Personal Data as AI Fuel?

AIs like ChatGPT and some of its friends, Bard, Bing Chat, and Llama, are pretty similar LLMs. They are all capable of creating text in the form of conversational interactions with users. These LLMs are given materials to “learn” how to interact with users by developers. The developers give various media such as text, literature, news, and articles by humans so that the LLM is able to produce text according to the theme and subject matter requested by the user. Bing Chat is a LLM that has the ability to collect data and information through the internet in order to provide more accurate and credible answers.

With the development of artificial intelligence systems. The data used for training will be more extensive, depending on the field concerned. There are doubts about individuals’ personal data being used to train artificial intelligence systems, as it may raise privacy concerns. For example, AI systems can use personal data to detect fraud and other types of criminal activity, AI systems can analyze a person’s financial data to detect transactions that are considered unusual, indicating fraud. Another example is the use of facial recognition systems, which can use personal data in the form of digital photos available through social media, websites, driver’s license registrations, surveillance cameras, and many other sources to recognize individual people.

In some countries, such as Singapore, there are draft guidelines on how personal data should be managed when used to train artificial intelligence models and systems. This document outlines how the country’s Personal Data Protection Act (PDPA) will apply when companies use personal information to develop and train their AI systems. The guidelines also cover best practices in building transparency into how AI systems use personal data to make decisions, forecasts, and recommendations.

The government’s role in regulating personal data used for AI training is crucial, as this can affect the quality, fairness, transparency and accountability of AI systems. Personal data is often used to train AI models that can make decisions or provide recommendations that have a significant impact on people’s lives, such as health, education, employment, finance, and justice. Therefore, it is imperative that personal data is processed in a lawful, ethical and responsible manner, and that data subjects are informed and protected from potential harm or risks.

Each country has different laws and regulations regarding personal data protection. For example, the European Union has adopted the General Data Protection Regulation (GDPR), which sets high standards for data protection and grants data subjects various rights, such as the right to access, rectify, erase, restrict, object to, and transfer their personal data. The GDPR also requires institutions that use personal data to train AI to provide clear and transparent information about how the processing is done, the logic underlying the processing, and the rights that data subjects have.

The government’s role in regulating personal data used for AI training is not only to protect the privacy and dignity of data subjects, but also to foster innovation and competitiveness in the AI sector. By establishing clear and consistent rules and standards for data processing and AI development, governments can create a level playing field for different actors and encourage trust and collaboration among them. Governments can also support research and education on AI ethics and social impacts, as well as increase public awareness and engagement on AI issues.

Regarding concerns about the ethics of using personal data in AI training, some companies, such as Nvidia, argue that synthetic data can make AI systems better and perhaps even more ethical. Synthetic data is data generated rather than collected. Gartner estimates that 60 percent of the data used to train AI systems will be synthetic data. Its use is debatable, however, as there are still questions about whether synthetic data can accurately reflect real-world data and prepare AI systems for real-world situations.

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  1. Спасибо, бро! кажется, пока мы боролись с Фейсбуком за прибыль от продажи наших данных рекламным компаниям, нас снова ограбили) Data is your personal natural resource.
    don’t care who take it from you. take it back. or fee)

      1. Если в Сингапуре, то такое возможно. Увы – воровство всегда имеет грязную ликвидность – пока воды не смешают нечистоты до неразличия


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