar of the AI Impact Summit. |
| Stephen Stamse (CAS) | Gave a high‑level framing: AI development is occurring “by the week, by the day, sometimes by the hour.” He warned that while AI may be approaching Artificial General Intelligence (AGI) or a future singularity, the technology also carries “downsides and risks” that must be managed through balanced rule‑making. Noted the bilateral dimension of Europe–India cooperation, especially in language‑model localisation and cultural relevance. Announced the arrival of two German speakers – Dr. Markus Siewert and Prof. Kristina Sinemus – and explained a brief delay for the scheduled keynote by Mr. Singh Lee (to be delivered later). |
| Moderator | Set ground rules: panelists should keep remarks succinct, stay within a set time limit, and engage the audience where possible. |
2. Indian Perspective on AI Governance
| Speaker | Content |
|---|---|
| Dr. Sasmit Patra (Rajya Sabha) | Emphasised an inclusive, constitution‑based approach. India is pursuing “soft‑touch” regulation rather than a rigid, EU‑style AI Act. Core constitutional values – equality, transparency, accountability, and inclusivity – guide policy. Because data is unevenly distributed across rural and urban areas, the focus is on “digital public infrastructure (DPI)” and ensuring interoperability of the DPI stack. |
| Jameela Sahiba | Reinforced the inclusive theme, noting that AI rules are demanded by weaker societal groups to keep powerful actors in check. Highlighted the need for “balanced, equitable society” where innovators have access to data, resources, and fair rules. Stressed that AI should not become a tool that entrenches existing power structures. |
| Mathukumilli Sribharat | Added a political‑level observation: AI governance must be a democratic process involving citizens, parties, and civil society. Warned against a single‑path solution; stressed continual dialogue with constituents. |
3. German Perspective and Translating Inclusiveness into Action
| Speaker | Content |
|---|---|
| Dr. Markus Siewert (TUM) | Argued that Germany looks to the Global South (India, Africa) to learn how to embed inclusivity into technology. Proposed a policy‑cycle mental model: 1. Up‑stream – collect weak signals, involve diverse stakeholders early, set up peer‑to‑peer learning. 2. Mid‑term – use policy pilots and participatory tools to experiment. 3. Down‑stream – create feedback loops to monitor implementation. Stressed that no single tool suffices; a flexible “learning‑while‑doing” mindset is required. |
| Prof. Dr. Kristina Sinemus (Hessian Ministry) | Described concrete actions in Hessen: • Investment in talent – funding PhD‑level AI projects and startup incubators. • AI‑Quality & Testing Hub – a label‑based certification aligned with the EU AI Act, intended to give “transparent, trustworthy” AI products market credibility. • Outreach tour – engaging with industry and citizens to build trust in AI, emphasizing that transparent rules foster investment and innovation. |
4. AI Governance in the Financial Sector
| Speaker | Content |
|---|---|
| Mr. Pravin Anand (Law Firm) | Outlined three systemic challenges in AI‑driven finance: 1. Volume of legislation – India passes ~20‑30 bills a year, far fewer than the EU’s 300‑400, so legislative speed is limited. 2. Courts as interim regulators – Indian courts have historically filled gaps (e.g., domain‑name seizures, privacy rulings) before legislation catches up. 3. Rule‑making vs. primary legislation – India prefers non‑enforceable guidelines that later become binding rules, allowing flexibility but reducing certainty. |
| Ivana Bartoletti (Wipro) | Emphasised that AI governance should be seen as value‑creation, not a compliance checkbox. Key points: • Embedding controls at design time – privacy, security, and ethical safeguards must be baked into products. • Democratizing AI tools – tools should be designed by diverse teams; otherwise they embed narrow biases. • Governance = long‑term value – “justifiable, contestable, traceable” (JCT) approach ensures customers, auditors, and regulators each get the transparency they need. |
| Moderator (Neeraj Agarwal) | Introduced a subsequent panel on AI for fraud prevention and financial inclusion (see Section 7). The moderator’s role was to bridge the policy dialogue with sector‑specific pilots. |
5. AI‑Enabled Fraud Prevention & Financial Inclusion (BFSI Panel)
This sub‑session, while not listed in the original speaker list, was part of the recorded programme and therefore is included in the comprehensive summary.
