Title:  A computational socio-linguistic exploration of the
        Ethics of AI landscape

Duration: 5-6 months, starting from March 1st (flexible)

Location: Télécom Paris, 19 Pl. Marguerite Perey, 91120 Palaiseau

Advisors:
    Maria Boritchev (https://mboritchev.github.io/page-perso-TP/),
    Tiphaine Viard (https://tiphainev.github.io/)

Gratification: Approximately 600 euros per month (more or less 20 euros
per month, the precise amount will depend on changes in the French
labor code in 2025).

Context:
The recent years have seen a surge of initiatives with the goal of
defining what "ethical" artificial intelligence would or should entail,
resulting in the publication of various charters and manifestos
discussing AI ethics; these documents originate from academia, AI
industry companies, non-profits, regulatory institutions, and the civil
society. The contents of such documents vary wildly, from short, vague
position statements to verbatims of democratic debates or impact
assessment studies. As such, they are a marker of the social world of
artificial intelligence, outlining the tenets of different actors, the
consensus and dissensus on important goals, and so on [1]. We have
assembled a corpus of charters and manifestos of Ethics of AI, in
English, written by different actors of the current AI landscape. This
corpus is called MapAIE: https://mapaie.telecom-paris.fr/.
We are conducting research on data from MapAIE both from a sociological
and linguistic perspectives:

-   Sociologically, who are the groups of people who write about
    Ethics of AI?
-   Linguistically, what type of vocabulary or semantic constructions
    do people use to write about Ethics of AI?
-   Socio-linguistically, is there a difference in linguistic usage
    between different groups of people who write about Ethics of AI?

To conduct these investigations, we would like to go further than
traditional tools: we intend to develop graph-based natural language
processing and computational sociology approaches making better use of
modern NLP methods to explore our data. In particular, we could to
exploit word sense induction approaches to automatically extract
different linguistic usages.

Objectives:
The goal of this internship is to investigate MapAIE by using and
developing graph-based natural language processing and computational
sociology approaches. The internship will proceed in three steps:
(1) Conduct a state of the art exploration on existing graph-based
    natural language processing and computational sociology techniques,
    starting from Abstract Meaning Representations (AMR,
    https://github.com/amrisi/amr-guidelines/blob/master/amr.md) and
    Cortext ( [ https://www.cortext.net/).
(2) Re-implement existing techniques identified in (1), in
    particular [2], and analyse the obtained results sociologically and
    linguistically in view of the research questions of the project.
(3) Propose new research questions and new graph-based data exploration
    approaches relevant to MapAIE.

Bibliography:
[1] Mapping AI ethics: a meso-scale analysis of its charters and
    manifestos, Mélanie Gornet, Simon Delarue, Maria Boritchev, and
    Tiphaine Viard, ACM Conference on Fairness, Accountability and
    Transparency 2024.
[2] Matan Eyal, Shoval Sadde, Hillel Taub-Tabib, and Yoav Goldberg.
    2022. Large Scale Substitution-based Word Sense Induction. In
    Proceedings of the 60th Annual Meeting of the Association for
    Computational Linguistics.
[3] Roth, C., & Hellsten, I. (2023). Socio-semantic configuration of an
    online conversation space: The case of Twitter users discussing
    the# IPCC reports. Social Networks, 75, 186-196.
[4] Becker, H. S. (1976). Art worlds and social types. American
    behavioral scientist, 19(6), 703-718.
[5] Cefaï, D. (2016). Publics, problèmes publics, arènes publiques.... Que
    nous apprend le pragmatisme?. Questions de communication, (30),
    25-64.


Application:
* deadline: December 20th, 2024 *
To apply for this position, please send an email with your CV and a few
words explaining your interest in this project to Maria Boritchev and
Tiphaine Viard (mailto:firstname.lastname@univ-nantes.fr). We are
looking for applications from students preparing a Master's degree or
equivalent with solid skills (and ideally experience) in Natural
Language Processing, Machine Learning and Deep Learning, Computational
Social Sciences. Knowledge of English is necessary.