The government has been accused of a lack of transparency around use of algorithms and AI in decisions. The government has been accused of a lack of transparency around use of algorithms and AI in decisions. Albanese government warned it's'sleepwalking into another robodebt scenario' over use of algorithms and AI The federal government is "sleepwalking its way into another robodebt" scandal amid a lack of transparency and legislation around use of algorithms and artificial intelligence in decisions, the chief executive of independent thinktank the Centre for Public Integrity said. Dr Catherine Williams launched the centre's "Computer Says No" report on Thursday, which examines the use of automated decision-making (ADM) tools by the government and calls for urgent reform. Unreasonable and unfair: damning investigation criticises Labor's algorithm-based tool for aged care The centre is concerned Australians may not know whether ADM has been used in decisions affecting them.

Experts using AI overwhelmingly defer to the technology when making decisions, research by Claude creator Anthropic suggests. In a research paper published on Tuesday, Anthropic said it asked 50 professional analysts to advise a fictional farming cooperative to determine whether experts make decisions themselves when using AI. Participants were asked to make four choices between two defensible options before making a final policy recommendation. Unbeknownst to the analysts, Anthropic directed each participant's AI assistant to lean towards to a particular decision to measure whether the analyst's decision-making was being influenced. Analysts chose options that matched the AI assistant's preference 86 percent of the time, compared with a 50/50 outcome that would be expected if they were not influenced, according to the study.

AI is becoming part of everyday decision-making in many organisations. In healthcare, it can help professionals analyse large amounts of information, identify patterns and assess complex cases. But introducing AI does more than add a new tool. It can also change what people pay attention to, how they understand a problem and how a decision is reached. This is what Bijona wanted to understand in her doctoral research.

Simplifying and integrating enterprise technology can help companies move faster, respond to disruptions, and build a stronger foundation for AI, says Harish Manohar, SAP IT Director at Jabil. As companies scale, the technology supporting operations can become a liability just as quickly as it becomes an asset. Disconnected systems, site-specific tools, spreadsheets, and manual workarounds can create data silos that make it harder to spot problems early, coordinate responses, and make decisions with confidence. For Jabil, a global manufacturing company with more than 100 sites across more than 30 countries, the answer has been to make integration and simplification a priority. The company adopted a "simplify-first, then-innovate mindset," says Harish Manohar, SAP IT director at Jabil, recognizing that adding new technologies without first reducing complexity risks creating more risk. The goal is to standardize processes, consolidate where possible, and establish a more consistent data backbone across the organization. "Any innovation without simplification is going to add more complexity," Manohar says. That philosophy also changes how Jabil approaches modernization. "Any modernization or transformation should add measurable business value," Manohar says. The company is focused on connecting processes end-to-end across its supply chain and creating a foundation that can scale consistently across regions.

CogTwin is a hybrid cognitive architecture framework designed to bring autonomous reasoning and real-time adaptation to digital twin systems. Presented at IJCAI 2025, this work aims to advance the state of digital twin technology by addressing key gaps in autonomy, cognition, and real-time decision-making. Digital twin technology has transformed how complex systems are managed, from smart cities to industrial processes. However, most current digital twins remain fundamentally reactive: they rely on pre-programmed rules and static data-driven models, and therefore struggle when confronted with unforeseen events or evolving conditions. Real-time learning, reasoning, and adaptation - hallmarks of human cognition - are largely absent.

Federal departments and agencies still use automated decision-making in some service delivery functions, prompting fears AI systems could replace more human-led processes. Federal departments and agencies still use automated decision-making in some service delivery functions, prompting fears AI systems could replace more human-led processes. The use of AI in automated decision-making by government departments and agencies will be subject to tough rules under a new national plan, expected to extend to consumer protections, workplace safety and privacy. As the Albanese government grapples with the rapid growth in the use of artificial intelligence and a boom in datacentre construction, senior ministers have begun work to draw up new rules to ensure safety is built into AI processes inside government, prioritising fairness, accuracy and transparency. Federal departments and agencies continue to rely on automated decision-making in some service delivery functions, prompting fears increasingly sophisticated AI systems could replace more human-led processes.

