Tag: technology

Why We Resist Before We Embrace: Lessons from Rogers for AI Adoption in India

Every era thinks it’s teetering on the edge of something revolutionary; every era also finds reasons to step back.

Every era thinks it’s teetering on the edge of something revolutionary; every era also finds reasons to step back. Printing presses were accused of ruining memory, erasers of encouraging mistakes, calculators of killing math, and the internet of replacing thinking with clicking. Today, AI tutors take their turn in the dock. The artefacts change; the reflex endures.

A short tour of resistance (and why it recurs)

In classrooms and workplaces, pushback is rarely irrational. It encodes uncertainty about norms, skills, incentives, and unintended consequences.

1440: Gutenberg’s printing press sparked criticism, with academics fearing that printed books would reduce memorization and critical thinking.
1980: The advent of the pencil eraser was seen as an incentive to make mistakes and a threat to careful writing.
1986: Protests against the use of calculators, arguing that students would forget how to do mental calculations.
2000: Criticism of internet access in classrooms, for fear of misinformation.
2010: Resistance to the use of smartphones and tablets in the classroom, with concerns about distractions and decreased social interaction among students.
2025: Resistance to AI tutors as easy-task.ai.

Everett Rogers’ Diffusion of Innovations gives us a durable lens: innovations spread through social systems over time, and their adoption depends on five perceived attributes: relative advantage, compatibility, complexity, trialability, and observability; moving in waves from innovators to laggards along a characteristic S‑curve.

“Academic research involves three steps: finding relevant information, assessing the quality of that information, then using appropriate information either to try to conclude something, to uncover something, or to argue something. The Internet is useful for the first step, somewhat useful for the second, and not at all useful for the third.”
– By Beth Stafford in Brabazon, Tara (2007). The University of Google, Ashgate (UK), pp.22.
New artefacts, old anxieties

What this means for AI in Indian education

Policy has set the table: NEP 2020 positions AI as a lever for learning, assessment, and administration; recent discussions propose Centres of Excellence to build institutional capacity. But diffusion won’t hinge on policy documents alone. Teachers and administrators adopt when AI clearly saves time, fits curricula and local languages, feels simple and trustworthy, can be piloted safely, and shows visible gains in student work.

Reviews of AI‑in‑education in India underline the same arc: real promise (personalization, admin efficiency), real constraints (infrastructure, capacity, equity, ethics). The practical path is micro‑pilots, transparent evaluation, and teacher‑led exemplars. Start small, show what changed, let peers observe, then scale. For scenario‑based futures and implementation pathways, see AI Scenarios on my site: r‑srini.in/2026/01/18/ai‑scenarios/

Augment, not replace!

And for Indian industry: beyond pilots to redesigned work

Indian firms are enthusiastic adopters; many already use AI and most plan to expand. Globally, however, even frequent users often stall between pilots and enterprise‑level impact. Crossing that gap demands workflow redesign (not bolt‑on tools), sound data governance, and credible line‑of‑sight to P&L. Executives need proof of advantage, confidence in compatibility with systems and compliance, lower complexity via guardrails and usable interfaces, safe trialability through sandboxes, and peer observability via references.

Public investment, from compute to Centres of Excellence, helps the ecosystem, but diffusion inside firms still hinges on these attributes. High performers are already reframing AI from cost‑cutting to growth and innovation, a signal the early majority watches closely.

Three moves that convert resistance into readiness

  1. Stage it: Run bounded pilots that teachers/managers own; publish before‑after evidence others can observe and adapt. (Trialability, observability)
  2. Lower friction: Invest in training, vernacular UX, and clean data pipes; perceived complexity kills diffusion faster than controversy. (Complexity, compatibility)
  3. Prove advantage: Tie AI to concrete outcomes—recovered teacher time, reduced cycle times, new revenue lines—tracked publicly. (Relative advantage)

Bottom line: Resistance isn’t the enemy; it’s information. Use it to make AI useful, legible, and locally credible; the conditions under which social systems adopt innovations.

Cheers.

(c) 2026. R. Srinivasan.

