AI and Digital Rights

At first glance, the Studio Ghibli style AI-generated art seems harmless. You upload a photo, the model processes it, and you get a stunning, anime-style transformation. But there's something far more complex beneath the surface—a quiet trade-off of identity, privacy, and control. Today, we casually give away fragments of ourselves: - Our faces to AI art apps - Our health data to wearables - Even our genetic blueprints to direct-to-consumer biotech services All in exchange for a few minutes of novelty or convenience. And while frameworks like India’s Digital Personal Data Protection Act (DPDPA) attempt to address this through “consent,” we must ask: What does consent even mean in an era of opaque AI systems designed to extract value far beyond that initial interaction? Because it’s not about the one image you uploaded. It’s about the aggregated behavioral and biometric insights these platforms derive from millions of us. That data trains models that can infer, profile, and yes—discriminate. Not just individually, but at community and population levels. This is no longer just a personal privacy issue. This is about digital sovereignty. Are we unintentionally allowing global AI systems to construct intimate, predictive bio-digital profiles of Indian citizens—only for that value to flow outward? And this isn’t just India’s challenge. Globally, these concerns resonate, creating complex challenges for cross-border data flows and requiring companies to navigate a patchwork of regulations like GDPR. The real risk isn’t that your selfie becomes a meme. It’s that your data contributes to shaping algorithms that may eventually determine what insurance you're offered, which job you’re filtered out of, or how your community is policed or advertised to, all without your knowledge or say. We need to go beyond checkbox consent. We need: 🔐 Privacy-by-design in every product 🛡️ Stronger enforcement of rights across borders 🧠 Collective awareness about how predictive analytics can influence entire societies Let’s be clear that innovation is critical. But if we don’t anchor it within ethics, rights, and sovereignty, we risk building tools that define and disadvantage us, rather than empower us. #Cybersecurity #PrivacyMatters #AIethics #DPDPA #DigitalSovereignty #DataProtection #AIresponsibility #IndiaTech

I'm increasingly convinced that we need to treat "AI privacy" as a distinct field within privacy, separate from but closely related to "data privacy". Just as the digital age required the evolution of data protection laws, AI introduces new risks that challenge existing frameworks, forcing us to rethink how personal data is ingested and embedded into AI systems. Key issues include: 🔹 Mass-scale ingestion – AI models are often trained on huge datasets scraped from online sources, including publicly available and proprietary information, without individuals' consent. 🔹 Personal data embedding – Unlike traditional databases, AI models compress, encode, and entrench personal data within their training, blurring the lines between the data and the model. 🔹 Data exfiltration & exposure – AI models can inadvertently retain and expose sensitive personal data through overfitting, prompt injection attacks, or adversarial exploits. 🔹 Superinference – AI uncovers hidden patterns and makes powerful predictions about our preferences, behaviours, emotions, and opinions, often revealing insights that we ourselves may not even be aware of. 🔹 AI impersonation – Deepfake and generative AI technologies enable identity fraud, social engineering attacks, and unauthorized use of biometric data. 🔹 Autonomy & control – AI may be used to make or influence critical decisions in domains such as hiring, lending, and healthcare, raising fundamental concerns about autonomy and contestability. 🔹 Bias & fairness – AI can amplify biases present in training data, leading to discriminatory outcomes in areas such as employment, financial services, and law enforcement. To date, privacy discussions have focused on data - how it's collected, used, and stored. But AI challenges this paradigm. Data is no longer static. It is abstracted, transformed, and embedded into models in ways that challenge conventional privacy protections. If "AI privacy" is about more than just the data, should privacy rights extend beyond inputs and outputs to the models themselves? If a model learns from us, should we have rights over it? #AI #AIPrivacy #Dataprivacy #Dataprotection #AIrights #Digitalrights

“Democratization” of creativity? Or just another Big Tech takeover? Generative AI is set to cannibalize up to 24% of musicians' and 21% of audiovisual creators' revenues by 2028, according to CISAC’s latest report. Meanwhile, AI companies’ profits are expected to skyrocket—€64 billion by 2028, fueled by unlicensed training data. This isn’t just about numbers; it’s about fairness. A recent ACLS survey shows 91% of creators believe they should be asked for permission before their works are used in AI training, and 92% demand compensation. Alarmingly, only 8% even knew their works had been used. And what about copyright? The 2nd Circuit’s Hachette v. IA (Internet Archive) decision nails the issue: “If authors knew their original works could be copied and disseminated for free, there would be little motivation to produce new works.” In my latest article, I explore the growing evidence of how AI impacts creators—from cannibalized revenues to unlicensed training—and why licensing, consent, and fair compensation are crucial for protecting creativity. #ArtificialIntelligence #GenerativeAI #CreatorsRights #CreativeEconomy #DigitalRights #CopyrightInfringement #ProtectCreators #InnovationOrExploitation #FairUse

