Ethics and Explainability for Responsible Data Science (EE-RDS)

Ethics and Explainability for Responsible Data Science (EE-RDS)


Publishing Date: 4/15/2021 1:00 PM

art Conference hosted by:

  • Data Science Across Disciplines (DSAD) Research Group, Institute for the Future of Knowledge, University of Johannesburg
  • Perception Robotics and Intelligent Machines Research Group (PRIME)University of Moncton
  • Cluster of Excellence: Machine Learning in Science, Tübingen University

Dates: 27 – 28 October 2021 

Registration: Please register for the conference here. All participants as well as presenters must register. If you are planning to submit an abstract, then you will need to do so as per the instructions below.

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KEYNOTE SPEAKERS

Yoshua Bengio (Department of Computer Science and Operational Research, Université de Montréal, IVADO, CIFAR, Scientific Director - Mila)

Geralyn Miller (Sr. Director of the AI for Good Research Lab at Microsoft) 

Mark Parsons (Editor in Chief, Data Science Journal, University of Alabama)

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THEMES

Is Data Science a new approach to solving problems, one that applies across disciplines as various as physics, sociology and linguistics? Or are machine learning, deep convoluted neural nets, and other exciting phrases just statistics on steroids?

Recent developments in Data Science broadly construed, and the products these have yielded (or promise to yield) are undeniably exciting: identifying and predicting disease, personalised healthcare recommendations, automating digital ad placement, predicting incarceration rates, and countless other tools have attracted a lot of attention. But what about the process behind these products? Are these amazing feats based on traditional scientific discoveries? Or does the problem-solving approach which is being implemented have an even wider range of applicability than we could imagine? While the Sciences and Engineering are driving the field, traditional Humanities and the Social Sciences are also experimenting and contributing to a growing body of knowledge around the use of data. This conference seeks to understand the nature and significance of data science for traditional modes of inquiry across the full spectrum. We also seek to interrogate underlying ethical issues that arise not only in research but also when data science is relied on in decision-making – this is where notions of explainability, fairness and discrimination form part of the practical application of responsible data science.

As a launching event of the Data Science Across Disciplines Research Group at the University of Johannesburg, this conference brings together reflections on both the actual and potential impact of data science across disciplines and sectors. Submissions are welcome from any disciplinary background, with a focus on scientific contributions, conceptual themes, and reflections within the areas of:

  1. Responsible Data Science: Reliable and Trustworthy approaches for data engineering, data science and modern machine learning.
  2. Algorithmic Fairness, Transparency, and Explainability.
  3. Social and Ethical aspects of Responsible Data Science.
  4. Use cases illustrating the cross-disciplinary nature of the field of Data Science.

All papers must be pitched in a suitably accessible way and speak to the cross-disciplinary nature of the event.

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ABSTRACT SUBMISSION

Please ensure that you have registered for the conference here before submitting an abstract. 

Abstract Submission: Please submit your extended abstract on the Microsoft CMT website and ensure that you use the IEEE abstract template provided here: IEEE Template.

Note: You may use either the LaTeX or Word template but your extended abstract must be a minimum of 4 pages long and in .pdf format
When you submit your extended abstract you will be asked to indicate whether you would be interested in publishing your work in IEEEXplore proceedings at a minimal fee. The authors of submissions of suitable quality will be contacted at a later stage should they indicate an interest in doing so.

Abstract Due Date: 30 August 2021

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SCIENTIFIC PROGRAMME

The Scientific Programme does not only encompass presentations by Keynote Speakers and registered participants, but also a Problem-Solving panel during which experts in the field will discuss and debate a possible solution to a stipulated problem within the field of Data Science. All conference participants and especially Postgraduate Students are encouraged to attend this panel debate and will be allowed to engage via the chat function.

27 October 2021
​13:00 – 13:10
​Opening Speaker
​13:10 – 13:30
​Speaker
​13:35 – 13:55
​Speaker
​14:00 – 14:20
​Speaker
​14:25 – 14:45
​Speaker
14:50 – 15:50
​Panel Discussion
​15:50  16:00
Break
16:00  17:00
​Plenary Speaker: Yoshua Bengio
​"AI for Social Good"
​17:00  17:20
​Speaker
​17:25  17:45
​Speaker
​17:50  18:10
​Break
​18:10  18:30
​Speaker
​18:35  18:55
​Speaker
19:00 – 20:00
​Plenary Speaker: Mark Parsons

28 October 2021

​13:00  13:10
​Opening Speaker
​13:10  13:30
​Speaker
​13:35  13:55
​Speaker
​14:00  14:20
​Speaker
​14:25  14:45
​Speaker
​14:50  15:50
​Panel Discussion
​15:50  16:00
​Break

16:00  17:00
​Plenary Speaker: Geralyn Miller
​17:00  17:20
​Speaker
​17:25  17:45
​Speaker
17:50  18:10
​Tea Break
​18:10  18:30
​Speaker
​18:35  18:55
​Speaker
19:00  20:00
​Plenary Speaker: TBA

PANEL DISCUSSION

As a part of this conference, we will host a Problem-Solving Panel Discussion where a group of specialists will consider a problem of real-world importance; they will clarify the issue at hand, discuss possible issues involved, consider the tools at their disposal and ultimately design and argue for a feasible solution.

On each of the days of the conference, 60 minutes will be set aside for a panel discussion on a particular problem or issue related to the theme(s) of the Conference. Panel members will be assigned by the Scientific Committee of the Conference, and attendees will be allowed to sign up to attend as a part of the audience.

Call for Panel Discussion Topics:
In order to submit a theme/use case for such a Panel Discussion, please contact Prof Charis Harley (charley@uj.ac.za) directly with a one page motivation, as well 2-3 suggestions for appropriate panel members. The Organising Committee will confirm who the panel members are once the themes for the panels are finalised.


POSTGRADUATE CHALLENGE

PG Students from all educational backgrounds are invited to take part in the EE-RDS Conference Challenge!  This challenge is an opportunity for aspiring data scientists to work on a problem of global importance and compete for the chance to win a prize. We await your innovative ideas and skilful programming!

Call for Challenges: 
The EE-RDS Organising Committee seeks proposals for scientific challenges for a Postgraduate Challenge. Submit your idea(s) to Prof Moulay Akhloufi (moulay.akhloufi@umoncton.ca).

SCIENTIFIC COMMITTEE


  • Charis Harley, Head of DSAD, Faculty of Engineering and the Built Environment, University of Johannesburg
  • Moulay Akhloufi, Head of the Perception Robotics and Intelligent Machines Group, Department of Computer Science, University of Moncton.
  • Turgay Çelik, School of Electrical and Information Engineering, Faculty of Engineering and the Built Environment, University of the Witwatersrand
  • Terence van Zyl, Institute of Intelligent Systems, University of Johannesburg
  • Anwar Vahed, Director at NICIS Data Intensive Research Initiative of South Africa