(Apologize for multiple postings)
*CALL FOR PAPERS*
*****************************************************************************************
*IPMU 2022 -*
Information Processing and Management of Uncertainty in Knowledge-Based
Systems /
July 11-15, 2022 – Milan, Italy
https://ipmu2022.disco.unimib.it/
*Special Session on "Data Perspectivism in Ground Truthing and Artificial
Intelligence"*
S3 - https://ipmu2022.disco.unimib.it/special-sessions/
**************************************************
* Description, scope and aims*
Many Artificial Intelligence applications are based on supervised machine
learning (ML), which ultimately grounds on manually annotated data. The
annotation process (often called
ground-truthing) is often performed in terms of a majority vote and this
has been proved to be often problematic, as highlighted by recent studies
on the evaluation of ML models. Recently, a different paradigm for
ground-truthing has started to emerge, called data perspectivism [1], which
moves away from traditional majority aggregated datasets, towards the
adoption of methods that integrate different opinions and perspectives
within the knowledge representation, training, and evaluation steps of ML
processes, by adopting a non-aggregation policy. This alternative paradigm
obviously implies a radical change in how we develop and evaluate ML
systems: such ML systems have to take into account multiple, uncertain, and
potentially mutually conflicting views [2]. This obviously brings both
opportunities and difficulties: novel models or training techniques may
need to be designed, and the validation phase may become more complex.
Nonetheless, initial works have shown that data perspectivism can lead to
better performances [3,4], and could also have important implications in
terms of human-in-the-loop and interpretable AI, as well as in regard to
the ethical issues or concerns related to the use of AI systems [5]. Data
perspectivism is a framework to treat uncertainty (the main theme of IPMU)
at the level of knowledge modeling and its integration in the development
and evaluation of systems.
The scope of this special session is to attract contributions related to
the management of subjective, uncertain, multi-perspective, or otherwise
non-aggregated data in ground-truthing, machine learning, and more
generally artificial intelligence systems.
Invited contributions: full research papers and research in progress
papers.
*Topics of interest:*
Subjective, uncertain, or conflicting information in annotation and
crowdsourcing processes;
Limits and problems with standard data annotation and aggregation
processes;
Theoretical studies on the problem of learning from multi-rater and
non-aggregated data;
Participation mechanisms/incentives/gamification for rater engagement
and crowdsourcing;
Ethical and legal concerns related to annotation and aggregation
processes in ground-truthing;
Creation and documentation of multi-rater and non-aggregated datasets
and benchmarks;
Development of ML algorithms for multi-rater and non-aggregated data;
Development of techniques to detect and manage multiple forms of
uncertainty in multi-rater and non-aggregated data;
Techniques for the evaluation of ML systems based on multi-rater and
non-aggregated data;
Applications of data perspectivism and non-aggregated data to
interpretable, human-in-the-loop AI and algorithmic fairness;
Experimental and application studies of ML/AI systems on multi-rater
and non-aggregated data, in possibly different application domains (e.g.
NLP, medicine,legal studies, etc.)
***************************************************
*Important dates*
Paper Submission deadline: Friday, 14 January 2022 *Friday, 18 February
2022 (STRICT)*
Notification of acceptance: April 1st, 2022
Camera ready due: April, 22nd, 2022
IPMU Conference: July 11th -15th, 2022
*************************************************
*Author Guidelines:*
Please refer to the IPMU 2022 page where guidelines and templates are
available, in the main conference Web site (
https://ipmu2022.disco.unimib.it/submission/).
All submissions accepted for presentation at IPMU 2022 will be published in
the Communications in Computer and Information Science (CCIS) series, by
Springer.
***************************************************
*IPMU 2022 S3 Special Session Co-Chairs:*
Andrea Campagner (University of Milano-Bicocca, Italy),
Teresa Scantamburlo (Ca’ Foscari University of Venice, Italy),
Valerio Basile (University of Turin, Italy),
Federico Cabitza (University of Milano-Bicocca, Italy)
***************************************************
*Related readings*
*[1] Basile, V., Cabitza, F., Campagner, A., Fell, M. (2021)Toward a
Perspectivist Turn in Ground Truthing for Predictive ComputingarXiv
preprint, arXiv:2109.04270https://arxiv.org/pdf/2109.04270.pdf
<https://arxiv.org/pdf/2109.04270.pdf>*[2] Zhang, J., Wu, X., Sheng, V.S.
(2016)
Learning from crowdsourced labeled data: A survey.
Artificial Intelligence Review
[3] Fornaciari, T., Uma, A., Paun, S., Plank, B., Hovy, D., Poesio, M.
(2021)
Beyond Black & White: Leveraging Annotator Disagreement via Soft-Label
Multi-Task
Learning
Conference of the North American Chapter of the Association for
Computational Linguistics:
Human Language Technologies (NAACL 2021)
[4] Campagner, A., Ciucci, D., Svensson, C.M., Figge, M. T., Cabitza, F.
(2021)
Ground truthing from multi-rater labeling with three-way decision and
possibility theory
Information Sciences
[5] Basile, V., Fell, M., Fornaciari, T., Hovy, D., Paun, S., Plank, B.,
Poesio, M., Uma, A. (2021)
We Need to Consider Disagreement in Evaluation
1st Workshop on Benchmarking: Past, Present and Future at ACL 2021
_______________________________________________
uai mailing list
uai@engr.orst.edu
https://it.engineering.oregonstate.edu/mailman/listinfo/uai