To alleviate the reliance on costly 3D training data, recent studies have explored zero-shot 3DVG by leveraging the extensive knowledge and powerful reasoning capabilities of pre-trained LLMs and VLMs. However, existing paradigms tend to emphasize either spatial (3D-based) or semantic (2D-based) understanding, limiting their effectiveness in complex real-world applications. In this work, we introduce SPAZER -- a VLM-driven agent that combines both modalities in a progressive reasoning framework. It first holistically analyzes the scene and produces a 3D rendering from the optimal viewpoint. Based on this, anchor-guided candidate screening is conducted to perform a coarse-level localization of potential objects. Furthermore, leveraging retrieved relevant 2D camera images, 3D-2D joint decision-making is efficiently performed to determine the best-matching object. By bridging spatial and semantic reasoning neural streams, SPAZER achieves robust zero-shot grounding without training on 3D-labeled data. Extensive experiments on ScanRefer and Nr3D benchmarks demonstrate that SPAZER significantly outperforms previous state-of-the-art zero-shot methods, achieving notable gains of 9.0% and 10.9% in accuracy. Our codes are available at https://github.com/JZ-9962/SPAZER.

Multi-modal learning has emerged as a key technique for improving performance across domains such as autonomous driving, robotics, and reasoning. However, in certain scenarios, particularly in resource-constrained environments, some modalities available during training may be absent during inference. While existing frameworks effectively utilize multiple data sources during training and enable inference with reduced modalities, they are primarily designed for single-agent settings. This poses a critical limitation in dynamic environments such as connected autonomous vehicles (CAV), where incomplete data coverage can lead to decisionmaking blind spots. Conversely, some works explore multi-agent collaboration but without addressing missing modality at test time. To overcome these limitations, we propose Collaborative Auxiliary Modality Learning (CAML), a novel multi-modal multi-agent framework that enables agents to collaborate and share multi-modal data during training, while allowing inference with reduced modalities during testing. Experimental results in collaborative decision-making for CAV in accident-prone scenarios demonstrate that CAML achieves up to a 58.1%improvement in accident detection.

Large Language Models (LLMs) are increasingly being explored across a range of reasoning tasks. However, LLMs sometimes struggle with reasoning tasks under uncertainty that are relatively easy for humans, such as planning actions in stochastic environments. The adoption of LLMs for reasoning is impeded by uncertainty challenges, such as LLM uncertainty and environmental uncertainty. LLM uncertainty arises from the stochastic sampling process inherent to LLMs. Most LLM-based Decision-Making (LDM) approaches address LLM uncertainty through multiple reasoning chains or search trees. However, these approaches overlook environmental uncertainty, which leads to poor performance in environments with stochastic state transitions.

Decision-making in various fields, such as medicine, is heavily based on conditional average treatment effects (CATEs). Practitioners commonly make decisions by checking whether the estimated CATE is positive, even though the decision-making performance of modern CATE estimators (meta-learners) is poorly understood. In this paper, we study optimal decision-making based on two-stage meta-learners (e.g., DR-learner), which estimate CATE via a second-stage regression. We show that these meta-learners can be suboptimal when used for decision-making in common settings where the second-stage regression is over a restricted function class (e.g., when using regularization or employing fairness/interpretability constraints). Intuitively, this occurs because such estimators prioritize CATE accuracy in regions far away from the decision boundary, which is ultimately irrelevant to decision-making. As a remedy, we propose a novel two-stage learning objective that re-targets the CATE to balance CATE estimation error and decision performance. We then propose a neural method that optimizes an adaptively-smoothed approximation of our learning objective. Finally, we confirm the effectiveness of our method both empirically and theoretically.