Call for papers
ETHICAI’s focus spans the entire processing pipeline from resource collection and annotation to system development and applications within Artificial Intelligence and Machine Learning.

Topics of interest
Ethics in multimodal, sensorial data collection
Ethics in annotation (crowd-sourced) of private data
Ethics in Affective, Behavioral, and Social Computing
Nudging and manipulation with machines
Human-machine interaction for vulnerable populations

Deadline for 1500-2000 words abstract submission 10 January 2018

Notification of acceptance 15 February 2018

Final version of accepted papers 9 March 2018

Workshop session 8 May 2018

Please submit your abstract via

For questions, please contact Laurence Devillers.

Laurence Devillers, LIMSI-CNRS/Paris-Sorbonne University, France

Björn Schuller, Imperial College London, UK / University of Augsburg, Germany

LJoseph Mariani, LIMSI-CNRS/University Paris-Saclay, France

Gilles Adda, LIMSI-CNRS/University Paris-Saclay, France

In the recent time of ever-more collection “in the wild” of “Big Data”, crowd-sourced annotation by large groups of individuals with often unknown reliability and high subjectivity, and “deep” and autonomous learning with limited transparency of what is being learnt and explainability of the decisions of the system, and how applications such as in health or robotics depending on such data may behave, ethics, legal, and societal implications (ELSI) have become more crucial than ever in the field of language and multimodal resources, making it a key concern of the LREC community. ETHICAI 2018 is the second workshop in this field, after ETHI-CA 2016 (with overlapping organisers)

The goal is thus to connect individuals ranging across LREC’s fields of interest such as human-machine and –robot and computer-mediated human-human interaction and communication, affective, behavioral, and social computing whose work touches on crucial ethical (or further ELSI) issues (e.g., privacy, traceability, explainability, accountability, etc.). According systems increasingly interact with and exploit data of humans of all ranges (e.g., children, adults, vulnerable populations) including non-verbal and verbal data occurring in a variety of real-life contexts (e.g., at home, the hospital, on the phone, in the car, classroom, or public transportation) and act as assistive and partially instructive technologies, companions, and/or commercial or even decision making systems. Obviously, an immense responsibility lies at the different ends from data recording, labeling, and storage to its processing and usage.