Canada's Bill C-36 tackles AI privacy. In an era of artificial intelligence, deepfakes and data-driven decision-making, Canada is moving to revise its privacy laws through Bill C-36, the Protecting Privacy and Consumer Data Act. Announced in June, Bill C-36 is Canada's first major overhaul of private-sector privacy legislation in more than 25 years. The bill explicitly recognises privacy as a fundamental right and also aims to give children's personal information stronger protections, enhance deletion rights and require greater transparency where automated systems make significant decisions about people. The 18-year-old shooting suspect allegedly used ChatGPT before the attack. The victims' families are now suing OpenAI, stating the company's AI safety team identified violent prompts but did not alert law enforcement.

The Canadian province of British Columbia is preparing to sue OpenAI, alleging the US company failed to alert police after its staff internally flagged violent ChatGPT conversations linked to the person responsible for February's Tumbler Ridge mass shooting . Attorney General Niki Sharma announced Tuesday that the province has hired legal teams in British Columbia and California to "explore all legal avenues to hold OpenAI and its decision-makers accountable for its documented failure to notify law enforcement regarding explicit, flagged threats made by the perpetrator on the company's ChatGPT platform." The move stems from the February 10 attack in the remote mountain community of Tumbler Ridge, where authorities say 18-year-old Jesse Van Rootselaar killed their mother and half-brother before going to the Tumbler Ridge Secondary School and opening fire. Five children between the ages of 11 and 13 and one educator were killed at the school. Twenty-seven other people were wounded before Van Rootselaar died from what police described as a self-inflicted gunshot wound.

People participate in a march to protest against the opening of AI datacentres in Vancouver, Canada, on 27 June 2026. People participate in a march to protest against the opening of AI datacentres in Vancouver, Canada, on 27 June 2026. We can live without AI, but can we live without clean water? Readers respond to an article about Erin Brockovich's battle against datacentres and voice their fears for the environment What are the benefits obtained from AI's massive use of electricity and water ( 'We're up against forces that have all the money in the world': Erin Brockovich on her battle against AI datacentres, 29 June)? Analysis shows that the top four uses of AI are "therapy/companionship", "technical assistance and troubleshooting", "fun and nonsense", and "fan fiction and storytelling". AI use for therapy, and due to loneliness, appears not to reduce loneliness.

Common Neighbors (CNs) and their higher-order variants are important pairwise features widely used in state-of-the-art link prediction methods. However, existing methods often struggle with the repetition across different orders of CNs and fail to fully leverage their potential. We identify that these limitations stem from two key issues: redundancy and over-smoothing in high-order common neighbors. To address these challenges, we design orthogonalization to eliminate redundancy between different-order CNs and normalization to mitigate over-smoothing. By combining these two techniques, we propose Orthogonal Common Neighbor (OCN), a novel approach that significantly outperforms the strongest baselines by an average of 7.7% on popular link prediction benchmarks. A thorough theoretical analysis is provided to support our method. Ablation studies also verify the effectiveness of our orthogonalization and normalization techniques. Code is available at: https://github.com/qingpingmo/OCN

We introduce FreshStack, a holistic framework for automatically building information retrieval (IR) evaluation benchmarks by incorporating challenging questions and answers. FreshStack conducts the following steps: (1) automatic corpus collection from code and technical documentation, (2) nugget generation from community-asked questions and answers, and (3) nugget-level support, retrieving documents using a fusion of retrieval techniques and hybrid architectures. We use FreshStack to build five datasets on fast-growing, recent, and niche domains to ensure the tasks are sufficiently challenging. On FreshStack, existing retrieval models, when applied out-of-the-box, significantly underperform oracle approaches on all five domains, denoting plenty of headroom to improve IR quality. In addition, we identify cases where rerankers do not improve first-stage retrieval accuracy (two out of five domains) and oracle context helps an LLM generator generate a high-quality RAG answer. We hope FreshStack will facilitate future work toward constructing realistic, scalable, and uncontaminated IR and RAG evaluation benchmarks.

