The effectiveness of consent in protecting privacy is diminishing in our data-rich world. A study found that companies’ privacy policies and actual data sharing practices are inconsistent, with technically sophisticated firms sharing less data. A digital consent framework, exemplified by India’s account aggregator system, could enhance privacy protection by allowing dynamic, informed consent, but it currently lacks features to fully ensure privacy, such as purpose limitation and data deletion upon consent revocation. Enhancements to this framework could restore faith in consent as a tool for privacy protection.

India’s draft Personal Data Protection Bill has sparked debate over data localization, with the government insisting on processing personal data within the country. Unlike other nations that restrict data transfers, India has made localization the default, an approach seen as out of character with its global trade stance.

While cryptocurrencies like Bitcoin and Facebook’s Libra can enable efficient cross-border transfers, India’s existing UPI system, with its robust digital payments infrastructure, already meets the country’s needs and can be internationalized without relying on blockchain technologies.

Regulating technology is challenging due to its rapid evolution, leading to a disjointed patchwork of rules. Governments often react to technology’s harmful impacts rather than proactively anticipating them. Regulatory entrepreneurship, where changing laws is part of the business strategy, complicates this further. To improve technology regulation, the government should focus on real objectives, adopt principle-based laws, and seek expertise from various fields. This approach would ensure more effective, adaptable, and comprehensive technology regulation.

Every new technology has disrupted the one before it and there is always opposition to the creative destruction technology fosters. In the long run disruption is necessary and we need to favor innovation to constantly reinvigorate the economy.

The economics of modern electricity requires huge power plants to be established at remote locations and be connected to our homes and offices through the grid, a network of transmission and distribution lines that ensures availability of electricity in our power sockets whenever we need it. This centralized model doesn’t contemplate storing energy. Instead it calls for 24/7 power generation, varying the amount of energy generated only by season and time of day based on the anticipated load.

The crash of Air France Flight 447 in 2009 highlights the dangers of over-reliance on automation in aviation. When the autopilot disengaged due to a malfunction, the pilots’ lack of manual flying experience led to a fatal error. But counter-intuitively - rather than relying on human intervention we need to trust more in machines and building better systems to automate them.

Many prominent figures have warned of the dangers of uncontrolled AI development. Even so, skeptics argue that humans will always control machines. Modern AI lacks the ability to reason with “what if?” questions and counterfactual imagination, which are essential for human-like intelligence. Though machines are not yet at this level, I would urge caution in advancing AI towards these capabilities.

India’s new IT minister, Ravi Shankar Prasad, has warned social media companies against misuse of their platforms for terrorism and communalism. The statement highlights a divisive debate between national security advocates and privacy proponents. A balanced approach is needed, with tech companies and law enforcement agencies moderating their stances to ensure both security and privacy.

Given the extensive surveillance efforts by the US and UK governments, as revealed by Edward Snowden, there has been a push for data localization laws in various countries. We need to question the effectiveness of mass data collection in preventing terrorism since the growing volume of data may render such efforts futile. At the same time we need to question the approach of data localization, given the difficulty in extracting actionable intelligence from vast amounts of information.