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)

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.



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

Title: “On the responsibility of governments towards the development of AI for social good”
AI research is enabling powerful tools, which can be oriented either towards nefarious or beneficial directions. Like in the area of climate change, market forces and profit maximization are not always well aligned with the collective interests of society when it comes to the development of AI for social good. The governments’ responsibility thus concerns the two sides of this coin: protecting the public with ethical regulations of AI and incentivizing the development of AI applications which have great societal value but are not sufficiently appealing from a profit perspective and thus require government investments. Regarding regulations, this presentation will discuss the importance of increased transparency, and regarding beneficial applications, the particular cases of AI for fighting climate change and drug discovery to prevent future pandemics and the looming catastrophe of antimicrobial resistance.

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

Title: “AI for Good”

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

Title: “Evolving Ethics in Research Data Science”
The ethics of data science is a critical but still emerging field of study. Concerns around individual privacy, personal data control, and the dangers of surveillance capitalism have been well documented and have even led to government action like the European Commission’s General Data Protection Regulation (GPDR). We have paid less attention to the ethics around research data management, sharing, and analysis, but these issues of research data ethics, while subtle, can have equally broad implications. They provide the context for what we, as a society, understand to be true.

In this presentation, I review the fundamental considerations of ethical data management, sharing ,and analysis –why it is scientifically and ethically necessary to share data, and why it may also be necessary to constrain data access, especially in an interdisciplinary context. Data, information and knowledge sharing is essential to understanding and addressing the critical interconnected challenges facing society from the pandemic to climate change. This requires new forms of knowledge production which must be deliberately more diverse and inclusive — not just sharing across disciplines but also across professions, cultures ,and fundamental ways of knowing. We must foster a culture of collaboration and community rather than the competition that is common in science. We must promote sustainability and maintenance not just novelty and innovation. I will illustrate how that is happening through new communities, new principles, and creative applications of technologies.

There are also important concerns of bias in how we choose and apply modern informatics technologies be they basic identifiers or sophisticated machine learning algorithms. Data science must provide a bridge across technical and social consideration. Data scientists cannot simply be technicians. We must understand how to apply our skills appropriately in context.

Our modern, data-rich environment provides new opportunities but also new and still evolving ethical considerations. I summarize these considerations in five major themes that data scientists must address: ethical openness, maintenance and care, the power of identification and classification, implicit bias, and the coproduction of knowledge.

Lianglin Hu (Director of National Basic Science Data Center, Deputy Director of the Big Data Department of CNIC)

Title: “Scientific Data Ethics in China”
This report includes three parts. Firstly, I will give a briefly introduction on Scientific Data progress in China in Part I, including 40-year data work in Chinese Academy of Sciences and 20-year data work hosted by Ministry of Science and Technology of the People´s Republic of China. In part II, our Scientific Data Ethics work will be highlights, such as policy and implement in different levels. At last,I will introduce the “CODATA Data Ethics Working Group Proposal” Submitted 2 month ago.





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.




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: 13 October 2021



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 – 13:15

Opening Speaker: Saurabh Sinha

Deputy Vice Chancellor : Research and Internationalisation, University of Johannesburg

13:15 – 13:45

Ricardo Corrales-Barquero

“A Review of Gender Bias Mitigation in Credit Scoring Models”

13:405 – 14:15

Risuna W. Nkolele

“Explainable Machine Learning: A Manuscript on the Customer Churn in the Telecommunications Industry”

14:15 – 14:45

Alta de Waal

“Evaluation of XAI as an enabler for fairness, accountability and transparency”

14:45 – 15:45

Panel Discussion 

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

15:45 – 16:00


16:00 – 17:00

Plenary Speaker: Yoshua Bengio

“On the responsibility of governments towards the development of AI for social good”

17:00 – 17:20

Jimut B. Pal

“Classifying Chest X-Ray COVID-19 images via Transfer Learning”

17:20 – 17:40

Abdul Qayyum

“Late-Ensemble of Convolutional Neural Networks with Test Time Augmentation for Chest XR COVID-19 Detection”

17:40 – 18:00


18:00 – 18:20

Suman Chaudhary

“Ensemble deep learning method for Covid-19 detection via chest X-rays”

18:20 – 18:40

Ayyaz Azeem

“Covid-19 Chest X-Ray Image Classification using Deep Learning”

18:40 – 19:40

Plenary Speaker: Mark Parsons

“Evolving Ethics in Research Data Science”

