Семинары

Human-Centered Data, Modeling and AI

Технические науки
Анализ данных, Имитационное моделирование, Искусственный интеллект, Математическое моделирование
Великобритания, Саутгемптон

21 июня–23 июня 2021

Форма участия: Очно-заочная

Срок подачи заявок: 20.04.2021

Организаторы: Online

emal: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]

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The Workshop

We have reached a truly historic turning point, human society is being reshaped faster and more profoundly by technology. Started from the birth of the World Wide Web, driven by the rapidly growing data and computing power, the most disruptive changes can be traced back to the recent development of Artificial Intelligence (AI). Like any powerful tool, AI promises the same level of risk and reward.

In the past 10 years, the development and use of AI is a decentralized global phenomenon with relatively low barriers to entry. We have seen that AI-generated content makes it more and more difficult to tell facts from the fiction; recent examples of machine learning deviations showed how our technology can easily magnify prejudice and inequality. Therefore, the frontier of AI cannot be just technology, it must also be humanistic.

Then how do we improve or adjust our web system to alleviate the existing issue, to help AI becoming more humanistic? Like, how do we preserving privacy when we design a new social network? How do we obtain unbiased sampling in temporal and spatial dimensions, or semantic dimensions? How do we establish anti-bias learning models to avoid Ecological Fallacy? Particularly, when social media are not only offering almost infinite data to related subjects but also influence both online and offline user behavior in a various way, itself should play a vital role on every aspect of human-centered data, modeling, and AI.

Topics for submissions include (but are not limited to) the following:

● Have a broader perspective on the Web and that combine analyses of Web data and other types of data (e.g., from surveys or interviews) to better understand user behaviour (i.e., online and offline)
● Interrogate questions of discrimination, representation, and fairness
● Ethical challenges of technologies, data, algorithms, platforms, and people in the Web
● Modeling Web-related structures, data, users and behaviours
● Impact of AI and machine learning on the development of Web Science
● Detecting, preventing and predicting anomalies in Web data (e.g., fake content, spam, algorithmic and data biases)
● Safeguarding and governance of the Web, including anonymity, security and trust
● Temporal and spatial dimensions of the Web as a repository of information
● Analysis and modeling of human vs. automatic behaviour (e.g., bots) and their influence on the structure of the Web and responding behaviour
● Health and well-being online
● Critical analyses of the Web and Web technologies
● Web economics, social entrepreneurship, and innovation
● Analysis of online social and information networks
● Health, politics, and education on the Web

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