Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Human-AI collaboration increasingly drives decision-making across industries, from medical diagnosis to content moderation. While AI systems promise efficiency gains by providing automated suggestions for human review, these workflows can trigger cognitive biases that degrade performance. We know little about the psychological factors that determine when these collaborations succeed or fail. We conducted a randomized experiment with 2,784 participants to examine how task design and individual characteristics shape human responses to AI-generated suggestions. Using a controlled annotation task, we manipulated three factors: AI suggestion quality in the first three instances, task burden through required corrections, and performance-based financial incentives. We collected demographics, attitudes toward AI, and behavioral data to assess four performance metrics: accuracy, correction activity, overcorrection, and undercorrection. Two patterns emerged that challenge conventional assumptions about human-AI collaboration. First, requiring corrections for flagged AI errors reduced engagement and increased the tendency to accept incorrect suggestions, demonstrating how cognitive shortcuts influence collaborative outcomes. Second, individual attitudes toward AI emerged as the strongest predictor of performance, surpassing demographic factors. Participants skeptical of AI detected errors more reliably and achieved higher accuracy, while those favorable toward automation exhibited dangerous overreliance on algorithmic suggestions. The findings reveal that successful human-AI collaboration depends not only on algorithmic performance but also on who reviews AI outputs and how review processes are structured. Effective human-AI collaborations require consideration of human psychology: selecting diverse evaluator samples, measuring attitudes, and designing workflows that counteract cognitive biases.

Neural Information Processing Systems http://nips.cc/

Summit Consortium a uthors and affiliations listed at end of file. Low - dose computed tomography (LDCT) imaging employed in lung cancer screening (LCS) programs is increasing in uptake worldwide. LCS programs herald a generational opportunity to simultaneously detect canc er and non - cancer - related early - stage lung disease. Yet these efforts are hampered by a shortage of radiologists to interpret scans at scale. Designed for broad accessibility and rapid adaptation, TANGERINE can be fine - tuned off the shelf for a wide range of disease - specific tasks with limited computational resources and training data. Relative to models trained from scratch, TANGERINE dem onstrates fast convergence during fine - tuning, thereby requiring significantly fewer GPU hours, and displays strong label efficiency, achieving comparable or superior performance with a fraction of fine - tuning data. Pretrained using self - supervised learni ng on over 98,000 thoracic LDCTs, including the UK ' s largest LCS initiative to date and 27 public datasets, TANGERINE achieves strong performance across 14 disease classification tasks, including lung cancer and multiple respiratory diseases, while general ising robustly across diverse clinical centres. By extending a masked autoencoder framework to 3D imaging, TANGERINE offers a scalable solution for LDCT analysis, departing from recent closed, resource - intensive models by combining architectural simplicity, public availability, and modest computational requirements. Its accessible, open - source lightweight design lays the foundation for rapid integration into next - generation medical imaging tools that could transform LCS initiatives, allowing them to pivot f rom a singular focus on lung cancer detection to comprehensive respiratory disease management in high - risk populations. National lung cancer screening (LCS) programs herald a generational opportunity to identify early pre - symptomatic disease phenotypes for some of the most common chronic respiratory diseases in the world. In contrast, LCS programmes afford the opportunity to detect preclinical stages of airways or interstitial lung damage, where imaging abnormalities are radiologically visible despite lung function tests remaining normal. Moreover, methods have often relied on patch - based approaches that risk losing contextual information and require prior knowledge of disease location for model development. These limit ations constrain the utility of such models in research and clinical environments, where computational resources are often limited. Hence, t here remains a pressing need for foundation models that are not only accurate and generalisable, but also lightweigh t, open - access, and computationally efficient - enabling fine - tuning with limited data and resources.

As social robots increasingly enter dementia care, concerns about deception, intentional or not, are gaining attention. Yet, how robotic design cues might elicit misleading perceptions in people with dementia, and how these perceptions arise, remains insufficiently understood. In this scoping review, we examined 26 empirical studies on interactions between people with dementia and physical social robots. We identify four key design cue categories that may influence deceptive impressions: cues resembling physiological signs (e.g., simulated breathing), social intentions (e.g., playful movement), familiar beings (e.g., animal-like form and sound), and, to a lesser extent, cues that reveal artificiality. Thematic analysis of user responses reveals that people with dementia often attribute biological, social, and mental capacities to robots, dynamically shifting between awareness and illusion. These findings underscore the fluctuating nature of ontological perception in dementia contexts. Existing definitions of robotic deception often rest on philosophical or behaviorist premises, but rarely engage with the cognitive mechanisms involved. We propose an empirically grounded definition: robotic deception occurs when Type 1 (automatic, heuristic) processing dominates over Type 2 (deliberative, analytic) reasoning, leading to misinterpretation of a robot's artificial nature. This dual-process perspective highlights the ethical complexity of social robots in dementia care and calls for design approaches that are not only engaging, but also epistemically respectful.

