Contemporary Practices of AI Platforms: How Far does India’s Data Protection Regime Reach - BW Legal World

The Digital Personal Data Protection Act, 2023 is India’s data protection framework replacing the earlier Information Technology Act, 2000 regime. It matters because platforms increasingly use personal data while integrating AI across their services. The Act sets rules for responsible data handling, rather than governing every risk created by AI systems.

When AI platforms process personal data, the Act requires notice and consent. It also supports data minimization, purpose limitation, user rights, regulated cross-border transfers, and related safeguards for processing. These rules shape how platforms collect and use information for AI-related activities.

The Act does not directly regulate algorithmic transparency, bias, or AI-generated decisions. Its reach largely ends at legally permissible data processing. The article therefore calls for broader rules that address algorithmic accountability, explainability, user protection, and different levels of risk across AI applications.

The article identifies seven sectors or areas using AI: healthcare, media, manufacturing, retail, finance, ocean monitoring, and drug discovery. It presents these as examples of AI’s reach, ranging from major industries to highly specialized applications. The breadth matters because AI is becoming part of many different economic and operational environments.

This spread means platforms can use AI in activities involving health, financial dealings, production, commerce, research, and environmental observation. These uses may involve different kinds of data and different consequences for users. The article links this expanding integration with the need for regulatory analysis across platforms.

The larger point is that AI’s impact extends beyond data collection. As platform economies and big data develop together, AI can influence services and outcomes across industries. India’s rules therefore need to address not only permissible data processing, but also transparency, accountability, and risks arising from AI-generated decisions.

A black-box algorithm is a system whose internal logic cannot be clearly interpreted by its users and, at times, even by its developers. The article connects this problem with the complexity of platform algorithms. Their decision processes may produce outputs without offering an understandable account of how those outputs were reached.

For example, a platform may generate a recommendation, ranking, or other automated result without showing which information shaped it. Users then cannot objectify or examine the algorithm’s logic. They remain uninformed about both the rationale for the output and the trajectory of the data used to produce it.

This opacity creates a gap in transparency and accountability. It also makes explainability obligations difficult to enforce in automated decision-making systems. The article argues that AI regulation must address this gap, rather than relying only on data protection rules governing collection and processing.

The article describes a conflict between the right to erasure and the way AI systems are trained. Platforms often state in their privacy policies that user data helps train AI systems and improve their outcomes. After that data is incorporated into training, removing the original information from the platform may not undo its influence inside the trained system.

For example, a user may request deletion of personal data after it has been used for AI training. The platform might erase the data from its stored records, but the article says it cannot necessarily retract the data from the trained AI system. The information has become embedded in the model’s technical structure.

As a result, erasure remains incomplete or limited. This creates a fundamental tension between a statutory user right and the practical architecture of AI models. The article indicates that stronger regulation must confront this technical difficulty directly.

These platforms pose higher risks because their dealings or data exchanges are sensitive, and some operate at a much larger scale of user interaction. The article names trading platforms, cryptocurrency platforms, payment gateways, and large-scale networking platforms. Errors or harmful automated decisions in these settings may affect users more seriously than decisions on lower-risk platforms.

The key mechanism is the combination of sensitive information, financial or transactional activity, and broad user reach. Algorithmic automation can influence outcomes across these systems. When the scale or sensitivity rises, the potential for loss or harm to users also rises, according to the article.

This is why the article rejects a uniform approach to AI oversight. Automation and algorithmic decision-making should be more strictly regulated on higher-risk platforms. Regulation should distinguish platforms by the likely consequences of their AI use, rather than applying identical requirements to every platform.

A risk-based AI framework would classify applications according to the potential harm they create for users. The article says platforms differ in the sensitivity of their dealings and data exchanges, as well as the scale of user interactions. Regulation should reflect those differences instead of treating every platform identically.

For example, trading platforms, cryptocurrency platforms, payment gateways, and large-scale networking platforms would receive stricter oversight. Their automation and algorithmic decision-making could face stronger transparency, accountability, and compliance requirements because the potential for loss or harm is higher. Lower-risk applications would not need the same level of control.

The article presents this differentiated framework as essential. It would focus regulatory attention where risks are greatest while still protecting users across platforms. The proposed approach would also move India beyond regulating only data processing and toward governing AI decisions and outcomes.

Algorithmic bias is the production of flawed or unfair outputs because an AI system learns from data that is not balanced or representative. The article says platforms are often trained on skewed datasets through unsupervised training. The system can then reflect the weaknesses present in its training material.

For example, if the training data overrepresents some patterns and underrepresents others, the platform may generate outputs that work poorly for certain users or situations. The mechanism is straightforward: the AI identifies patterns in the available data, and skewed data can lead it toward distorted conclusions. Those outputs may be inaccurate or biased.

The article treats algorithmic bias as a major risk of platform AI. Data protection rules alone do not resolve it because the problem concerns the system’s outputs and decision-making. Stronger regulation would therefore need transparency and accountability measures addressing how AI systems are trained and what they produce.