In News:

The Government of India released a Working Paper on Generative AI & Copyright proposing a national framework titled “One Nation, One License, One Payment”—India’s first structured model to regulate AI training on copyrighted works while balancing creator rights and AI innovation.

Core Issue

Generative AI (GenAI) models are trained on vast datasets scraped from books, news, music, films, and art—often without permission or compensation to creators.

Legal Gap

India’s Copyright Act, 1957:

• Protects reproduction rights under Section 14
• Provides fair dealing exceptions under Section 52
• Does not explicitly recognise Text & Data Mining (TDM) for commercial AI training

This creates ambiguity on whether large-scale AI training constitutes copyright infringement.

Key Concerns Identified

1. AI Training = Reproduction?

Training requires copying and transforming works, which may amount to reproduction under Section 14.

2. Fair Dealing Limitations: Fair dealing covers private research, criticism, reporting—not commercial AI model training.

3. No Compensation Mechanism: Creators (writers, artists, musicians, journalists) currently receive no royalties despite their works improving AI outputs.

4. Cultural & Economic Risk: Unregulated AI usage may dilute Indian cultural content and reduce long-term incentives for human creativity.

5. Unequal Bargaining Power: Large global AI firms benefit from Indian datasets, while individual creators lack negotiating capacity.

Need for a Balanced Framework

• Protect India’s growing creative economy
• Ensure predictable legal access to data for AI innovation
• Support the IndiaAI Mission and domestic startups
• Maintain cultural diversity and sustainability of creative professions
• Enable fair revenue-sharing between AI firms and Indian creators

Key Proposals of the Working Paper

1. Mandatory Blanket License: AI developers can train on lawfully accessible copyrighted works without individual permissions under a single national licence.

2. Statutory Royalty Payments: Creators will receive royalties linked to AI revenues, ensuring ongoing compensation.

3. Creation of a Collective Management Body: A proposed Copyright Royalties Collective for AI Training (CRCAT) would:

• Collect licence fees
• Distribute royalties to rights holders
• Represent both members and non-members

4. Royalty Rate-Setting Committee: A government-appointed body will determine fair royalty rates with periodic review and oversight.

5. Single-Window Digital Licensing: A simplified compliance system to:

• Reduce legal burden
• Enable startups and MSMEs to innovate
• Provide nationwide validity through one licence → one payment

Why This Model is Significant

• First attempt to align copyright law with AI-era realities
• Seeks to avoid litigation-heavy, opt-out frameworks
• Balances innovation, cultural preservation, and economic justice
• Positions India as a policy pioneer in AI governance

Why is it in the News?

Predictive AI is revolutionizing data analysis, decision-making, and industry leadership, offering businesses unprecedented insights and strategic advantages.

What is Predictive Artificial Intelligence (AI)?

• Predictive artificial intelligence (AI) utilizes machine learning techniques to analyze historical data and forecast future events, distinguishing it from traditional AI focused solely on retrospective analysis.
• This cutting-edge technology employs advanced algorithms and machine learning models to sift through extensive datasets, identifying subtle patterns and trends.
• Unlike conventional approaches, Predictive AI doesn't just analyze data; it transforms it into actionable insights, enabling organizations to:
  • Anticipate future outcomes,
  • Predict market shifts, and
  • Make strategic decisions with unprecedented foresight.
• By continuously learning from past data and adapting to changing trends, Predictive AI becomes an invaluable tool, guiding businesses through uncertain landscapes.

How Predictive AI Work?

• Leveraging Big Data: Predictive AI relies on access to extensive datasets, often referred to as "big data," as larger datasets typically lead to more accurate analyses.
• Utilizing Machine Learning (ML): As a subset of AI, ML involves training computer programs to analyze data autonomously, without human intervention.
  • In the realm of predictive AI, ML algorithms are applied to vast datasets to extract valuable insights.
• Autonomous Processing: Predictive AI models are capable of autonomously processing massive datasets, eliminating the need for human oversight.
• Pattern Recognition: Through ML techniques, predictive AI learns to recognize patterns within datasets, associating specific data points or occurrences with potential future events.
  • By examining numerous factors, predictive AI can identify intricate patterns indicative of recurring events, enabling organizations to anticipate future outcomes effectively.

Difference Between Predictive AI and Generative AI:

• Predictive AI and generative AI both employ machine learning techniques and leverage extensive datasets to generate their outputs.
• However, while predictive AI utilizes machine learning to forecast future outcomes, generative AI employs machine learning to produce original content.
• For instance, a predictive AI model may inform fishermen about impending storms, whereas a generative AI model may craft a fictional narrative depicting various scenarios involving weather and fishing expeditions.
• While both types of AI rely on statistical analysis to discern patterns, their objectives, machine learning methodologies, and applications differ significantly.

Various Applications of Predictive AI:

• Assessing the Impact of Natural Disasters: With the recent eruption of a volcano in Iceland, the potential repercussions on air travel echo concerns from a similar event in 2010, which disrupted flights across Europe.
  • Predictive AI leverages data analysis to identify patterns and anticipate the impact of such extreme weather events on air travel. Platforms like Yandex offer interactive maps for real-time monitoring of ash clouds post-eruption.
• Enhancing Oil and Gas Exploration: In the realm of oil and gas exploration, companies possess extensive historical geological data that can inform predictive AI systems.
  • By analyzing past drilling successes, these systems can predict optimal locations for new oil wells.
    • For instance, Saudi Aramco utilizes its meta-brain generative AI to optimize drilling plans, analyze geological data, and forecast drilling outcomes accurately.
• Inventory and Supply chain management: Predictive AI aids in inventory and supply chain management by identifying peak consumer demand periods, facilitating proactive stock adjustments, and optimizing resource allocation to address fluctuations in road congestion and meet increased user demands.
• Marketing campaigns: Just as predictive AI can anticipate user or customer behavior, it can help prognosticate what kinds of content or products prospective customers may be interested in.
• Advancing Medical Research: Predictive AI plays a pivotal role in drug discovery, a cornerstone of contemporary medical research.
  • Pharmaceutical companies are increasingly collaborating to leverage predictive AI models for analyzing vast datasets and identifying potential drug candidates. Initiatives like the 'MELLODDY Project', supported by the EU Innovative
  • Medicines Initiative and multiple pharmaceutical firms, exemplify this collaborative effort in pooling data and leveraging predictive AI for drug discovery.