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1. AI Reaches for the Stars

   https://doi.org/10.1162/DAED.a.979  

   Offner, Stella_S R (January 2026, Daedalus)

   Abstract Artificial intelligence is rapidly being integrated into a variety of tasks in astronomical research, from model fitting to anomaly detection. While AI offers huge potential to accelerate discoveries about the universe, its current capabilities and trustworthiness fall short. I discuss the road to full AI integration into astronomical workflows: why the field of astronomy is a particularly fertile ground for AI development, the potential revolutionary gains possible for scientific analysis and discovery, how AI developments within astronomy can accelerate AI advancements more broadly, and the current roadblocks to fully AI-integrated astronomy. 

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2. Who is Responsible, the Advisor or the AI? Understanding the Effects of Advisors Disclosing Their AI Use on Their Perceived Responsibility and AI Reliance

   https://doi.org/10.1145/3757524  

   Mendel, Tamir; Mandal, Soumik; Nov, Oded; Wiesenfeld, Batia Mishan (October 2025, Proceedings of the ACM on Human-Computer Interaction)

   Human advisors increasingly have access to AI recommendations and use them to shape the advice they give clients, often without disclosing their AI use to their clients. Disclosure of AI use involves informing individuals when AI is being used on their behalf by another person, such as disclosing to clients that advisors are using AI to formulate their expert advice. Our study aims to investigate whether and how disclosing advisors' use of AI assistance to their clients influences the advisors' perceived responsibility for decisions and degree of AI reliance. Recruiting financial advisors to perform a personal finance advising simulation that manipulated whether clients would know that advisors used an AI system (disclosed vs. not disclosed), we found that financial advisors felt less responsible for their recommendations when they believed their use of AI assistance would be disclosed rather than undisclosed to clients. We also found that advisors' perceived personal responsibility was higher when their reliance on AI was lower. Advisors' perceived self-competence increased their perceived personal responsibility for investment decisions relative to AI, and their trust in AI decreased their perceived responsibility. We conclude by discussing how our findings can inform disclosure schemes for improved human-AI collaboration in advising. 

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3. Five tensions of artificial intelligence adoption for organ allocation: Applying the technology–organization–environment framework

   https://doi.org/10.1177/20552076261425344  

   Babaee, Amaneh; Shank, Daniel B; Canfield, Casey; Hall, Joely Grace; Lentine, Krista L; Randall, Henry; Schnitzler, Mark (February 2026, DIGITAL HEALTH)

   BackgroundThe US organ transplantation system is pursuing modernization of the allocation process through the integration of new technologies such as artificial intelligence (AI). However, the legal and ethical issues within the transplantation industry are still of concern. ObjectiveWe explore the opportunities and challenges for Organ Procurement Organizations (OPOs) to adopt AI. The US organ transplant system is a highly regulated industry yet open to innovation. MethodsTen structured interviews were conducted with OPO representatives using the Extended Technology, Organization, Environment (TOE) framework. ResultsOverall, we identified five core tensions in AI adoption: (1) misconceptions, (2) approach to training, (3) need for AI expertise, (4) impact of organization size, and (5) top-down versus bottom-up adoption viewpoints. First, some of the positive perceptions of AI, such as bias elimination, are related to misconceptions about what is technically possible. Second, some OPOs believed that using AI systems requires basic knowledge about the AI system, while others stated that AI should be intuitive and require no training. Third, they disagreed on whether it is necessary to add AI-experienced staff as part of an AI adoption strategy. Fourth, smaller OPOs may struggle to develop, maintain, and implement AI systems due to their limited resources, yet they are more nimble and able to pivot due to less bureaucracy. Fifth, there are competing visions for how AI should be adopted across OPOs nationwide, either top-down driven by regulatory requirements or bottom-up driven by performance expectations. ConclusionsOngoing work is needed to determine best practices for integrating AI in OPOs to support optimal organ use and expand transplant access for patients. The TOE framework highlights organization-level tensions that need to be addressed by the transplant sector for successful AI adoption and integration. 

