Speakers
11:00 AM-12:20 PM
Elena Gabriela Ardelean
AI4DevelopmentAgency, AustriaTitle: Why Community-Driven AI Is The Future
Abstract:
Digital technologies are shaping all aspects of our lives and represent a game-changer in implementing the sustainable development agenda and serving our communities. In particular, Artificial Intelligence (AI) is changing life as we know it — from job opportunities to the way we interact with each other, conduct business and guard our privacy. The question is no longer “WHY AI” but “HOW”. With the right mindset, we as a community can put the progress of AI for the benefit of our societies. Our research shows that ML/AI can transform our communities and build products that benefit the majority. However, the adoption of AI by the majority is facing problems such as mistrust, disinformation, and misuse of personal data. To tackle these challenges, solutions need to be built by communities. Our findings show that when communities are given the right support and environment, they can come together to create these products with a success rate of over 90%. One of the solutions we are currently working on to address mistrust and disinformation, is a Knowledge Platform and playful learning tool developed on a multi-dimensional Algorithm to help map AI initiatives driven by communities around the globe, understand AI concepts, and develop essential skills for the future. Our platform has several unique features: (i) globally accessible, (ii) aggregates data from multiple sources, (iii) multidisciplinary, diverse and collaborative, and (iv) presents customizable learning modules.
Biography:
Elena Gabriela Ardelean is the CEO & Founder of the Artificial Intelligence 4 Development Agency (AIDA), a responsible social enterprise which supports communities in understanding and using AI. Elena believes AI represents a global challenge and an excellent opportunity for which she engages widely to prepare citizens for the future and tackle disinformation. Until recently, Elena was working for the World Bank advising Institutions on a wide range of reforms and activities, including strategic planning, performance measurement, and policy analysis. Before that, she worked in Paris as an ICT Senior Consultant in Strategic Organizational Change. Elena is currently enrolled in an EMBA program. She is certified in Machine Learning from Stanford University, holds a Master’s degree in International Affairs from Sciences Po Paris and has studied Philology and Cultural Studies at the University of Vienna, Austria.
Rehan Babar
University of Calgary, CanadaTitle: Deeper Look: Artificial Intelligence (AI)Acceptance In Radiology
Abstract:
Ubiquitous computational power. Faster Data processing. Rapid progress of analytic techniques. We are amid major changes all around us and they are happening at an exponential pace. Artificial Intelligence (AI) – which aims to mimic human cognitive functions – is bringing a paradigm shift to the field of radiology. In the last decade, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Further implementation of AI in radiology will significantly improve the quality, value, and depth of radiology’s contribution to patient care and revolutionize radiologists’ workflows. However, recent reports of health information technology (IT) show that the acceptance between purchased technologies and clinical work systems is critical in determining intended end users to accept or reject the technology, to use or to misuse it, or to incorporate it into their clinical workflows or work around it. This paper assesses technology implementation frameworks in the context of AI in radiology and employs a widely accepted and validated technology acceptance framework - the Technology Acceptance Model (TAM). The model is built on the premise that when an end-user is introduced to a technology, there are constructs and relationships that influence when and how a user will interact with the technology. In addition, the findings can further inform and provide guidance for policymakers, AI application developers, and business management on the educational needs of radiologists, research and development, and the role of radiologists in moving forward with AI in radiology.
Biography:
Rehan Babar, is currently completing his Bachelor of Commerce Honours at University of Calgary (UofC) with a specialization in Accounting/Finance and Artificial Intelligence (AI). He has represented UofC in some of the most prestigious management competitions in the world (Harvard Case Competition, Inter-collegiate Business Case Competition) and has earned a top-3 spot in 5 global competitions. He is among the first 7 students from the Haskayne School of Business to be selected for the prestigious and rigorous Honours research program and has received multiple awards for his research pertaining to AI in the field of radiology