[AI Sharing-I] AI sharing, not data sharing
[AI Sharing-I] AI sharing, not data sharing
  • By Prof. Kyoung Jun Lee (klee@khu.ac.kr)
  • 승인 2022.07.02 03:36
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Kyoung Jun Lee, Professor of Kyung Hee University
Kyoung Jun Lee, Professor of Kyung Hee University

With the keywords of participation, sharing, and openness, Web 2.0 has developed into a sharing economy business model represented by Uber and Airbnb. Uber, which provides taxi services by sharing its cars with others, does not make a single car, but achieved a corporate value of USD 60 – 66 billion, like the world's top automakers such as GM, Mercedes-Benz, and Audi. Airbnb also has not built a hotel but achieved a similar corporate value to the combined market capitalization of the three major hotel chains, Hilton, Marriott, and Hyatt.

What about the data-sharing business? Will the data-sharing economy business model succeed? In the meantime, there have been many slogans such as "Let's disclose data!" "Let's share data!" and "Let's create a data dam (by collecting such disclosed and shared data)." Can the data be shared, disclosed, and collected well in the dam?

Data is not only raw material for AI but also a key raw material for the era of the 4th Industrial Revolution. This valuable data is an asset to individuals and companies alike. That's why neither individuals nor companies want to disclose or share their data. Individuals are also required to protect privacy and it is difficult for companies to legally share their customer data, and there is also no reason to do so business-wise.

So, both data sharing and data disclosure only exist as a slogan but not in reality. To facilitate data sharing and disclosure, the Korean government revised the Data 3 Act and made it possible to use de-identified data by pseudonym processing without the consent of the data subject. Banks tried to improve the performance of AI services provided to customers by sharing data with credit rating agencies and establishing a platform that can store and utilize user data from many medical institutions. The last government attempted to build big data platforms in 16 fields.

These attempts are clearly worth seeing, but they are not fundamental. There are still concerns about the leakage of personal information and privacy infringement in de-identified data.

Attempts to share data have continued not only in the government but also in the private sector. Samsung Group launched Monimo this year, which shares data between insurance, stock, and credit card companies, but it keeps having a difficulty in data sharing such as continuous consumer verification. Finally, four days after Monimo's launch, a customer information leak occurred. Fintech company Toss recently caused a stir when it was found that they illegally sold customer information to insurance planners for around USD 60 per case.

These kinds of incidents may cause data-sharing activities between individuals and companies to become passive and shrink. In the end, disclosing and sharing one's assets or data, whether it is an individual or a company, will only succeed when it is beneficial. Even if people and companies are forced to share data, they will only share low-quality data because they tend to pursue their own interests.

As this situation continues, only large corporations or big tech companies that already have a lot of data and high-end AI manpower can make a good AI. These large corporations and big tech platforms will have increasingly stronger AI, and this will make them more powerful economic players causing the problem of polarization.

So, what is the alternative?

To solve this problem, AI-Sharing is an alternative. With AI-Sharing, while data is owned and maintained by each subject, AI is shared between subjects to increase performance and lower costs. In this way, individual businesses, small business owners, and SMEs can continue to expand their customer access points while owning data and strengthening AI.

AI-Sharing Platform

The AI-Sharing platform helps to pursue synergy and management efficiency by helping economic entities (firms, governments, and health organizations, etc.) by sharing an AI model, the learned results, and its various derived services. Allowing a reliable AI-sharing platform to share AI rather than data between economic entities will reduce AI development costs and increase the performance of the shared AI.

Professor Lee from Kyung Hee University (School of Business & Big Data Applications) 

Professor Kyoung Jun Lee graduated from KAIST's Management Science with an undergraduate, Master’s, and Doctor’s degree, and graduate from Graduate School of Public Administration, Seoul national university with Mater’s and Doctor’s degree. He also studied at Carnegie Mellon, MIT, and UC Berkeley. In 1995, he was the first Korean to win the Innovative Artificial Intelligence 
Application Award by the American Society of Artificial Intelligence (AAAI) and also won the 1997 and 2020 (best awards). He published three research papers in AAAI's quarterly journal AI Magazine. He is currently focusing on research of User-Centric AI with Harex InfoTech. He served as the chairman of the Korea Intelligent Information System Association (2017) and is currently serving as the vice-chairman of the Leading Future Agendas of Business & Society and the senior vice president of the Korea Society of Management Information Systems. He selected 25 Korean AI startups in 2020 and 100 Korean AI startups in 2021. In 2018, he was awarded the Presidential Citation for e-government merit and is currently a general member of the National Academy of Engineering of Korea

Original Source: Reproduced from the Korean version of the IT Chosun on June 20, 2022


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