By Varunavi Bangia

In the digital age, technology has outpaced the law. While innovation brings a world of new possibilities, it simultaneously necessitates robust new defenses. For decades, rapid technological advancements have largely evaded legal scrutiny, leaving a profound regulatory gap. To understand the friction between modern technology and current legislation, one must confront the reality of the contemporary data economy: it is no longer just about the data you give, but the inferences drawn from who you are.

Current data protection regulations across global jurisdictions are anchored in the concept of "informed consent." However, the data economy has evolved far beyond the monetization of personal data collected directly from a source. Today, big data, inferential analytics, and advanced knowledge discovery have rendered the traditional, narrow definition of "personal data" obsolete.

The Erosion of Individual Identity

The most critical challenge facing regulators today is the "de-individualization of the person." Instead of treating citizens as unique individuals based on their own characteristics, modern algorithms treat them as mere data points belonging to specific clusters. By aggregating massive amounts of information from diverse sources, corporations and governments can build profiles that are more complete—and more invasive—than ever before.

This shift has created a dangerous legal loophole. Many data protection frameworks define "personal data" as information relating to a natural person who is "directly or indirectly identifiable." Once data is anonymized or generalized, it falls outside the purview of regulation. Yet, the impact of such data remains deeply personal. If an algorithm identifies an individual as belonging to a category susceptible to a specific disease, that person may face systemic discrimination—denied insurance or employment—often without ever knowing their categorization was the catalyst for that denial.

Chronology of a Regulatory Lag

The trajectory of data regulation has consistently struggled to catch up with the capabilities of predictive analytics.

• Early 2000s: The rise of Knowledge Discovery in Databases (KDD) began to challenge traditional notions of individual privacy, as scholars like A. Vedder identified the risk of group-based stereotyping.
• 2010-2015: The "Big Data" explosion saw the collapse of the anonymity promise. Research by Paul Ohm and others demonstrated that even anonymized datasets could be cross-referenced to re-identify individuals with alarming accuracy.
• 2017: The landmark Puttaswamy v. Union of India judgment in the Indian Supreme Court recognized informational privacy as an expression of identity, laying a constitutional foundation for potential group-based protections.
• 2018-2021: Despite the Puttaswamy precedent, legislative efforts—including the Personal Data Protection Bill (2017) and the Data Protection Bill (2021)—failed to address the nuances of inferential analytics. These bills remained tethered to the "personally identifiable" standard, ignoring the pervasive harm of algorithmic profiling.
• Present Day: Regulators continue to treat personal and non-personal data as mutually exclusive categories, failing to account for the reality that non-personal data is frequently used to create highly personal, predictive profiles.

Supporting Data: The Anatomy of Inferential Harm

The danger lies in the "inference gap." While some jurisdictions include inferences within the definition of personal data, the practical application is narrow. Profiling can occur without identifying a specific person and without utilizing any "personal data" at all.

For instance, an entity might use irreversibly de-identified, non-human datasets to predict the creditworthiness or health risks of a group. Under current legal frameworks, this process is often exempt from data protection standards because it does not involve an "identifiable" person.

This leads to a binary, and ultimately destructive, dichotomy:

1. Personal Data: Subject to strict protection but increasingly easy to bypass through aggregation.
2. Non-Personal Data: Treated as an economic resource, devoid of privacy constraints, yet capable of inflicting significant social and individual harm through group-based discrimination.

The reality is that these categories are no longer distinct. We must move toward a model that recognizes "human non-personal data"—anonymized data that can still identify or impact a person or group—as a subset of protected personal information.

Official Responses and the Legislative Vacuum

Governmental bodies have, at times, acknowledged the need to regulate non-personal data. However, these efforts are often framed through an economic lens—viewing non-personal data as "untapped potential" for growth rather than a privacy risk.

In India, the Srikrishna Committee and subsequent Joint Parliamentary Committee reports have missed critical opportunities to address the specific dangers of data mining. While the Supreme Court of India in Puttaswamy and Navtej Johar acknowledged that the aggregation of seemingly "non-private" data creates dangerous, detailed profiles, these judicial warnings have yet to translate into hard, enforceable regulatory policy.

The current trend of legislative drafting persists in the belief that privacy protection must inherently come at the cost of economic development. This is a false choice. True regulation would involve "hard accountability"—auditing algorithmic decisions and conducting ex-ante impact assessments that look beyond security to the ethical and social consequences of data processing.

The Case for Collective Privacy

If the law is to survive the age of big data, it must pivot from protecting the individual to protecting the "group." This is not a shift toward collective identity in a sociological sense, but a defensive strategy against "categorical profiling."

1. Redefining the Right

We need a right that belongs to a group as a group. This right would not depend on the members of the group knowing one another or having organizational ties. Instead, it would focus on the entity doing the profiling. If a company clusters people based on shared behavioral patterns to facilitate discriminatory decision-making, the group itself must have the legal standing to challenge that practice.

2. Categorical Privacy

As an alternative to traditional rights, "categorical privacy" would apply to data that, once aggregated, can no longer identify an individual but retains the power to impact them. If the disclosure or use of such aggregated data leads to adverse consequences—such as social stigmatization or financial exclusion—it should trigger the same protections afforded to personal information.

3. Ex-Ante Accountability

Regulators must move from reactive measures to proactive ones. This includes:

• Algorithmic Auditing: Requiring companies to submit to audits that assess the bias and societal impact of their predictive models.
• Ethical Impact Assessments: Mandatory assessments before the deployment of profiling technologies, evaluating not just technical security, but the potential for discriminatory social outcomes.
• Disincentivizing Misuse: Implementing regulatory penalties that make the mass, unchecked aggregation of group data a high-cost endeavor for corporations.

Implications for the Future

The failure to evolve our regulatory framework will have severe consequences for society. We are moving toward a future where our opportunities—our jobs, our health coverage, our credit, and our social standing—are determined by "inferred identities" generated by algorithms that we cannot see, challenge, or even understand.

The current focus on "notice and consent" is a relic of an era when data collection was a deliberate act by an individual. Today, data is a byproduct of existence. We are profiled by what we do, what we don’t do, and most significantly, by the group that an algorithm decides we belong to.

To secure a digital future, we must stop treating privacy as a purely individualistic pursuit. We need a robust framework that recognizes the power of inferential analytics and treats collective data as a critical, protected asset. Without this, the right to privacy will become an empty promise, shielding the individual while leaving the group—and by extension, the person—entirely exposed to the invisible, algorithmic hand of the modern data economy.

This research was supported by an unrestricted scholarship pursuant to the Facebook India Tech Scholars Program 2021-2022. The work was conducted under the research mentorship of the Software Freedom Law Center and without any oversight from Meta. The views expressed herein are those of the author and are not necessarily those of Meta or the Software Freedom Law Center.

Varunavi Bangia is a 5th-year student of BA LLB (Hons) at the West Bengal National University of Juridical Sciences (WBNUJS), Kolkata.