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Call for papers! FATES 2020 at the Web Conference

From: Caroline Burle <cburle@nic.br>
Date: Mon, 9 Dec 2019 10:27:49 -0300
To: public-webmachinelearning@w3.org
Message-ID: <20e3867b-83a1-c66c-971f-e8754970f9cf@nic.br>
Dear Machine Learning for the Web Community Group,

On behalf of all the organizing committee, I send bellow the Call for 
Papers for FATES 2020 at the Web Conference: http://fates.isti.cnr.it/.

Caroline Burle

Template Fates


*FATES on the Web 2020 *

*Second Workshop on Fairness, Accountability, Transparency,
Ethics and Society on the Web*

Joint with The Web Conference 2020 
Taipei, Taiwan
20-24 April 2020


      About the Workshop

Following the successful edition of FATES 2019, this edition of the 
FATES workshop will promote the discussion around these critical 
questions and join forces towards a Web that is truly inclusive, 
transparent and open.

Data is learned from people. Personal data collected from social media 
and mobile devices, often considered sensitive information, has been 
extensively used by systems for a number of purposes, including user 
behavior forecasting, content recommendation and fraud detection. User 
behavior, in turn, is changing based on the algorithms that users are 
exposed to. Recent studies have revealed that many machine-learning 
based systems exhibit biases, including racial and gender bias. This 
scenario raises new challenges concerning algorithmic fairness and 
accountability, transparency of machine-learning models, the importance 
of developing better AI systems on the Web and tools to deal with 
privacy matters, and ethics on modeling and analyzing online 
communities, such as social media interactions, mobility data, political 
engagement networks, healthcare communities, and so on.

The goal of this workshop is to gather researchers and developers from 
academia, industry, and civil society to present and debate topics of 
the importance of developing better AI systems on the Web and tools to 
deal with privacy matters. To achieve this, we will seek contributions 
that describe research initiatives, projects, results, and design 
techniques and experiments that are being developed to deal with 
fairness and accountability, transparency, and ethics on AI and privacy. 
In this sense, we will encourage submissions in various degrees of 
progress, such as new results, visions, techniques, innovative 
application papers, and progress reports.

In this way, we will stimulate an interdisciplinary debate about 
emerging topics on the Web, creating an open forum for Web researchers, 
professionals, and industrial practitioners to share evolving knowledge 
and report ongoing work.


      Topics and Themes

• Algorithmic fairness and algorithmic bias, particularly on web data

• Credibility and reputation in social media

• Fairness, accountability, transparency, and ethics in web search and 
(social) web mining

• Fairness-aware recommender systems and diversity in recommendation

• Ethics of opinion mining and opinion formation on the web

• Ethical models/frameworks around web platforms and data

• Investigation of black-box systems, particularly web platforms and 

• Innovative methods for studying/analyzing the fairness, 
accountability, transparency and ethics of web platforms

• Impact of web platforms and algorithms on employment and the future of 

• Transparency and ethics of web-scale data analysis

• Transparency, fairness, and ethics of crowd-sourcing

• Transparency-aware algorithms for online civic engagement

• Web platforms and the public interest

• Algorithmic fairness and bias for smart cities

• Ethical and privacy aspects in mobility data analysis

• Ethical-aware machine learning models

• Ethics and legal audits on the use of sensitive data

• Evaluation methods for human-centered machine learning

• Fairness Metrics with Human Supervision

• Fairness Warnings

• Fake news, social bots, misinformation, and disinformation on social media

• Hate speech in social media

• Human-centered research for end-user ML

• Human-in-the-loop for privacy-aware machine learning

• Humans perceived consequences of surveillance algorithms

• Information/knowledge design/visualization for Privacy

• Methods and models for Social Computing and Digital Humanities

• Models for ensuring transparency and responsibility of government data

• Privacy-preserving and fairness-aware machine learning on the web

• Search Design for services on the webSocial web mining

• Usability challenges of machine learning

• User Experience (UX) for Privacy

• Design patterns and design research for ML Systems

• Transparency and Explainability in ML

All submissions will be peer reviewed and evaluated on the basis of 
originality, relevance, quality, and technical contribution. Submissions 
must present original work. Concurrent submissions are not allowed.



The papers accepted as full papers or short papers will be published 
jointly with The Web Conference proceedings. Papers accepted as 
discussion papers will ** not ** be published jointly with The Web 
Conference proceedings.


      Submission Guidelines

Authors can submit full papers (up to 10 pages in length), short papers 
(up to 6 pages in length), and discussion papers (up to 2 pages in 
length), written in English. Papers must be submitted at 
https://easychair.org/conferences/?conf=fates2020, in PDF according to 
the ACM format published in the ACM guidelines 
(www.acm.org/publications/proceedings-template), selecting the generic 
“sigconf” sample.

The PDF files must have all non-standard fonts embedded. PDF files must 
be double-blind. Submissions containing author identifying information 
are subject to rejection without review.


      Important Dates

Paper submission deadline: January 10, 2020
Paper acceptance notification: February 7th, 2020
Paper camera-ready version (firm deadline): February 17, 2020
FATES on the Web 2020: April 20 or 21, 2020


      Program Committee

Aline Paes, Universidade Federal Fluminense, Brazil
Anastasios Giovanidis, LIP6/Sorbonne Univ. (CNRS), France
Ana-Andreea Stoica, Columbia University, USA
Anna Monreale, University of Pisa, Italy
Arkaitz Zubiaga, Queen Mary University of London, UK
Arthi Manohar, Brunel University, UK
Bettina Berendt, Univ. of Leuven, Belgium
Claudio Lucchese, Università Ca’ Foscari Venezia, Italy
Cristina Muntean, ISTI/CNR, Italy
David Millen, IBM T.J. Watson Research, USA
Ida Mele , ISTI/CNR, Italy
Jefferson Silva, Pontifical Catholic University of São Paulo, Brazil
Josep Domingo-Ferrer, Rovira i Virgili University, Catalonia
Lucy Vasserman, Google, USA
Marco Antonio Casanova, Pontifical Catholic University of Rio de 
Janeiro, Brazil
Marília Guterres Ferreira, UDESC, Brazil
Maria Luiza Machado Campos, Federal University of Rio de Janeiro, Brazil
Michele Melchiori, University of Brescia, Italy
Nisha Talagala, Pyxeda AI, USA
Raffaele Perego, ISTI/CNR, Italy
Roberto Trani, ISTI/CNR, Italy


      Program Committee Co-Chairs and Organizers

Jonice Oliveira, Federal University of Rio de Janeiro, Brazil
Lívia Ruback, Federal University of Rio de Janeiro, Brazil
Chiara Renso, ISTI/CNR, Italy
Jeanna Matthews, Clarkson University, USA ( Organizing Committee Chair)
Caroline Burle, Ceweb.br at Brazilian Network Information Center 
(NIC.br) and W3C Brazil Office, Brazil
Diogo Cortiz, Ceweb.br at Brazilian Network Information Center (NIC.br) 
and PUC São Paulo, Brazil
Heloisa Candello, IBM Research, Brazil

Received on Monday, 9 December 2019 13:27:58 UTC

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