AI for good initiatives often rely on the assumption that technical interventions can resolve complex social problems. In the context of human trafficking (HT), such techno-solutionism risks oversimplifying exploitation, reinforcing power imbalances and causing harm to the very communities AI claims to support. In this paper, we introduce the Radical Questioning (RQ) framework as a five step, pre-project ethical assessment tool to critically evaluate whether AI should be built at all, especially in domains involving marginalized populations and entrenched systemic injustice. RQ does not replace principles based ethics but precedes it, offering an upstream, deliberative space to confront assumptions, map power, and consider harms before design. Using a case study in AI for HT, we demonstrate how RQ reveals overlooked sociocultural complexities and guides us away from surveillance based interventions toward survivor empowerment tools. While developed in the context of HT, RQ's five step structure can generalize to other domains, though the specific questions must be contextual. This paper situates RQ within a broader AI ethics philosophy that challenges instrumentalist norms and centers relational, reflexive responsibility.

Real-world networks, with their evolving relations, are best captured as temporal graphs. However, existing software libraries are largely designed for static graphs where the dynamic nature of temporal graphs is ignored. Bridging this gap, we introduce TGX, a Python package specially designed for analysis of temporal networks that encompasses an automated pipeline for data loading, data processing, and analysis of evolving graphs. TGX provides access to eleven built-in datasets and eight external Temporal Graph Benchmark (TGB) datasets as well as any novel datasets in the .csv format. Beyond data loading, TGX facilitates data processing functionalities such as discretization of temporal graphs and node subsampling to accelerate working with larger datasets. For comprehensive investigation, TGX offers network analysis by providing a diverse set of measures, including average node degree and the evolving number of nodes and edges per timestamp. Additionally, the package consolidates meaningful visualization plots indicating the evolution of temporal patterns, such as Temporal Edge Appearance (TEA) and Temporal Edge Trafficc (TET) plots. The TGX package is a robust tool for examining the features of temporal graphs and can be used in various areas like studying social networks, citation networks, and tracking user interactions. We plan to continuously support and update TGX based on community feedback. TGX is publicly available on: https://github.com/ComplexData-MILA/TGX.

Time-of-flight-based ranging among transceivers with different clocks requires protocols that accommodate varying rates of the clocks. Double-sided two-way ranging (DS-TWR) is widely adopted as a standard protocol due to its accuracy; however, the precision of DS-TWR has not been clearly addressed. In this paper, an analytical model of the variance of DS-TWR is derived as a function of the user-programmed response delays, which is then compared to the Cramer-Rao Lower Bound (CRLB). This is then used to formulate an an optimization problem over the response delays in order to maximize the information gained from range measurements. The derived analytical variance model and optimized protocol are validated experimentally with 2 ranging UWB transceivers, where 29 million range measurements are collected.

You are free to share this article under the Attribution 4.0 International license. Artificial intelligence can help identify biases in news reporting that we wouldn't otherwise see, researchers report. For a new study, researchers got a computer program to generate news coverage of COVID-19 using headlines from Canadian Broadcast Corporation (CBC) articles as prompts. They then compared the simulated news coverage to the actual reporting at the time. The findings show that CBC coverage was less focused on the medical emergency and more positively focused on personalities and geo-politics.

AI can help identify biases in news reporting that we wouldn't otherwise see. Researchers from McGill University have recently directed a computer program to generate news coverage of COVID-19 using headlines from CBC articles as prompts. They then compared the simulated news coverage to the actual reporting at the time and found that CBC coverage was less focused on the medical emergency and more positively focused on personalities and geo-politics. "Reporting on real-world events requires complex choices, including decisions about which events and players take center stage. By comparing what was reported with what could have been reported, our study provides perspective on the editorial choices made by news agencies," says Professor Andrew Piper of the Department of Languages, Literatures, and Cultures at McGill University.

You are free to share this article under the Attribution 4.0 International license. Will artificial intelligence be a help or a hindrance in the response to climate change? In the journal Nature Climate Change, a team of experts in AI, climate change, and public policy present a framework for understanding the complex and multifaceted relationship of AI with greenhouse gas emissions and suggest ways to better align AI with climate change goals. "AI affects the climate in many ways, both positive and negative, and most of these effects are poorly quantified," says coauthor David Rolnick, assistant professor of computer science at McGill University and a core academic member of Mila – Quebec AI Institute. "For example, AI is being used to track and reduce deforestation, but AI-based advertising systems are likely making climate change worse by increasing the amount that people buy." "Climate change should be a key consideration when developing and assessing AI technologies," says Lynn Kaack, assistant professor of computer science and public policy at the Hertie School, and lead author of the report.

A study published last month in the peer-reviewed journal Nature Climate Change sought to understand the potential impact of artificial intelligence on climate change. AI has both positive and negative effects on the climate, according to study co-author David Rolnick, Assistant Professor of Computer Science at McGill University and a Core Academic Member of Mila – Quebec AI Institute. "AI affects the climate in many ways, both positive and negative, and most of these effects are poorly quantified," he explained. "For example, AI is being used to track and reduce deforestation, but AI-based advertising systems are likely making climate change worse by increasing the amount that people buy." "Climate change should be a key consideration when developing and assessing AI technologies," The researchers highlighted that engineers, policymakers and scientists can all contribute to using AI to achieve climate goals, McGill University noted. For instance, AI-based autonomous vehicles can help reduce carbon emissions if used for public transportation but could increase emissions if used for personal transportation.

The use artificial intelligence (AI) is exploding across the planet as it evolves into an essential tool for a myriad of fields and industries. But to successfully implement the technology into business operations, IT leaders need to surround themselves with global experts while building a state-of-the-art ecosystem. The place to start looking for such experts in Canada is Québec, the nation's AI powerhouse. Seventh in the world – that's where the province ranks in the Global AI Index published by the British firm Tortoise Media, a ranking of the most competitive countries in AI. Canada overall comes in fourth place – a remarkable achievement of which Québec is a real driving force.