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Among explainability techniques, SHAP stands out as one of the most popular, but often overlooks the causal structure of the problem. In response, do-SHAP employs interventional queries, but its reliance on estimands hinders its practical application. To address this problem, we propose the use of estimand-agnostic approaches, which allow for the estimation of any identifiable query from a single model, making do-SHAP feasible on complex graphs. We also develop a novel algorithm to significantly accelerate its computation at a negligible cost, as well as a method to explain inaccessible Data Generating Processes. We demonstrate the estimation and computational performance of our approach, and validate it on two real-world datasets, highlighting its potential in obtaining reliable explanations.

This position paper argues that the theoretical inconsistency often observed among Responsible AI (RAI) metrics, such as differing fairness definitions or trade-offs between accuracy and privacy, should be embraced as a valuable feature rather than a flaw to be eliminated. We contend that navigating these inconsistencies, by treating metrics as divergent objectives, yields three key benefits: (1) Normative Pluralism: maintaining a full suite of potentially contradictory metrics ensures that the diverse moral stances and stakeholder values inherent in RAI are adequately represented; (2) Epistemological Completeness: using multiple, sometimes conflicting, metrics captures multifaceted ethical concepts more fully and preserves greater informational fidelity than any single, simplified definition; (3) Implicit Regularization: jointly optimizing for theoretically conflicting objectives discourages overfitting to any one metric, steering models toward solutions with better generalization and robustness under real-world complexities.

However, their widespread adoption is hindered by critical limitations, such as low information transfer rates and extensive user-specific calibration. To overcome these challenges, recent research has explored the integration of Large Language Models (LLMs), extending the focus from simple command decoding to understanding complex cognitive states. Despite these advancements, deploying agentic AI faces technical hurdles and ethical concerns. Due to the lack of comprehensive discussion on this emerging direction, this position paper argues that the field is poised for a paradigm extension from BCI to Brain-Agent Collaboration (BAC). We emphasize reframing agents as active and collaborative partners for intelligent assistance rather than passive brain signal data processors, demanding a focus on ethical data handling, model reliability, and a robust human-agent collaboration framework to ensure these systems are safe, trustworthy, and effective.

A freelance HR consultant paid Garfield AI about £400 to send a legal letter and then issue court proceedings over an unpaid debt of £7,000. A freelance HR consultant paid Garfield AI about £400 to send a legal letter and then issue court proceedings over an unpaid debt of £7,000. Barrister who was given material produced by Garfield AI says advocacy at trial'remained fundamentally human' An artificial intelligence law firm has won a case in an English court, in what is believed to be the first time a trial has been won using an AI lawyer. A freelance HR consultant, Tamires Camal Taquidir, paid the firm, Garfield AI, about £400 to send a legal letter and then issue court proceedings over an unpaid debt of £7,000. The co-founder of Garfield, Philip Young, called it a "landmark moment" for access to justice and said many small businesses have had to write off debts because the cost of litigation outweighed the money they could hope to win.

Three things to watch amid Anthropic's latest feud with the government Anthropic's standoff with Washington has already raised new questions about AI safety and sovereignty--and about Chinese competition. For those of you enjoying your summer unaware of Anthropic's latest feud with the US government, here's a recap: In April the company said it had built an AI model called Mythos that was so good at working with code it could pose a global cybersecurity threat. Anthropic gave access to a small group of cybersecurity experts so they could see what they were up against. Then it released a modified version called Fable which it said was safer to the public on Tuesday, June 9. That Friday, the federal government told the company it was a threat to national security and placed export controls on the new release. Anthropic revoked access to both models hours later.

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OpenAI Launches Full-Scale Effort to Patch Open-Source Bugs as It Takes on Anthropic's Mythos Amid concerns about AI models' cybersecurity capabilities, OpenAI revealed an improved version of GPT-5.5-Cyber and its "Patch the Planet" initiative to fix open-source software bugs. As fears about AI hacking capabilities grow, OpenAI on Monday made a slew of cybersecurity-focused announcements, including an improved version of its limited-access security-specialized model GPT-5.5-Cyber, As advances across the AI industry leave critical open-source projects at increasing risk of falling behind, though, the company also said on Monday that it is launching an effort known as Patch the Planet, founded with the prominent research-focused security firm Trail of Bits and in collaboration with vulnerability management firms HackerOne and Calif. The project has already begun its work offering free security consulting services to open source maintainers to not only help them find and patch vulnerabilities, but also support them in strengthening their code bases and incorporating AI security tools into their development process. The idea is to give individualized support to as many open-source projects as possible to improve both their current security and long-term resilience in a way that will actually be sustainable.

As machine learning (ML) algorithms are increasingly used in medical image analysis, concerns have emerged about their potential biases against certain social groups. Although many approaches have been proposed to ensure the fairness of ML models, most existing works focus only on medical image diagnosis tasks, such as image classification and segmentation, and overlooked prognosis scenarios, which involve predicting the likely outcome or progression of a medical condition over time. To address this gap, we introduce FairTTE, the first comprehensive framework for assessing fairness in time-to-event (TTE) prediction in medical imaging. FairTTE encompasses a diverse range of imaging modalities and TTE outcomes, integrating cutting-edge TTE prediction and fairness algorithms to enable systematic and fine-grained analysis of fairness in medical image prognosis. Leveraging causal analysis techniques, FairTTE uncovers and quantifies distinct sources of bias embedded within medical imaging datasets. Our large-scale evaluation reveals that bias is pervasive across different imaging modalities and that current fairness methods offer limited mitigation. We further demonstrate a strong association between underlying bias sources and model disparities, emphasizing the need for holistic approaches that target all forms of bias. Notably, we find that fairness becomes increasingly difficult to maintain under distribution shifts, underscoring the limitations of existing solutions and the pressing need for more robust, equitable prognostic models.

The claim that the AI community, or society at large, should'democratize AI' has attracted considerable critical attention and controversy. Two core problems have arisen and remain unsolved: conceptual disagreement persists about what democratizing AI means; normative disagreement persists over whether democratizing AI is ethically and politically desirable. We identify eight common AI democratization traps: democratization-skeptical arguments that seem plausible at first glance, but turn out to be misconceptions. We develop arguments about how to resist each trap. We conclude that, while AI democratization may well have drawbacks, we should be cautious about dismissing AI democratization prematurely and for the wrong reasons. We offer a constructive roadmap for developing alternative conceptual and normative approaches to democratizing AI that successfully avoid the traps.