| Speaker | Content |
|---|---|
| Mr. Suresh Sethi (Protein eGov) | Highlighted the scale of digital payments in India (UPI > 20 billion transactions per month) and the need for AI to move from rule‑based fraud detection to real‑time, adaptive risk intelligence. Stressed three pillars: speed & reactiveness, networked intelligence (detecting mule‑account chains across institutions), and continuous learning. |
| Mr. Bhuvan Lodha (Mahindra & Mahindra AI Division) | Introduced the JCT framework (Justifiable, Contestable, Traceable) for AI in finance, describing how it satisfies customers (transparency, recourse), auditors (evidence, bias checks), and regulators (human‑in‑the‑loop oversight). |
| Mr. Manish Agarwal (Kotec 811) | Discussed tiered onboarding: low‑friction products for low‑risk customers, with higher‑risk cases escalating to video‑KYC or physical verification. Cited AI‑driven risk scoring that reduces false positives while preserving financial inclusion. |
| Mr. Saurabh Mithal (DBS India) | Noted that AI currently accounts for < 20 % of data‑center loads; the share is projected to quintuple by 2030. Highlighted the flexibility potential of large‑scale data‑centers to shift loads in response to grid signals, provided regulatory mechanisms are in place. |
| Mr. Srinjai Ghosh (Temasek) | Emphasized that data‑center localisation (e.g., renewable‑powered campuses) can achieve 100 % annual renewable coverage and that the industry is moving toward hour‑by‑hour renewable matching. |
| Moderator (Neeraj Agarwal) | Summarised the panel’s consensus: AI can speed up fraud detection, lower friction for legitimate users, and enable cross‑border data‑sharing for better risk analytics, but regulatory clarity and trust frameworks are essential. |
6. AI for Grid Modernisation – South‑South Learning
The transcript transitions to a second, more technical panel on AI‑enabled power‑system operation. Although no additional session title was supplied, the content belongs to the same overarching dialogue on AI policy and rule‑of‑law.
| Speaker | Content |
|---|---|
| Sudhat (Energy Analyst) | Described three macro‑trends driving AI adoption in power systems: electrification, variable renewable generation, and affordability pressures. Stressed that AI is needed to forecast minute‑by‑minute renewable output and to manage distributed resources. |
| Namrita Mukherjee (International Solar Alliance) | Highlighted India’s Smart‑Meter rollout (250 million units) as the foundational data layer for AI. Mentioned the need for open‑source data platforms and digital public infrastructure (DPI) to enable cross‑border learning. |
| Mahesh (Policy Expert) | Outlined regulatory levers: 1. Renewable‑energy forecasting mandates (e.g., deviation‑settlement mechanisms). 2. Frequent grid‑code updates to accommodate DERs and AI‑driven controls. 3. Incentivising digital twins, advanced analytics, and performance‑based incentives for utilities that reduce curtailment or improve outage reliability. |
| Sujit (IES/DPi) | Framed AI as a systemic “plumbing” problem: the grid must evolve from a static, unidirectional network to a multidirectional, self‑organising platform where every DER (rooftop solar, EVs, data‑centers) can exchange real‑time signals. Cited ongoing pilots for predictive asset health monitoring and AI‑driven connection‑eligibility checks for new consumers. |
| Ranjan (Utility Executive) | Shared concrete outcomes from AI‑based demand‑forecasting: 20‑25 % cost reduction on short‑term market purchases, 10‑15 % on spot‑market procurement, and a pilot‑to‑scale journey for AI‑driven preventive maintenance on 43 kV/11 kV feeders (using drones, satellite imagery, and failure‑prediction models). |
| Siddharth (Researcher, Data‑Centers) | Presented the energy‑efficiency challenge of data‑centers: today ~1.5 % of global electricity, projected 3 % by 2030, with AI workloads growing five‑fold. Argues that AI‑driven load‑shifting can make data‑centers grid assets rather than burdens, but regulatory mechanisms (e.g., demand‑response programmes) are still missing. |
| Namrita (More) | Clarified the difference between “annual renewable coverage” (year‑level contracts) and “hourly renewable matching” (future target for many tech firms). Stressed that transparent, anonymised data sharing across borders will accelerate such matching. |
