Online conference from October 27-28, 2021. As a launching event of the Data Science Across Disciplines Research Group (EE-RDS) 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:
- Responsible Data Science: Reliable and Trustworthy approaches for data engineering, data science and modern machine learning.
- Algorithmic Fairness, Transparency, and Explainability.
- Social and Ethical aspects of Responsible Data Science.
- Use cases illustrating the cross-disciplinary nature of the field of Data Science.
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.
- 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
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 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.
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! Submit your idea(s) to Professor Moulay Akhloufi (email@example.com).