Europe made history in 2024. India should pay attention. The European Union passed the AI Act – the world’s first comprehensive law to regulate Artificial Intelligence. Approved by the European Parliament in March 2024 and taking effect in August 2024, this landmark legislation is already being referred to as the “GDPR of AI” 【EU Parliament, 2024】. I was speaking with a few media houses and was surprised to learn that they don't have an AI policy at the institutional level yet. Why does this matter outside Europe? Because just like GDPR reshaped global data practices, the AI Act is set to influence how AI is built and deployed worldwide – including in India. What does the EU AI Act do? 1. Transparency first → Chatbots must disclose they’re bots. No pretending to be human. 2. Labels on AI content → Deepfakes, AI images/videos must carry clear disclaimers or watermarks. 3. Bans on misuse → No “social scoring,” no exploiting vulnerabilities (e.g., AI toys nudging kids into harm). 4. Strict oversight for high-risk AI → Systems that decide loans, diagnose X-rays, or shortlist CVs must undergo fairness, bias, and accuracy checks with human oversight. This risk-based framework (unacceptable, high, limited, minimal risk) balances innovation with protection. And India? Unlike the EU, India doesn’t yet have an AI-specific law. But several steps have been taken: ✅ National Strategy for AI (2018) ✅Principles for Responsible AI (2021) ✅Digital Personal Data Protection Act (2023) ✅Advisories on AI labelling and consent by MeitY ✅The launch of INDIAai, a national AI portal (2024)* Still, our frameworks remain fragmented. With AI increasingly shaping governance, education, health, and financial systems, India needs a clear, comprehensive regulatory path. The EU’s AI Act shows that regulation is not about slowing innovation – it’s about building trust. For a diverse and fast-scaling country like India, a rights-first, innovation-friendly approach isn’t optional; it’s urgent. What do you think: Should India borrow from the EU’s framework, or design its own model rooted in our unique realities? *link in comment. #ArtificialIntelligence #EUAIAct #India #DigitalIndia #30dayWritingChallenge #AI #AiwithAdira

Recently, a verdict from the Changsha Intermediate Court in China has underscored the challenges posed by AI-driven content creation and the limits of licensing agreements.   In this high-profile case, two tech giants—Baidu and Tencent—found themselves in a dispute over the use of copyrighted material. Baidu’s “Du Jia” AI video generation software, integrated with its self-media platform “Baijiahao,” allowed users to search, select, edit, and repurpose video clips using AI algorithms. Tencent, which held the rights to a popular television series, had licensed Baidu to display the series under strict conditions, prohibiting any secondary creative processes like editing or modifying the content.   However, Tencent presented evidence showing that users could consistently generate short video clips (3-7 seconds) on the Du Jia platform in response to specific text inputs. These clips, stored and readily accessible on Baidu’s servers, were deemed to infringe on Tencent’s “network dissemination rights” under China’s Copyright Law. The court ruled that Baidu’s actions exceeded the licensing agreement, constituting unauthorized secondary creation and copyright infringement.   This case is a pivotal reminder of the importance of clearly defined rights and restrictions in licensing agreements, especially as AI technologies continue to evolve. It also highlights the complexities of AI-generated content and the critical need to safeguard intellectual property rights in the digital era.   For tech companies and content creators alike, this ruling is a stark reminder that while AI-driven innovation unlocks new possibilities, it must always be balanced with strict adherence to established IP laws and regulations.   #IntellectualProperty #AICopyright #LegalTech #InnovationLaw #AI #ContentCreation #DigitalTransformation #ChinaIP #COPYRIGHT

Why the U.S. Needs Unified AI Regulation And What Principles Should Guide It As artificial intelligence transforms every sector of our economy, the need for robust, harmonized regulation across the United States has never been clearer. While some regulation is essential to ensure safety, fairness, and trust, a fragmented, state-by-state approach risks stifling innovation and creating a complex compliance landscape that is especially challenging for startups and global enterprises alike. This moment is both a concern and an opportunity a chance to unify around core principles that will shape the future of AI in America. Key Principles for U.S. AI Regulation • Data Privacy and Sovereignty Individuals must retain control over their personal data. Regulations like the California Consumer Privacy Act (CCPA) set important precedents, but a unified federal standard is needed to ensure consistent protection and clarity for businesses and consumers nationwide. • Content Lineage and Fair Use Rights Transparency around the origins of AI-generated content is critical. Clear guidelines on data lineage knowing where training data comes from and how it’s used are essential for trust and compliance. The U.S. Copyright Office’s evolving stance on fair use in AI training underscores the need for a balanced approach. • Algorithmic Fairness and Non-Discrimination AI systems must be designed and tested to prevent bias and discrimination. The Blueprint for an AI Bill of Rights and recent FTC enforcement actions highlight the importance of protecting individuals from algorithmic harms, especially in sensitive areas like hiring, lending, and healthcare. • Transparency and Explainability Users deserve to understand how automated decisions are made. National known working rules should be standardized to ensure that AI systems are not “black boxes” but are accountable and understandable. • Safety, Security, and Human Oversight AI must be safe and reliable, with rigorous pre-deployment testing and ongoing monitoring. Systems should include human-in-the-loop options and fallback mechanisms, particularly where critical rights or safety are at stake. • Accountability and Governance Companies developing and deploying AI should be responsible for the impacts of their technologies. This includes clear liability frameworks, independent audits, and mechanisms for redress. • Innovation and Global Competitiveness Regulation should foster, not hinder, responsible innovation. The Path Forward The current patchwork of local, state, and federal initiatives is a testament to the urgency and complexity of AI governance. But it also highlights the need for a coordinated national approach one that draws on the best of public and private sector expertise, aligns with global standards, and is adaptable as technology evolves. https://lnkd.in/gnQ2wfUz