Data-driven inferencing is a fundamental technology that supports an AI society. The level of technology has far surpassed what the industrial world and the general public expected at the time of the second AI boom, and AI technology is driving major changes in a wide range of industries and in everyday life. As mentioned earlier, while big data is essential for statistical machine learning, the tendency of US-based IT mega-corporations to use big data to monopolize wealth has come under strict scrutiny, because it's often individuals who generate big data, as is the case in e-commerce and social media. Perhaps, this is a situation that was bound to happen.
IT mega-corporations have internalized the algorithms that make information services possible, and are updating them on a per-millisecond basis with big data. Indeed, these corporations possess social simulation technology, are capable of observing society through big data, and are constantly changing their simulation models. As described above, in social simulations, the process of obtaining data and the measures to reflect it in model modification are important. From this perspective, demands were made to the US-based IT mega-corporations, mainly by European countries, to place strict regulations on big data collection processes. This is how the GDPR framework was established.
The direct involvement of human behavior in mathematical models also provides insight into future trends in the algorithms and information services of IT mega-corporations as social simulations. Society has many restrictions, including laws and social norms, as well as restrictions on a range of actions rooted in human ethics. Differences in values between countries with varying religions and cultures also lead to differences in human behavior. As long as algorithms play a role as social simulations, social changes and behavioral changes in players should be reflected in the algorithms. Now that AI technology is deeply ingrained in our social lives, we must create a human-centric AI society, not a technology-driven one. In order to achieve this, there's a strong need to visualize the needs and concerns of society and reflect them in actual AI technology development (especially in algorithms), and in relevant legal systems.
Professor, Faculty of Science and Engineering
Director of the AI and Data Science Center