[visionlist] CfP FAT/MM: Fairness, Accountability and Transparency in Multimedia
xavier.alameda-pineda at inria.fr
Mon Apr 29 11:01:53 -04 2019
ACM MM 2019 Workshop on Fairness, Accountability and Transparency in Multimedia (FAT/MM)
Paper submission, July 8th
Author Notification, August 5th
Camera-Ready, August 15th
Website : [ https://project.inria.fr/fatmm | https://project.inria.fr/fatmm ] /
The computational inclusiveness and transparency of automatic information processing methods is a research topic that exhibited growing interest in the past years. In the era of digitized decision-making software where the push for artificial intelligence happens worldwide and at different strata of the socio-economic tissue, the consequences of biased, unexplainable and opaque methods for multimedia analysis and content retrieval, can be dramatic. In this context, the multimedia community must put together the necessary efforts in applying its expertise and know-how and investigate how to transform the current technical tools and methodologies so as to derive computational models that are transparent and inclusive.
Information processing is one of the fundamental pillars of multimedia, it does not matter whether data is processed for content, experience or systems applications, the automatic processing of information is used in every corner of our community. This is why it is crucial to start bringing the notion of fairness, accountability and transparency into ACM Multimedia.
Call for Contributions
The workshop aims to foster research around a timely and crucial topic for the present digitized society: the fairness, accountability and transparency of multimedia algorithms. The workshop has a strong scientific link with the FAT/ML workshop and the ACM FAT* conference. Differently from FAT/ML, which is anchored in machine learning, the FAT/MM workshop addresses fairness, accountability and transparency in the core of the multimedia community. We expect submissions covering any topic closely related to the multimedia community AND falling in one (or many) of the following categories:
* Techniques and models for fairness-aware multimedia modeling, multimedia information retrieval, and recommendation.
* Interpretable and explainable models in multimedia.
* Models and frameworks for conducting FAT audits of multimedia systems.
* Models for addressing inclusion and exclusion in multimedia.
* Qualitative, quantitative, and experimental studies on subjective perceptions of algorithmic bias and unfairness.
* Experimental results of FAT audits of multimedia systems.
* Objective metrics for measuring unfairness and bias in multimedia.
* Generation of human-readable explanations for multimedia models and algorithmic outputs.
* Metrics for measuring inclusiveness in multimedia systems.
Data collection and curation
* Defining, measuring and mitigating problematic biases in multimedia datasets.
* Improvement of data collection processes to be more fair, diverse, and inclusive.
* Data collection regarding potential unfairness in systems.
Xavier, on behalf of the FAT/MM organisers
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