What’s the news?

• Generative artificial intelligence (AI) has emerged as a potent force in the digital landscape, raising critical questions about regulation, copyright, and potential risks.

Central Idea

• In a remarkably short period, chatbots such as ChatGPT, Bard, Claude, and Pi have demonstrated the remarkable potential of generative AI applications. However, these AI marvels have also exposed their vulnerabilities, prompting policymakers and scientists worldwide to grapple with the question, whether generative AI should be subject to regulation.

What is generative AI?

• Like other forms of artificial intelligence, generative AI learns how to take actions based on past data.
• It creates brand-new content—a text, an image, even computer code—based on that training instead of simply categorizing or identifying data like other AI.
• The most famous generative AI application is ChatGPT, a chatbot that Microsoft-backed OpenAI released late last year.
• The AI powering it is known as a large language model because it takes in a text prompt and, from that, writes a human-like response.

What is the legal framework on which generative AI rests?

• U.S. Copyright Approach:
  • In the United States, copyright law recognizes only humans as copyright holders.
  • Consequently, AI-generated works often fall outside the scope of copyright protection.
  • This situation poses challenges when it comes to attributing authorship to AI-generated content.
• India’s Ambiguity:
  • India’s position on AI-generated content and copyright remains ambiguous.
  • A recent case highlights this ambiguity, where a copyright application for an AI-generated work was initially rejected.
  • The lack of clear guidelines in India regarding copyright protection for AI-generated content adds complexity to the legal landscape.

The European Union’s AI Act

• Individual Rights: The EU AI Act places a strong emphasis on safeguarding individual rights within the AI landscape. It seeks to protect individuals from potential AI-related harm, ensuring that their rights are upheld.
• Leveling the Playing Field: Recognizing the dominance of large tech corporations in AI development, the Act aims to foster a more competitive environment. This involves measures to reduce the concentration of AI development within a select few companies, promoting innovation and diversity.
• Transparency Obligations: The AI Act introduces transparency requirements for AI-generated content. Specifically, it mandates the labeling of AI-generated material as such and requires summaries of the training data used. These provisions aim to enhance transparency and accountability in AI systems.

Contrasting Approaches: Risk-Based vs. Relaxed Regulation

• EU’s Risk-Based Approach:
  • In contrast, the European Union employs a risk-based approach to AI regulation.
  • This approach involves delineating prohibitions on certain AI practices, recommending ex-ante assessments for others, and enforcing transparency requirements for low-risk AI systems.
  • The EU’s approach acknowledges the multifaceted risks posed by AI and seeks to mitigate them effectively.
• U.S. Regulatory Approach:
  • The United States maintains a relatively relaxed approach to AI regulation, which may be attributed to underestimating the associated risks or a general reluctance towards extensive regulation.
  • This approach raises concerns, especially in sectors like education, where there is minimal control over the use of generative AI tools by students, including age and content restrictions.
  • Additionally, discussions regarding the regulation of AI risks, particularly in the context of disinformation campaigns and deepfakes, are notably limited in the U.S.

AI Through an Indian Legal Lens

• Comprehensive Regulatory Framework: India necessitates a comprehensive regulatory framework that spans both horizontal regulations applicable across sectors and vertical regulations specific to distinct industries. The absence of such regulations results in uncertainties and impediments to effectively addressing AI-related issues.
• Data Protection Clarity: The Digital Personal Data Protection (DPDP) Act of 2023 plays a pivotal role in addressing data protection concerns. However, the DPDP Act exhibits certain gaps, such as legitimizing data scraping by AI companies when data is publicly available.

Challenges surrounding trade secrets and transparency in the context of AI

• Trade Secrets:

• Corporations frequently employ trade secrets to safeguard their AI models and training data from disclosure.
• Nevertheless, when AI systems have the potential to cause significant societal harm, there may arise a need to compel companies to divulge these particulars.
• This predicament raises questions about achieving a balance between safeguarding trade secrets and addressing the broader societal consequences of AI.

• Transparency:

• Guaranteeing transparency in AI systems holds paramount importance, particularly when AI-generated content is disseminated.
• The societal imperative for transparency, particularly in instances where AI-generated content might be exploited for malicious purposes or cause harm,

Way forward

• Continued Dialogue: Policymakers, legal experts, industry leaders, and stakeholders should engage in ongoing discussions and collaboration to develop effective regulations and guidelines for generative AI.

• Ethical Considerations: The development and deployment of AI systems should prioritize ethical principles to ensure responsible use and mitigate potential harms.
• Transparency and Accountability: There should be efforts to promote transparency in AI systems, especially when AI-generated content is involved. Accountability mechanisms should also be in place to address issues arising from AI use.
• Comprehensive Regulation: Governments and international bodies may consider developing comprehensive regulatory frameworks that encompass various aspects of AI, including data protection, transparency, accountability, and liability.
• Public Education: Initiatives to educate the public about AI’s implications, benefits, and limitations should be developed, particularly in sectors where AI is extensively used, such as education.

Conclusion

• The global regulation of generative AI emerges as a pressing concern. Adaptive and thoughtful regulatory approaches are essential to address the evolving challenges and opportunities introduced by generative AI on a global scale.

Also read:

AI generative models and the question of Ethics

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