Introduction

Recently, concerns over Big Tech's dominance in AI have grown, with U.S. courts examining Google's monopoly in search, including the use of AI. At the same time, two authors sued Apple in federal court, accusing it of using their books without permission to train its "OpenELM" AI model by adding them to a pirated dataset without consent, credit, or payment.

These cases reflect a growing trend of legal challenges against AI firms over the use of copyrighted content in training Generative AI LLMs and creating a digital monopoly on the internet.

Key Stakeholders and their Interests 

What are ethical concerns regarding operations of AI LLMs?

• Digital monopoly and unfair competition: Allegations against dominant AI firms (Google, Microsoft, OpenAI) for anti-competitive practices such as control over data, algorithms, and markets, suppresses smaller players and violate principles of fairness, equal opportunity, and distributive justice.
  • E.g. the dominance of Google as the default search engine in phones creates a digital monopoly for the company.
• Copyright and Intellectual Property Rights (IPR) Infringement: Issues like Breach of respect for authorship, fairness in compensation have emerged.
  • It also violates the Kantian duty to treat creators as ends in themselves rather than as mere means of using their material without consent and fair compensation for training AI.
  • E.g. AI models scrape books, articles, music, art pieces, code, etc. without due permissions, impacting the revenue streams of small content creators.
• Privacy and sensitive information: If sensitive information, such as unpublished research, patient records, or business documents, is uploaded, there is a risk of breach of trust, lack of consent and violation of transparency norms.
  • E.g. South Korea suspended new downloads of the Chinese AI app DeepSeek after the company admitted it had not fully complied with the country's privacy rules.
• Privatization of Public Knowledge: Monopolistic firms turn freely available data into proprietary products.
  • E.g. Wikipedia data being used for AI training without acknowledgment.
• Corporate Profit vs. Ethics: Pursuit of market dominance at the expense of creators' rights and societal fairness highlights lack of moral integrity in corporate practices.
• North-South Divide: Global South creators' works are often used without safeguards, while Global North firms dominate profits. This deepens digital colonialism and economic inequality.

Way Ahead

• Strengthening Legal Frameworks: Enact a comprehensive AI law, aligned with global standards like the EU's AI Act, addressing consent, licensing, IPR, liability, and accountability in AI training and deployment.
• Promoting Fair Use and Compensation: Create a fair data licensing regime to ensure creators (writers, musicians, coders) are credited and compensated. 
• Ensuring Competition and Preventing Monopolies: 
  • Empower the Competition Commission of India to monitor AI markets for anti-competitive practices. 
  • Promote open-source AI models and public data commons to counter big tech monopolies. 
• Safeguarding Privacy and Sensitive Data: Mandate data anonymization, consent-based access, and independent audits for AI firms. Enforce strong penalties for privacy breaches and data misuse. 
• Democratizing Knowledge and Access: 
  • Protect public digital goods like open research databases. 
  • Promote AI literacy programs to empower citizens. 
• Balancing Corporate Profit with Ethical Responsibility: Establish Ethics Review Boards within AI firms, akin to medical ethics committees. 
• Global Treaty: 
  • Advocate for a global treaty on AI governance under the UN for equitable benefit-sharing. 
  • Foster South-South cooperation on AI research, open datasets, and digital infrastructure. 
•  Building Institutional Capacity in India: 
  • Create a National AI Regulation Authority to coordinate across sectors. 
  • Encourage public-private partnerships for ethical indigenous AI development.

Conclusion

Generative AI holds transformative potential but unchecked monopolies, IPR violations, and privacy risks threaten fairness and equity. A balanced approach combining strong regulation, ethical innovation, and global cooperation is essential to ensure AI becomes a tool for empowerment, not exploitation.