Program

morning (9.30-12.30)

David Lazer: Foundations of Computational Social Science

afternoon (14.30-17.30)

Dino Pedreschi: Social Artificial Intelligence

morning (9.30-12.30)

Fosca Giannotti: Human-centered Artificial Intelligence

afternoon (14.30-17.30)

Alexandra Olteanu – Fairness, Accountability, Transparency and Ethics

morning (9.30-12.30)

Filippo Menczer – Computational social science methods to study online virality and its manipulation

afternoon (14.30-17.30)

Dirk Hovy: Understanding society through text – computational linguistics

morning (9.30-12.30)

Short talks by students

afternoon (14.30-17.30)

evening (20.00)

morning (9.30-12.30)

Alessandro Vespignani – Computational social science for epidemics

afternoon (14.30-17.30)

Laszlo Barabasi – Science of Science

 

Dirk Hovy
Title: Understanding society through text – computational linguistics

Abstract: Text is an important component in today’s information-rich society, and provides signal for a variety of social studies. Participants will learn about text representations and how to use and interpret modern embedding models effectively. The course will also cover text classification techniques and clustering, which are critical for understanding complex patterns in language data. We will finally cover geocoding, which is a method for incorporating geographical context into text-based analysis. As running example, we will retrace the main components of the paper “Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting”, both in theory and practice (via Jupyter notebooks).

This workshop provides a comprehensive practical guide for social scientists interested in using Python to dissect and comprehend text data.

 

Filippo Menczer
Title:Computational social science methods to study online virality and its manipulation

Abstract: As social media have become major channels for the diffusion of news and information, it is critical to understand how the complex interplay between cognitive, social, and algorithmic biases triggered by our reliance on online social networks makes us vulnerable to manipulation and disinformation. We focus on two key factors that contribute to online virality: the structure of the social network and the engagement mechanisms that manage our limited attention. This lecture overviews computational social science efforts (spanning network analytics, modeling, and machine learning) to study the viral spread of misinformation and to develop tools for countering the online manipulation of opinions.

 

Dino Pedreschi
Title: Social Artificial Intelligence: Challenges of the Human-AI Ecosystem

Abstract: The rise of large-scale socio-technical systems in which humans interact with AI systems (including assistants and recommenders) multiplies the opportunity for the emergence of collective phenomena and tipping points, with unexpected, possibly unintended, consequences. This is apparent even in such simple everyday applications as navigation systems where suggestions may create chaos if too many drivers are directed on the same route. Similarly personalised recommendations on social media may amplify polarisation, filter bubbles, and radicalisation. On the other hand, we may learn how to foster “wisdom of crowds” and collective action effects to face social and environmental challenges. In order to understand the impact of AI on socio-technical systems and design next-generation AIs that team with humans to help overcome societal problems rather than exacerbate them, we propose to build the foundations of Social AI at the intersection of Complex Systems, Network Science and AI, and discuss the open challenges of human-AI ecosystems such as social media, car navigation systems, and generative AI platforms, outlining possible research avenues.

 

Fosca Giannotti
Titolo: Human-centered Artificial Intelligence.

Abstract: The future of AI lies in enabling people to collaborate with machines to solve complex problems. Like any efficient collaboration, this requires good communication, trust, clarity, and understanding. Explaining to humans how AI reasons is only a part of the problem: we also must be able to design AI systems that understand and collaborate with humans. Hybrid decision-making systems aim at leveraging the strengths of both human and machine agents to overcome the limitations that arise when either agent operates in isolation.

The lecture will highlight the steps needed for promoting human-AI collaboration and seamless interaction maintaining the human responsibility of choice through a progressive disclosure to prevent cognitive overload. Three distinct paradigms, characterized by a different degree of human agency and machine autonomy will be discussed: i) human oversight, with a human expert monitoring AI prediction augmented by explanation; ii) Learning to defer, in which the machine learning model is given the possibility to abstain from making a prediction when it receives an instance where the risk of making a misprediction is too high; iii) collaborative and interactive learning, in which human and AI engage in communication to integrate their distinct knowledge and facilitate the human ability to make informed decisions.