"I think if this technology goes wrong, it can go quite wrong." - Sam Altman, CEO, OpenAI
Rise of accessible deepfakes
AI has moved from a niche tool to a widespread threat. Highly realistic fake content makes it increasingly difficult for individuals to separate truth from fiction, leading to a surge in the creation of nonconsensual imagery involving women. This is on the verge of a social crisis now.
Crackdowns on AI platforms
The Indian government recently issued a warning to X regarding Grok, its generative AI chatbot, over concerns about obscene content. This move signals a shift toward stricter oversight, requiring platforms to overhaul their technical and governance safeguards to prevent further misuse.
Check our posts on Deepfakes; click here
Technical challenges in AI safety
Establishing safety remains difficult as users find ways to bypass restrictions using jailbreak prompts. Because ai models rely on complex neural pathways, developers struggle to block specific harmful content without damaging the mathematical coherence and the overall output quality of the system.
Evolving legal liability frameworks
Legal experts argue that platforms can no longer avoid liability for harms caused by their tools. Under current laws like the Information Technology Act, intermediaries may lose their safe harbour immunity if they do not act quickly to remove illegal or sexually explicit imagery.
Implementing mandatory safety guardrails
Future industry standards may mandate features like traceable watermarking and consent first image processing. These guardrails must be enforceable rather than optional to ensure proper accountability for platforms and to protect victims from increasingly sophisticated and harmful digital threats.
Summary
The mainstreaming of ai deepfakes has exposed significant gaps in current regulatory and technical safety measures. As governments increase pressure on platforms like X, the focus shifts toward shared liability and mandatory features like watermarking. Protecting users requires a combination of robust legal frameworks and technical barriers.
Food for thought
If AI models learn by identifying patterns across all data, can we ever truly delete their ability to generate harm without breaking the intelligence itself?
Check our posts on Deepfakes; click here
AI concept to learn: Reinforcement learning from human feedback (RLHF)
This is a training method where humans rank model responses to help the AI learn which outputs are safe. It acts as a safety layer by teaching the system to suppress harmful behaviors rather than forgetting them. This process aligns models with human values while maintaining performance.
[The Billion Hopes Research Team shares the latest AI updates for learning and awareness. Various sources are used. All copyrights acknowledged. This is not a professional, financial, personal or medical advice. Please consult domain experts before making decisions. Feedback welcome!]
