- Wed, Jan 29, 2020
Ethics at Noon
Josée Johnston & Shyon Baumann, What is “Good” Food? How Foodies Negotiate Status and Ethics in Food Choices (Ethics@Noon)What Is “Good” Food? How Foodies Negotiate Status and Ethics in Food Choices
How do foodies decide what is “good” food — food worthy of eating, discussing, researching and photographing? This talk will draw from research on foodies to discuss the ways that foods become venerated as high status choices. Our research charts the rise of an omnivorous food culture that values high-class cuisine alongside street-stand tacos and diner meals. Besides identifying key markers of foodie status, we will discuss how food choices relate to ethical consumption deals like democratic openness, multiculturalism, and sustainability. Drawing from a survey with Toronto-based food shoppers, we explore the intersection of foodie culture and ethical consumption. This data suggests the emergence of a high-status foodie who appreciates the finest, most delicious foods, while also seeking to feel ethically virtuous at the dining table.
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Shyon Baumann
University of Toronto
SociologyJosée Johnston
12:30 PM - 02:00 PM
University of Toronto
Sociology
Centre for Ethics, University of Toronto
200 Larkin - Wed, Jan 29, 2020
Ethics of AI in Context: Emerging Scholars
Vinyas Harish & Nuwan Perera, Machine Learning for Health at the Public-Private Boundary: Questions to Consider (Ethics of AI in Context: Emerging Scholars)Machine Learning for Health at the Public-Private Boundary: Questions to Consider
Recent advances in artificial intelligence (AI) have spurred much interest and investment in the ‘disruption’ of healthcare. Major breakthroughs in numerous areas of machine learning (ML) are leading to the creation of ‘decision support systems’ which promise to aid physicians throughout the trajectory of a patient’s care. Numerous major technology companies (e.g. Microsoft, Apple, Google) have identified healthcare as an untapped opportunity and key vertical for their business. As seen with recent headlines in the media (e.g. Google’s Project Nightingale), we propose that ML for health at the public-private boundary brings forward unique ethical considerations not seen with other technologies. There is limited academic literature to inform decision-making by non-technical stakeholders and a lack of clarity around the practical discussion around what should be done when considering a [public-private?] partnership in this space. We provide a series of questions to guide the framework around making responsible decisions when partnering with private sector to design and deploy AI/ML solutions for health.
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Vinyas Harish
University of Toronto
Medicine and Public Health
Nuwan Perera
04:00 PM - 05:30 PM
Software Engineer
integrate.ai
Centre for Ethics, University of Toronto
200 Larkin