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PhD student to work on ANR project -- analyse privacy preserving recommender systems H/F

This offer is available in the following languages:
Français - Anglais

Date Limite Candidature : mercredi 29 septembre 2021

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General information

Reference : UMR5217-OANGOG-005
Workplace : GRENOBLE
Date of publication : Wednesday, September 8, 2021
Scientific Responsible name : Oana Goga
Type of Contract : PhD Student contract / Thesis offer
Contract Period : 36 months
Start date of the thesis : 1 December 2021
Proportion of work : Full time
Remuneration : 2 135,00 € gross monthly

Description of the thesis topic

Online advertising is becoming the de facto way businesses are attempting to reach customers. The appeal comes from the fact that online advertising platforms are gathering impressive amounts of data on users and are able to infer users' interests, behaviors and demographics at a fine-grain. Previous work (including ours) showed that Facebook provides advertisers with more than 250k AI-inferred attributes to micro-target consumers such as “anti-abortion movements” and “cancer awareness” and, on top, Facebook employs AI-based targeting optimization algorithms to deliver ads to the most relevant audience. While detailed targeting criteria and targeting optimizations are creating opportunities for business to reach interested parties, it also opens the way for businesses to use user's personal data to deceive and manipulate them. The risks triggered by such technology are being recognized by European Institutions, and many fear a weaponization of the technology to engineer polarization, promote voter disengagement and manipulate citizens.
The goal of the PhD project is to rigorously assess risks incurred by targeted online advertising by understanding: (1) how AI-based targeting can influence consumption trajectories and beliefs, and in which conditions; and (2) how AI-based targeting optimizations can lead to (intentional or unintentional) exploitation of people's vulnerabilities instead of serving their interests. To answer these questions we need to both understand the capabilities of AI-based targeting platforms as well as understanding how people decide and form beliefs thanks to the information received on these platforms. The PhD project will develop a measurement methodology that builds on behavioral and experimental economics methods to design robust randomized controlled experiments on how targeted online ads can exploit consumer's cognitive biases and that is based on computer science large-scale measurement methodologies of real- wold systems and previous technology we developed at LIG (AdAnalyst). Hence, this PhD project faces two challenges a methodological one with the measurement of consumer preferences and belief formations on online platform, and scientific one with the risk assessment of these micro- targeting platform.
The methodology is based on recruiting a large sample of social media users who will contribute to the project: (1) by agreeing to donate data about the content they see on their social media feeds by installing a monitoring tool we provide; (2) by answering multiple surveys along the observation period; and (3) by agreeing to take part in randomized controlled trials to assess information targeting.

Work Context

The student will work on an ANR project.

The student will work at LIG under the supervision of Oana Goga.

Constraints and risks


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