We resolve a 30-year-old open problem concerning the power of unlabeled data in online learning by tightly quantifying the gap between transductive and standard online learning. In the standard setting, the optimal mistake bound is characterized by the Littlestone dimension dof the concept class H(Littlestone, 1987). We prove that in the transductive setting, the mistake bound is at least Ω d . This constitutes an exponential improvement over previous lower bounds of Ω(loglog(d)), Ω p log(d), and Ω(log(d)), due respectively to Ben-David, Kushilevitz, and Mansour (1995, 1997), and Hanneke, Moran, and Shafer (2023). We also show that this lower bound is tight: for every d, there exists a class of Littlestone dimension d with transductive mistake bound O d . Our upper bound also improves upon the best known upper bound of (2/3) d from Ben-David et al. (1997). These results establish a quadratic gap between transductive and standard online learning, thereby highlighting the benefit of advance access to the unlabeled instance sequence. This contrasts with the PAC setting, where transductive and standard learning exhibit similar sample complexities.

We introduce DeCaFlow, a deconfounding causal generative model. Training once per dataset using just observational data and the underlying causal graph, DeCaFlow enables accurate causal inference on continuous variables under the presence of hidden confounders. Specifically, we extend previous results on causal estimation under hidden confounding to show that a single instance of DeCaFlow provides correct estimates for all causal queries identifiable with do-calculus, leveraging proxy variables to adjust for the causal effects when do-calculus alone is insufficient. Moreover, we show that counterfactual queries are identifiable as long as their interventional counterparts are identifiable, and thus are also correctly estimated by DeCaFlow. Our empirical results on diverse settings--including the Ecoli70 dataset, with 3 independent hidden confounders, tens of observed variables and hundreds of causal queries--show that DeCaFlow outperforms existing approaches, while demonstrating its out-of-the-box applicability to any given causal graph.

Submodular maximization subject to a p-matchoid constraint has various applications in machine learning, particularly in tasks such as feature selection, video and text summarization, movie recommendation, graph-based learning, and constraintbased optimization. We study this problem in the dynamic setting, where a sequence of insertions and deletions of elements to a p-matchoid M(V,I) occurs over time and the goal is to efficiently maintain an approximate solution. We propose a dynamic algorithm for non-monotone submodular maximization under a p-matchoid constraint. For a p-matchoid M(V,I) of rank k, defined by a collection of m matroids, our algorithm guarantees a (2p +2 p p(p +1) +1 +ϵ)-approximate solution at any time t in the update sequence, with an expected amortized query complexity of O(ϵ 3 pk4 log2(k)) per update.

Graph embeddings play a critical role in graph representation learning, allowing machine learning models to explore and interpret graph-structured data. However, existing methods often rely on opaque, high-dimensional embeddings, limiting interpretability and practical visualization. In this work, we introduce Topological Evolution Rate (TopER), a novel, lowdimensional embedding approach grounded in topological data analysis.

Selective classifiers improve model reliability by abstaining on inputs the model deems uncertain. However, few practical approaches achieve the gold-standard performance of a perfect-ordering oracle that accepts examples exactly in order of correctness. Our work formalizes this shortfall as the selective-classification gap and present the first finite-sample decomposition of this gap to five distinct sources of looseness: Bayes noise, approximation error, ranking error, statistical noise, and implementation-or shift-induced slack. Crucially, our analysis reveals that monotone post-hoc calibration--often believed to strengthen selective classifiers--has limited impact on closing this gap, since it rarely alters the model's underlying score ranking. Bridging the gap therefore requires scoring mechanisms that can effectively reorder predictions rather than merely rescale them. We validate our decomposition on synthetic two-moons data and on real-world vision and language benchmarks, isolating each error component through controlled experiments. Our results confirm that (i) Bayes noise and limited model capacity can account for substantial gaps, (ii) only richer, feature-aware calibrators meaningfully improve score ordering, and (iii) data shift introduces a separate slack that demands distributionally robust training. Together, our decomposition yields a quantitative error budget as well as actionable design guidelines that practitioners can use to build selective classifiers which approximate ideal oracle behavior more closely.