Measuring and Mitigating Risks of AI-driven Information Targeting

ERC (European Research Council)HORIZON-ERCID: 101041223
EC Contribution
€15,000
Consortium Size
2 orgs
Start Year
2022
Summary

We are witnessing a massive shift in the way people consume information. In the past, people had an active role in selecting the news they read. More recently, the information started to appear on people's social media feeds as a byproduct of one's social relations. At present, we see a new shift brought by the emergence of online advertising platforms where third parties can pay ad platforms to show specific information to particular groups of people through paid targeted ads. These targeting technologies are powered by AI-driven algorithms. Using these technologies to promote information, rather than promote products as they have been initially designed for, opens the way for self-interested groups to use user's personal data to manipulate them. European Institutions recognize the risks, and many fear a weaponization of the technology to engineer polarization or manipulate voters.The goal of this project is to study the risks with AI-driven information targeting at three levels: (1) human-level--in which conditions targeted information can influence an individual's beliefs; (2) algorithmic- level--in which conditions AI-driven targeting algorithms can exploit people's vulnerabilities; and (3) platform-level--are targeting technologies leading to biases in the quality of information different groups of people receive and assimilate. Then, we will use this understanding to propose protection mechanisms for platforms, regulators, and users.This proposal's key asset is the novel measurement methodology I propose that will allow for a rigorous and realistic evaluation of risks by enabling randomized controlled trials in social media. The measurement methodology builds on advances in multiple disciplines and takes advantage of our recent breakthrough in designing independent auditing systems for social media advertising. Successful execution will provide a solid foundation for sustainable targeting technologies that ensure healthy information targeting.

Consortium (2)

Project Results (7)

Source: CORDIS, the EU research results database.

Publications (6)
Client-side and Server-side Tracking on Meta: Effectiveness and Accuracy
Proceedings on Privacy Enhancing Technologies· 2024DOI
Asmaa El fraihi, Nardjes Amieur, Walter Rudametkin, Oana Goga
FactCheckBureau: Build Your Own Fact-Check Analysis Pipeline
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management· 2024DOI
Oana Balalau, Pablo Bertaud-Velten, Younes El Fraihi, Garima Gaur, Oana Goga, Samuel Guimaraes, Ioana Manolescu, Brahim Saadi
Marketing to Children Through Online Targeted Advertising: Targeting Mechanisms and Legal Aspects
Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security· 2024DOI
Tinhinane Medjkoune, Oana Goga, Juliette Senechal
On Detecting Policy-Related Political Ads: An Exploratory Analysis of Meta Ads in 2022 French Election
Proceedings of the ACM Web Conference 2023· 2024DOI
Vera Sosnovik, Romaissa Kessi, Maximin Coavoux, Oana Goga
Understanding the Privacy Risks of Popular Search Engine Advertising Systems
Proceedings of the 2023 ACM on Internet Measurement Conference· 2024DOI
Salim Chouaki, Oana Goga, Hamed Haddadi, Peter Snyder
What News Do People Get on Social Media? Analyzing Exposure and Consumption of News through Data Donations
Proceedings of the ACM Web Conference 2024· 2024DOI
Salim Chouaki, Abhijnan Chakraborty, Oana Goga, Savvas Zannettou
Other Results (1)
Periodic Reporting for period 1 - MOMENTOUS (Measuring and Mitigating Risks of AI-driven Information Targeting)