The meteoric ascent of generative artificial intelligence has created a technology sensation thanks to user-focused products such as OpenAI’s ChatGPT, Dall-E, and Lensa. However, with the boom in user-friendly AI, privacy risks imposed by these projects seem to be ignored or not widely known. International governments and major tech figures, like Elon Musk, are starting to sound the alarm, citing privacy and security concerns.
Italy recently placed a temporary ban on ChatGPT, potentially inspiring a similar block in Germany. Meanwhile, hundreds of AI researchers and tech leaders signed an open letter urging a six-month moratorium on AI development beyond the scope of GPT-4. Despite commendable efforts to try to rein in irresponsible AI development, the wider landscape of threats AI poses to data privacy and security goes beyond one model or developer.
The issue of data privacy in AI had been broached even before products like ChatGPT entered the mainstream, with scandals happening mostly out of the public eye. For example, Clearview AI, an AI-based facial recognition firm used by governments and law enforcement agencies, faced legal issues for its illegal facial recognition database. There’s a threat that consumer-focused visual AI projects could be used for similar nefarious purposes.
Deepfake scandals involving pornography and fake news created through consumer-level AI products have only heightened the urgency to protect users from harmful AI usage. AI models fundamentally rely on new and existing data to build and strengthen their capabilities. Part of their data input includes the personal data of the people using these AI systems, which can easily be misused if centralized entities or hackers get ahold of it.
As governments and developers raise alarm on AI’s effects, maintaining urgency without being alarmist is essential for creating effective regulations before it’s too late. Legislative action has been taken by entities like the EU, Brazil, and the Canadian government to sanction certain types of AI usage and development. But pros and cons come with hefty AI development strategies, such as federated machine learning, which boosts data privacy by using multiple independent sources to train the algorithm with their data sets.
For users, being more vigilant about which AI tools they access and use is essential. It’s crucial to keep in mind that when using a free AI product, personal data becomes the product. As generative AI development becomes more accessible and privacy concerns grow, it’s important to balance protection and progress, ensuring that user information and privacy remain at the forefront.
Source: Cointelegraph