This study evaluates the biases in Gemini 2.0 Flash Experimental, a state-of-the-art large language model (LLM) developed by Google, focusing on content moderation and gender disparities. By comparing its performance to ChatGPT-4o, examined in a previous work of the author, the analysis highlights some differences in ethical moderation practices. Gemini 2.0 demonstrates reduced gender bias, notably with female-specific prompts achieving a substantial rise in acceptance rates compared to results obtained by ChatGPT-4o. It adopts a more permissive stance toward sexual content and maintains relatively high acceptance rates for violent prompts, including gender-specific cases. Despite these changes, whether they constitute an improvement is debatable. While gender bias has been reduced, this reduction comes at the cost of permitting more violent content toward both males and females, potentially normalizing violence rather than mitigating harm. Male-specific prompts still generally receive higher acceptance rates than female-specific ones. These findings underscore the complexities of aligning AI systems with ethical standards, highlighting progress in reducing certain biases while raising concerns about the broader implications of the model's permissiveness. Ongoing refinements are essential to achieve moderation practices that ensure transparency, fairness, and inclusivity without amplifying harmful content.

Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not conform to the norms are an indication that the rules of science governing the data are incomplete, and something new needs to be present to explain these unexpected outliers. The challenge of finding anomalies can be confounding since it requires codifying a complete knowledge of the known scientific behaviors and then projecting these known behaviors on the data to look for deviations. When utilizing machine learning, this presents a particular challenge since we require that the model not only understands scientific data perfectly but also recognizes when the data is inconsistent and out of the scope of its trained behavior. In this paper, we present three datasets aimed at developing machine learning-based anomaly detection for disparate scientific domains covering astrophysics, genomics, and polar science. We present the different datasets along with a scheme to make machine learning challenges around the three datasets findable, accessible, interoperable, and reusable (FAIR). Furthermore, we present an approach that generalizes to future machine learning challenges, enabling the possibility of large, more compute-intensive challenges that can ultimately lead to scientific discovery.

Machine Learning (ML) is driving a revolution in the way scientists design, develop, and deploy data-intensive software. However, the adoption of ML presents new challenges for the computing infrastructure, particularly in terms of provisioning and orchestrating access to hardware accelerators for development, testing, and production. The INFN-funded project AI_INFN ("Artificial Intelligence at INFN") aims at fostering the adoption of ML techniques within INFN use cases by providing support on multiple aspects, including the provision of AI-tailored computing resources. It leverages cloud-native solutions in the context of INFN Cloud, to share hardware accelerators as e ffec-tively as possible, ensuring the diversity of the Institute's research activities is not compromised. In this contribution, we provide an update on the commissioning of a Kubernetes platform designed to ease the development of GPU-powered data analysis workflows and their scalability on heterogeneous, distributed computing resources, possibly federated as Virtual Kubelets with the interLink provider.

In this paper, we propose a novel active illumination framework to enhance the performance of VO and V-SLAM algorithms in these challenging conditions. The developed approach dynamically controls a moving light source to illuminate highly textured areas, thereby improving feature extraction and tracking. Specifically, a detector block, which incorporates a deep learning-based enhancing network, identifies regions with relevant features. Then, a pan-tilt controller is responsible for guiding the light beam toward these areas, so that to provide information-rich images to the ego-motion estimation algorithm. Experimental results on a real robotic platform demonstrate the effectiveness of the proposed method, showing a reduction in the pose estimation error up to 75% with respect to a traditional fixed lighting technique. I. INTRODUCTION Vision-based pose estimation is one of the most widespread strategies to achieve mobile robot localization. Several effective Visual Odometry (VO) and Visual SLAM (V -SLAM) approaches have flourished in the last decades [1], and the recent emergence of visual-inertial techniques has shown even more impressive results [2], [3]. The effectiveness of VO and V -SLAM solutions depends on the capability to extract robust and highly-descriptive visual features.