Co-organised by
Tomohiro Ando (Melbourne Business School, University of Melbourne)
Robert Kohn (UNSW Business School, University of New South Wales)
Valentin Zelenyuk (School of Economics, University of Queensland)
This event series aims to unleash ideas and insights for harnessing the successful future of business & society. Therefore, the first part (speaker's talk) focuses on cutting edge ideas and the second part (discussion) explores its practical usages in business and society, bridging a gap between new ideas and real business/managing organisations and society. Main audience includes business professionals (including C-suites, directors, managers) from small to large organizations, government and regulatory and not-for-profit organizations. It is not necessarily to have a fluency of data analytics. While idea transfer to business and society is the focus, interested academics and students are also welcome to join our event series.
Date/Time: April 29, 2022. 10:00AM-11:30AM
Speaker: Professor Soroush Saghafian (Harvard University)
Discussion: Machine Learning and Public Policy
Flyer: here
Date/Time: January 19, 2022. 10:00AM-11:30AM
Speaker: Dr Sean Taylor (Lyft)
Discussion: An Expansive View of Experimentation
Flyer: here
Recorded video is available: here
Speaker: Professor Xiao-Li Meng (Harvard University)
Discussion: From COVID-19 Testing to Election Prediction: How Small Are Our Big Data?
Video is available click here
Flyer: here
Speaker: Professor Alon Halevy (Facebook AI)
Discussion: Big Data, Machine Learning and AI for Preserving Integrity in Online Social Networks
Video is available click here
Flyer: here
Speaker: Professor Cynthia Rudin (Duke University)
Discussion: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Video is available click here
Flyer: here
Speaker: Professor Kosuke Uetake (Yale University)
Discussion: Big Data and Analytics for Managing Online Platform Market
Video is available click here
Flyer: here
Speaker: Professor Michael Littman (Brown University)
Discussion: Telling, Inspiring, Demonstrating, and Explaining: Getting Machines To Do What We Want
Video is available click here
Flyer: here
Speaker: Dr Sean Taylor (Lyft)
Discussion: An Expansive View of Experimentation
Video is available click here
Flyer: here
Speaker: Professor Xiao-Li Meng (Harvard University)
Discussion: From COVID-19 Testing to Election Prediction: How Small Are Our Big Data?
Flyer: here
Abstract: Professor Meng discussed the term “Big Data” emphasizes data quantity, not quality. He tackled important questions such as “What will be the effective sample size when we take into account the deterioration of data quality because of, for example, the selection bias in COVID-19 testing or the non-response bias in 2016 US Election polling results?” He provided an answer to such questions.
Speaker: Professor Matthew Harding (University of California, Irvine).
Discussion: Can AI replace high-skilled workers?
Flyer: here
Abstract: Professor Harding discussed how Artificial Intelligence (AI) can learn and replicate subjective judgements of high-skilled workers, a possible enabler for improving business efficiency, as well as his perspectives on how big data, and AI can create value in business. Discussion also explore practical limitations of managerial use of AI.
Speaker: Professor Alon Halevy (Facebook AI)
Discussion: Big Data, Machine Learning and AI for Preserving Integrity in Online Social Networks
Flyer: here
Abstract: Professor Halevy discussed how big data, AI and analytics can help us to Preserving Integrity in Online Social Networks. Through a survey came from the perspective of having to combat a broad spectrum of integrity violations at Facebook, Alon discussed a potential and current challenges of machine learning, AI and state-of-art tools.
Speaker: Professor Yasutora Watanabe (University of Tokyo)
Discussion: Big Data and Context-based Marketing
Flyer: here
Abstract: Professor Watanabe discussed how big data can be an enabler for understanding customer behaviour, particularly when contextual factors play an important role, as well as his perspectives on how analytics, big data, and AI can create value in business. After a high-level summary of context marketing, discussion focused on how to bring these research insights into managerial usages of AI, Big Data and Analytics.
Speaker: Professor Cynthia Rudin (Duke University)
Discussion: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Flyer: here
Abstract: With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions, including flawed bail and parole decisions in criminal justice. Explanations for black box models are not reliable, and can be misleading. If we use interpretable machine learning models, they come with their own explanations, which are faithful to what the model actually computes. Professor Rudin gave several reasons why we should use interpretable models, the most compelling of which is that for high stakes decisions, interpretable models do not seem to lose accuracy over black boxes.
Speaker: Professor Kosuke Uetake (Yale University)
Discussion: Big Data and Analytics for Managing Online Platform Market
Flyer: here
Abstract: Professor Uetake discussed how big data and analytics can help us to manage multi-sided online platform markets. Together with a high-level summary of key aspects in managing platform, practical recommendations and discussion were provided. Through big data analysis, Kosuke also shared new empirical findings on online platform management.
Speaker: Professor Michael Littman (Brown University)
Discussion: Telling, Inspiring, Demonstrating, and Explaining: Getting Machines To Do What We Want
Flyer: here
Abstract: These days, pretty much everything is a computer, resulting in devices that are powerful and flexible. But they are only useful if they carry out our wishes. Programming was invented as a mechanism for communicating the steps we want machines to carry out, but some tasks have proven difficult for even expert programmers to express. Machine learning provides a broader palette of techniques for conveying our intent to computers. Professor Littman covered several advances in machine learning from the perspective of how we can use them to better express our wishes to computers.
Speaker: Dr Steve Shwartz
Discussion: The Impact of AI on Society in the Coming Years
Flyer: here
Abstract: Dr Shwartz explained how AI works and why we do not need to worry about evil robots trying to exterminate us. He discussed how AI will impact society in many ways in the coming years, by covering AI-enhanced weapons of war, threats to our privacy, how AI can increase discrimination, and the impact of AI on employment.