November 11 at 1:00pm<\/strong><\/p>\n XAVIER ST-DENIS –\u00a0<\/strong>Assistant Professor, Institut national de la recherche scientifique (INRS) Artificial Intelligence is increasingly used by firms for human resources functions, such as hiring,\u00a0employee retention policies, and performance assessment. Many use cases are seen as problematic because of\u00a0the black box nature of many AI algorithms. One source of concern is the use of data based on past decisions\u00a0and assessments made by humans to train algorithms. This is seen as a possible source of bias in AI. In the\u00a0context of COVID-19, the use of AI in the workplace is faced by an additional challenge: algorithms are trained\u00a0on pas data to formulate predictions. However, the COVID-19 pandemic resulted in important disruptions in all\u00a0aspects of professional life. Will this compromise the ability of AI to formulate accurate predictions in the short\u00a0term? This presentation will be a brief introduction to the use of AI in the workplace, with discussions of\u00a0implications of the COVID-19 pandemic.<\/p>\n November 11 at 2:30 pm<\/strong><\/p>\n CHARLES C. ONU<\/strong> (Join this presentation through MS Teams) – Machine Learning Researcher & Vanier Doctoral Scholar, Mila & McGill University Hearing the words “artificial intelligence” has almost become a daily occurrence. What really does the\u00a0pursuit of artificial intelligence involve? What does it mean to build AI? How can this play an important role in\u00a0social good, and specifically, in providing access to healthcare for millions? We will answer these questions\u00a0during this talk.<\/p>\n November 11 at 4:00 pm<\/strong><\/p>\n MARTIN GIBERT –\u00a0<\/strong>Ethics of AI Research Officer, Centre de recherche en \u00e9thique (CR\u00c9) & l\u2019Institut de valorisation des donn\u00e9es\u00a0(IVADO) This talk will propose a map of the main ethical issues raised by recent developments in artificial\u00a0intelligence: the existential risk of a hostile AI, human replacement, surveillance, and manipulation. It will also\u00a0discuss the best way to program robots, such as autonomous cars or chatbots.<\/p>\n November 13 at 11:00 am<\/strong><\/p>\n SAMEER BHATNAGAR –\u00a0<\/strong>Physics Teacher, Dawson College This talk will be a brief overview of how Machine Learning techniques are applied to different systems\u00a0in education. Latent Skill Estimation, Automatic Essay Scoring, Personalized November 13 at 1:00 pm<\/strong><\/p>\n LLANA JAMES –\u00a0<\/strong>Public Intellectual & Doctoral Candidate, Faculty of Medicine, University of Toronto What training, education, and critical thinking skills do learners need as they contemplate the age of AI\u00a0and the future of work? We know that AI will be part of every aspect of life. How can we make ethical choices in\u00a0this shifting landscape? What are we really dealing with, and what principles can best guide us?<\/p>\n
\nTitle:<\/strong> The use of AI in hiring: Understanding algorithmic bias, fairness, and other challenges<\/strong><\/p>\n
\nTitle: What is AI and how can it be used for good?<\/strong><\/p>\n
\nTitle: An introduction to the ethics of artificial intelligence<\/strong><\/p>\n
\nTitle: Applications of machine learning in education<\/strong><\/p>\n
\nContent Recommendation and Feedback, Early Identification of Students at Risk: these are all examples of how\u00a0some core ML techniques are being used at scale today in different learning platforms.<\/p>\n
\nTitle: Hype vs. reality in an AI world: A practical guide to the future<\/strong><\/p>\n
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