| Sujit (Again) | Re‑iterated the importance of standards (interoperability, data‑privacy, security) and the India Energy Stack as a set of reusable building blocks that can be customised for each DISCOM while preserving a common core. |
| Ranjan (Again) | Highlighted capacity‑building for regulators as a bottleneck: talent in the energy sector is paid half of comparable tech‑sector salaries, making it difficult to attract AI expertise. Suggested regulatory sandboxes, performance‑based incentives, and continuous up‑skilling as remedies. |
7. Closing Address – Rule of Law & Democratic Resilience
| Speaker | Content |
|---|---|
| Mr. Mahaveer Singhvi (Ministry of External Affairs) | Summarised the day’s insights: • AI transformation is already happening; the question is how to keep it democratic, accountable, and anchored in the rule of law. • Democratisation is not just computational power but also access to quality data, transparent governance, and legal safeguards. • Stressed the three pillars of the rule of law for AI: accountability, transparency, fairness. • Highlighted India’s Digital Public Infrastructure (DPI) (Aadhaar, UPI, DG‑Locker) as a model for scaling inclusive AI services. • Called for global cooperation (Europe, Singapore, other partners) to avoid fragmented regulatory “silos”. • Emphasised capacity‑building for policymakers, regulators, and citizens so that AI becomes a tool for empowerment rather than concentration of power. • Concluded that the rule of law is the foundation that will allow AI to become a catalyst for equitable growth. |
Key Takeaways
- Inclusivity First – Both India and Germany advocate soft‑touch, constitution‑based regulation (India) and risk‑based AI Act (Germany) that embed equality, transparency, accountability, and inclusivity throughout the AI lifecycle.
- Governance as Value‑Creation – AI governance should be seen as a means to generate long‑term business value (trust, resilience, innovation) rather than a mere compliance burden.
- JCT Framework for Finance – Justifiable, Contestable, Traceable (JCT) offers a practical template for satisfying customers, auditors, and regulators alike in AI‑driven financial services.
- AI‑Powered Fraud Prevention – Real‑time, network‑wide AI can dramatically improve detection speed, reduce false positives, and protect the financial inclusion of marginalised users.
- Data‑Centers as Grid Assets – With appropriate hour‑by‑hour renewable matching and demand‑response mechanisms, data‑centres can shift from being a net‑load to a flexible, dispatchable resource.
- Regulatory Levers for the Power Sector – Mandatory renewable‑forecasting, frequent grid‑code updates, performance‑based incentives, and sandbox pilots are essential to move from pilots to nationwide AI‑enabled grid operations.
- Digital Public Infrastructure (DPI) is the Backbone – Massive smart‑meter roll‑outs and open‑source data platforms provide the necessary data foundation for AI across energy, finance, and public services.
- South‑South Cooperation Matters – Knowledge sharing among emerging economies can accelerate adoption of AI‑driven grid solutions, but each country must first establish a basic digital‑data layer before “leap‑frogging” is realistic.
- Talent Gap & Capacity Building – The skill shortage in the energy sector (AI, data science) is a primary bottleneck; solutions include regulatory sandboxes, performance‑based rewards, and continuous up‑skilling of regulators and utility staff.
- Rule of Law as Enabler – Upholding accountability, transparency, and fairness ensures AI’s democratic deployment, builds public trust, and safeguards against power concentration—critical for both national policy and international cooperation.
See Also:
- artificial-general-intelligence-a-new-paradigm-of-safety-security-privacy-ethics-and-governance
- responsible-ai-at-scale-governance-integrity-and-cyber-readiness-for-a-changing-world
- mahaai-building-safe-secure-and-smart-governance
- ai-for-democracy-reimagining-governance-in-the-age-of-intelligence
- democratizing-ai-resources-and-building-inclusive-ai-solutions-for-india
- the-governance-gap-designing-global-standards-for-ai-advisory-boards
- ai-for-economic-growth-and-social-good-ai-for-all-driving-economic-advancement-and-societal-well-being
- democratizing-ai-resources-in-india
- ai-for-all-indias-policy-architecture-for-public-interest-ai-and-inclusive-development
- from-evidence-to-scale-testing-financing-and-operationalizing-technology-and-ai-for-development-and-humanitarian-action