IEEE International Workshop on Fair and Interpretable Learning Algorithms (FILA 2020)
December 10 - 13, 2020
Atlanta, GA, USA
https://fila-workshop.github.io/

In conjunction with the IEEE International Conference on Big Data (IEEE BigData 2020)
December 10 - 13, 2020
Atlanta Marriott Marquis
Atlanta, GA, USA
http://bigdataieee.org/BigData2020/

Introduction
With the proliferation of artificial intelligence (AI) and machine learning (ML) algorithms in every aspect of our automated, digital and interconnected society, the issues of fairness, explainability, and interpretability of AI and ML algorithms have become very important. If the decision (output) made by an algorithm is not transparent to or interpretable by the receivers of such decisions, then they will always have questions about its efficacy. Similarly, an AI/ML algorithm should not amplify existing inequalities in the society by impacting different sections of the population differently, and instead, be fair to everyone.

The International Workshop on Fair and Interpretable Learning Algorithms (FILA 2020) will provide a venue for academic researchers, industry professionals, and government partners to come together, present and discuss research results, use cases, innovative ideas, challenges and opportunities that arise from designing machine learning algorithms that are fair and interpretable. Since the task is inherently multi-disciplinary, the workshop will attempt to foster collaboration between different research communities working on problems ranging from Algorithms, Theoretical Computer Science, Network Science to Artificial Intelligence, Machine Learning, Data Science, and Social Choice, Game Theory, Computational Social Science. This year, our focus is specifically on fairness. We encourage submissions spanning the full range of theoretical and applied works. Topics of interest include, but are not limited to:

Identification of unfairness and biases

Biases in popularly used machine learning datasets
Fairness audits on the use of sensitive data
Biases in news and social media
Investigation of black-box systems, particularly web platforms and algorithms
Biases in machine learning from complex data (e.g., networks, time series)
Biases in information retrieval and natural language processing
Other novel application domains such as economics, healthcare, climate studies
Machine learning in the context of developing countries and other under-represented communities

Designing fair learning algorithms

The statistical and computational complexity of fair machine learning
Online and stochastic optimization methods for fair machine learning
Fair machine learning through Bayesian methods
Achieving fairness through computational social choice and game theory
Fairness, accountability, transparency and ethics in search
Fairness and diversity in recommender systems
Fairness-aware algorithms for social impact
Evaluation methods for fair and interpretable machine learning
Fairness beyond supervised learning, e.g. clustering, reinforcement learning
Interpretability of neural network and deep learning algorithms
Accountability in human-in-the-loop machine learning

Submission guidelines: We welcome submissions related to the above research areas. Submissions may include previously published results, late-breaking results, and work in progress. We also solicit vision or position papers. As IEEE BigData 2020 will no longer take place physically in Atlanta, Georgia, US, and will instead take place virtually, the relevant workshop submissions will be accepted for video presentations in the virtual presentation session.

Archival and Non-archival: FILA 2020 offers authors the choice of archival and non-archival paper submissions. Archival papers will appear in the published proceedings of the workshop, if they are accepted. Whereas, accepted non-archival papers will only appear in the workshop website and not in the proceedings. Authors of non-archival papers are free to also submit their work for publication elsewhere. Note that all submissions will be judged by the same quality standards, regardless of whether the authors choose the archival or non-archival option. Authors of all accepted papers must register and present their work at the workshop, regardless of whether their paper is archival or non-archival.

Submission Site: Cyberchair's FILA submission portal
https://wi-lab.com/cyberchair/2020/bigdata20/scripts/submit.php?subarea=S31&undisplay_detail=1&wh=/cyberchair/2020/bigdata20/scripts/ws_submit.php

Writing Guidelines: Paper authors may choose between two formats: Long (10 pages) and Short (4 pages), in the IEEE Computer Society proceedings manuscript format. Both formats will be rigorously peer-reviewed. You are strongly encouraged to print and double-check your PDF file before its submission, especially if your paper contains Asian/European language symbols (such as Chinese/Korean characters or English letters with European fonts). Complete papers are required; abstracts and incomplete papers will not be reviewed. The formatting guidelines are available at: https://www.ieee.org/conferences/publishing/templates.html.

Important Dates (All times are 11:59 PM Eastern Standard Time)
Paper Submission: October 7, 2020 (extended)
Notification: November 1, 2020
Camera Ready: November 15, 2020
Workshop: December 10 - 13, 2020

Note: All accepted papers will be made available online prior to the workshop and remain accessible afterward (unless for non-archival papers under explicit requests by the authors). Video presentations will be uploaded to Youtube after getting consent from the presenters.

Keynote Speakers:
o Professor Krishna P. Gummadi (Max Planck Institute for Software Systems, Saarbrücken, Germany)
o Professor Hanghang Tong (University of Illinois at Urbana-Champaign, Urbana, IL, USA)
o Professor Auroop Ratan Ganguly (Northeastern University, Boston, MA, USA)

Organization

General Chairs:
o Arindam Pal (Data61, CSIRO and Cyber Security CRC, Sydney, New South Wales, Australia)
o Yinglong Xia (Facebook AI, Santa Clara, CA, USA)

Program Chairs:
o Abhijnan Chakraborty (Max Planck Institute for Software Systems, Saarbrücken, Germany)
o Mayank Singh (IIT Gandhinagar, Gujarat, India)