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
  • Sponsorship is being provided by the National Integrated Cyberinfrastructure System (NICIS)

EE-RDS 2021 Conference Proceedings

Here is the link to EE-RDS 2021 publications in IEEE Xplore: https://ieeexplore.ieee.org/xpl/conhome/9708546/proceeding


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.

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.

 


SCIENTIFIC PROGRAMME (2021)

The Scientific Programme below provides details on EE-RDS 2021.  All presentations from 2021 may be viewed here. To view a particular presentation, you may click on the title below.

 

27 October 2021

14:45 – 15:45

Panel Discussion 

“Trustworthy AI and Lending”
Chaired by Kush R. Varshney

15:45 – 16:00

Break

17:40 – 18:00

Break

18:40 – 19:40

Plenary Speaker: Mark Parsons

28 October 2021

13:00 – 13:15

Opening Speaker: Charis Harley

14:45 – 15:45

Panel Discussion

15:45 – 16:00

Break

16:00 – 17:00

Plenary Speaker: Geralyn Miller

17:00 – 17:20

Shixiao Li

“Classification network of COVID-19 based on multi-modality fusion network”

17:40 – 18:00

Break

18:40 – 19:00

Plenary Speaker: Lianglin Hu


PANEL DISCUSSIONS (2021)

During EE-RDS 2021 we hosted Problem-Solving Panel Discussions where groups of specialists considered problems of real-world importance. See details below from last year’s panels:

27 October 2021

Panel Discussion: ​”Trustworthy AI and Lending”

Chair:

  • Kush R. Varshney, Distinguished Research Staff Member and Manager, IBM Thomas J. Watson Research Centre

Panel members:

  • Jiahao Chen, CTO at Parity AI
  • Moise Busogi, Carnegie Mellon University (CMU) Africa

Topic:

In this panel discussion, the discussants will be examining home mortgage lending approval decisions carried out by machine learning systems. They will consider the various phases of the development lifecycle (problem specification, data preparation, modelling, evaluation, deployment) from the perspective of trustworthy AI. They will critically examine the fairness, explainability, robustness, and transparency of a solution that could be created.

28 October 2021

Panel Discussion: ​”Data Stewardship and Responsible Data Science: Lessons from the CODATA-RDA Schools for Research Data Science”

Chair:

  • Louise Bezuidenhout, University of Cape Town

Panel members:

  • Hugh Shanahan, Royal Holloway University of London
  • Shanmugasundaram Venkataraman, Digital Curation Centre
  • Joy Davidson, Digital Curation Centre
  • Sanjin Muftic, University of Cape Town
  • Naniki Maphakwane, Botswana Open University

Topic:

Data-driven research relies on a range of expertise within research communities. This has led to the emergence of the data stewards, individuals who provide oversight or data governance support within organizations and ensure the quality and fitness for purpose of data assets including the metadata. The CODATA-RDA Schools for Research Data Science, together with FAIRsFAIR, have developed data stewards training workshops that focus on training on responsible/open research and research data management. This panel will discuss this training in detail, and include a broader discussion about the benefits and challenges of educating data stewards and their impact on responsible research.

Appropriate links are provided here:

CODATA-RDA Schools for Research Data Science: Link
Example of upcoming data steward school: Link
Data Steward Schools collaboration with FAIRsFAIR: Link


ORGANISING COMMITTEE

Chair of Committee:

  • Charis Harley, Head of Data Science Across Disciplines Research Group (DSAD), Faculty of Engineering and the Built Environment, University of Johannesburg

Committee:

  • Moulay Akhloufi, Head of the Perception Robotics and Intelligent Machines Group (PRIME), Department of Computer Science, University of Moncton
  • Terence van Zyl, Institute of Intelligent Systems, University of Johannesburg
  • Anwar Vahed, Director at NICIS Data Intensive Research Initiative of South Africa
Oct 27, 2021
Faculties Johannesburg Business School

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