Synthetic Data vs. Anonymised / Differentially Private Data: Why the Distinction Matters

I was invited to be part of a panel on AI during the launch of the Safe Human Future community last week in Delhi.

In conversations during the event, I met with some interesting folks and had long conversations about data quality. When talking about privacy-preserving data use, three terms often get conflated (at least amongst the novices): synthetic data, anonymised data, and differentially private data. While all three aim to reduce privacy risks, they differ fundamentally in construction, guarantees, and ideal use-cases.

Anonymised data is real data with direct identifiers removed or masked. Its weakness is structural: if the underlying patterns of the real dataset remain embedded, attackers can often re-identify individuals through linkage attacks or inference techniques. A growing body of research shows that even datasets without names or addresses can be deanonymised when combined with auxiliary information, because the data points are still tied to real individuals.

Differential privacy, by contrast, injects calibrated noise into queries or datasets so that the presence or absence of any individual does not materially change analytical outputs. This provides a mathematically provable privacy guarantee. But the trade-off is accuracy: heavy noise addition can distort minority-class patterns or small-sample statistical relationships.

Synthetic data takes a different route altogether. Instead of modifying real data, it generates completely artificial records that mimic the statistical properties of the source dataset. No row corresponds to any real person. This disconnection from real individuals eliminates a large class of re-identification risks and makes the data highly shareable. It does, however, require careful quality evaluation—poorly generated synthetic data can hallucinate unrealistic correlations or miss critical rare events.

Why Firms Use Synthetic Data

Firms increasingly rely on synthetic datasets for scenarios where real-world data is sensitive, incomplete, biased, or simply unavailable. Typical use-cases include:

  1. Product development and testing: Fintech and healthtech companies often need realistic datasets to test algorithms safely without exposing personal information.
  2. Machine learning model training: Synthetic data helps overcome class imbalance, enrich training sets, or simulate rare but important events (e.g., fraud patterns).
  3. Data sharing across organisational boundaries: Cross-functional teams, vendors, or academic collaborators can work with synthetic datasets without entering into heavy data-processing agreements.
  4. Accelerating regulatory compliance: In sectors such as banking, telecom, and healthcare, where privacy regulations are tight, synthetic datasets reduce bottlenecks in experimentation, sandboxing, and model audits.

From a governance standpoint, synthetic data often plays a complementary role: firms still use real data for production-grade analytics but use synthetic data for exploration, prototyping, and secure experimentation.

Alignment with the Indian DPDP Act and Rules

The Digital Personal Data Protection (DPDP) Act, 2023 emphasises lawful processing, purpose limitation, data minimisation, and protection of personal data. Importantly:

  • The Act’s obligations apply only to digital personal data of identifiable individuals.
  • High-quality synthetic data, by definition, contains no personal data, and therefore does not fall within the compliance net.

This creates a strategic opportunity for firms: synthetic datasets allow innovation outside the regulatory burden while maintaining alignment with the Act’s intent: protecting individuals’ data rights. Many enterprises are beginning to use synthetic data as a “privacy-by-design accelerator,” reducing the operational costs of compliance while enabling safe analytics.

Synthetic Data and Artificial Pearls: A Useful Analogy

The distinction between synthetic and real data is similar to the comparison between artificial pearls and natural pearls. Natural pearls, harvested from oceans, are biologically authentic but scarce, costly, and highly variable in quality. Artificial pearls, especially high-grade cultured pearls, are manufactured with precise control over structure, size, lustre, and durability.

In many cases, artificial pearls are actually superior to natural ones:

  • They have more consistent structure.
  • They are available in specific sizes and configurations designers need.
  • Their strength and finish can be engineered for durability.
  • They reduce dependence on environmentally intensive harvesting.

Synthetic data plays a similar role. Just as the best artificial pearls capture and improve upon the aesthetics of natural pearls without relying on oysters, synthetic datasets capture the statistical essence of real data while offering higher usability, lower risk, and greater design freedom.

In contexts where quality matters more than provenance, such as stress-testing jewellery designs or building machine learning models, the engineered version can outperform the natural one.

Cheers.

(C) 2026. R Srinivasan.