28 October 2021

13:00 – 13:15

Opening Speaker: Charis Harley

Head: Data Science Across Disciplines Research Group

13:15 – 13:45

Yolanda Nkalashe

“Interpretable Natural Language (text) Processing for Classification of Misinformation data”

13:45 – 14:15

Irene Nandutu

“Integrating AI ethics in wildlife conservation AI systems in South Africa: a review, challenges, and future research agenda”

14:15 – 14:45

Lukasz A. Machowski

“Nano Version Control and the Repo as the Next Data Structure in Computer Science and Artificial Intelligence”

14:45 – 15:45

Panel Discussion

“Data Stewardship and Responsible Data Science”
Chaired by Louise Bezuidenhout

15:45 – 16:00


16:00 – 17:00

Plenary Speaker: Geralyn Miller

“AI for Good”

17:00 – 17:20

Shixiao Li

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

17:20 – 17:40

Vishnu Mohan

“Detection of COVID-19 from Chest X-ray images: A Deep Learning Approach”

17:40 – 18:00


18:00 – 18:20

Salman Ahmed

“PRNet: Progressive Resolution based Network for Radiograph based disease classification”

18:20 – 18:40

Aidyn Ubingazhibov

“CXR COVID-19 detection using Ensemble learning and TTA”

18:40 – 19:00

Plenary Speaker: Lianglin Hu

 “Scientific Data Ethics in China”



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.

27 October 2021

Panel Discussion: ​”Trustworthy AI and Lending”


  • 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


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”


  • 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


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



PG Students from all educational backgrounds are invited to take part in the EE-RDS Conference Challenge! This year’s challenge is the effective detection of COVID-19 via Chest X-ray images. This is an opportunity for aspiring data scientists to work on a problem of global importance and compete for the chance to win one of two laptops sponsored by the National Integrated Cyberinfrastructure System (NICIS). We await your innovative ideas and skilful programming!

Details to the challenge may be obtained here:
Participants need to join to get access to the data. The train/validation data set is ready and the test data set with a submission sample will be posted soon.

Challenge Paper Due Date: 10 October 2021

You may also contact Prof Moulay Akhloufi for further details at:


Yoshua Bengio

Yoshua Bengio

Department of Computer Science and Operations Research
Université de Montreal

Yoshua Bengio is a full Professor in the Department of Computer Science and Operations Research at Université de Montreal, as well as the Founder and Scientific Director of Mila and the Scientific Director of IVADO. Considered one of the world’s leaders in artificial intelligence and deep learning, he is the recipient of the 2018 A.M. Turing Award with Geoff Hinton and Yann LeCun, known as the Nobel prize of computing. He is a Fellow of both the Royal Society of London and Canada, an Officer of the Order of Canada, and a Canada CIFAR AI Chair.


Geralyn Miller

Senior Director
AI for Good Research Lab at Microsoft


Mark Parsons

Mark Parsons

Editor in Chief: Data Science Journal
University of Alabama

Mark A. Parsons is a Research Scientist and geographer at the University of Alabama in Huntsville working to help align data, software, and information standards and processes across NASA’s science divisions. Mark has more than 25 years of experience in researching and developing data stewardship policies, practices, and systems. He has repeatedly and effectively built dynamic, functional collaborations across all sorts of differences in language and professional cultures. Mark has coordinated stewardship of a broad range of data from satellite remote sensing to Indigenous knowledge. He was the first Secretary General of the Research Data Alliance. He led the data management effort for the International Polar Year and helped establish the Exchange for Local Observations and Knowledge of the Arctic (ELOKA). His published work has guided national data policies and practice and has contributed to educational programs. Mark lives in Colorado and likes to ride bicycles, bake bread, and play outside.

Lianglin Hu

Director of the National Basic Science Data Center
Deputy Director of the Big Data Department of CNIC

Lianglin Hu is the Secretary General of the Chinese National Committee for CODATA (CODATA-China) and member of two National Standardization Technology Committees (TC28 and TC486 ). Since 2003, he has been studying policies and standards for scientific data management and sharing, including data ethics. He is a major contributor to more than 10 national standards, such as Data Provenance Description Model (GB/T 34945-2017), Scientific Data Citation (GB/T 35294-2017), Data Quality Evaluation Index (GB/T 36344-2018) etc. Recently, he is actively promoting the establishment of a Data Ethic Task Group in CODATA.


Chair of Committee:

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


  • 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
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