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4. Integrating AI models into ecological research workflows: The case of terrestrial bioacoustics

   https://doi.org/10.1111/2041-210x.70133  

   Kitzes, Justin; Chronister, Lauren; Czarnecki, Chapin; Fiss, Cameron; Freeland‐Haynes, Louis; Goodman, Brooke D; Lapp, Sam; Lyon, R Patrick; Nossan, Hannah; Rhinehart, Tessa A; et al (February 2026, Methods in Ecology and Evolution)

   Abstract Data collected by autonomous sensors, including camera traps and acoustic recorders, have enormous potential to generate new scientific insights in ecology and related fields. Modern machine learning and AI classification methods are critical to analysing these often immense data streams. Accordingly, considerable effort has been dedicated to building AI models that accurately detect and classify species and events of interest in these data.These AI models, however, form only one part of a larger research framework that is needed to answer ecological questions using sensor data. We argue that a deep understanding of this research context is required to develop and apply appropriate AI models that can support scientific advances in ecology and evolution.In this manuscript, we contextualize the use of AI methods in autonomous biodiversity surveys, focusing on terrestrial bioacoustics as a case study, by discussing six sequential areas that together form a research project: hardware, field deployments, data management, detection and classification using AI and related models, statistical analysis and ecological insight. For each area, we briefly highlight several ways in which decisions made in that area can constrain, support or interact with the development and application of AI models.We conclude with several suggestions for better development and integration of AI models into ecological research, including the need for additional research at the interface of AI models and statistical analysis, the question of achieving human‐level performance with AI models and the sources of future methodological advances in AI for ecology. 

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5. The Data-Augmented, Technology-Assisted Medical Decision Making (DATA-MD) Curriculum: A Machine Learning and Artificial Intelligence Curriculum for Clinical Trainees

   https://doi.org/10.1097/ACM.0000000000006089  

   Wong, Andrew; Sussman, Jeremy; Price, Nicholson; Makar, Maggie; Li, Benjamin; Yang, Jun; James, Cornelius (January 2025, Academic Medicine)

   Abstract ProblemDespite the rapidly expanding role of artificial intelligence (AI) and machine learning (ML) in health care, a significant knowledge gap remains among clinicians in their ability to evaluate and use AI and ML tools. ApproachThe Data-Augmented, Technology-Assisted Medical Decision Making (DATA-MD) curriculum was developed to teach fundamental AI and ML concepts to clinical trainees. The curriculum contains 4 modules: (1) Introduction to AI/ML in Healthcare, (2) Epidemiology and Biostatistics in AI/ML, (3) Use of AI/ML to Augment Diagnostic Decisions, and (4) Ethical and Legal Considerations of AI/ML in Healthcare. The curriculum was piloted in May and June 2023 among University of Michigan internal medicine residents and delivered to 2 cohorts of 11 and 12 residents. All learners completed presession and postsession assessments on AI and ML knowledge before and after the curriculum and a retrospective pre-post survey to evaluate familiarity with AI and ML concepts, comfort with AI and ML literature appraisal, and attitudes toward AI and ML use. OutcomesTwenty of 23 learners (87%) completed the presession knowledge assessment before participating in the DATA-MD sessions, and all 23 learners completed the postsession knowledge assessment and retrospective pre-post survey. Median knowledge scores significantly improved for modules 1 to 3 (module 1: 2.5 to 3.0,P= .008; module 2: 1.0 to 2.0,P= .049; module 3: 2.0 to 3.0,P< .001) but not module 4 (2.0 before and after testing;P= .80). Learners reported increased confidence in their abilities to appraise the AI and ML literature and use AI and ML tools in future practice. Next StepsThe DATA-MD curriculum pilot demonstrates that a standardized AI and ML curriculum can improve trainees’ knowledge and attitudes about clinical AI and ML. Next steps include expansion to learners from different medical specialties, health professions, and academic institutions. 

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