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Data is the foundation of AI. Poor-quality data drive up costs and can lead to hidden problems for AI models, especially in complex fields such as healthcare and manufacturing. Meanwhile, biased data negatively affect the performance of AI models, and untested evaluation datasets can result in false positives or overestimates of model accuracy. For data publishers to realize their true potential in supporting the AI ecosystem and its impacts, they should take measures to ensure that their datasets support AI practitioners' needs; in other words, their data should be made AI-ready. In this article, we present a framework for data publishers to follow to make their datasets AI-ready. The framework provides specific, actionable guidance based on previous work and experience at the Open Data Institute and augmented with insights from literature and discussions with a range of experts. We first define AI-ready data before briefly discussing a selection of frameworks in the literature and where they are insufficient. We then provide a visual snapshot of our framework for AI-ready data, and a subsequent in-depth discussion of its criteria. Finally, we demonstrate the usage of our framework with a number of example datasets. We conclude by discussing the further steps that should be taken for the entire open data ecosystem to be made AI-ready in order to realize its true potential in supporting an innovative future.

As a field, web development is roughly 30 years old, and during this period, it has been transformed several times already as it has moved from static websites to dynamic web applications. Now, with the introduction of Artificial Intelligence (AI), the field is again at the cusp of a transformation as the latest AI tools might change how to develop for the web yet again. The objective of this study is to look into this phenomenon and understand how AI is changing web development. To achieve this task, we chose to use the sequential qualitative–quantitative design method that combines interviews with a survey to validate and expand our findings from the interviews. We found that AI is used by web developers to increase their development efficiency, as even the current tools are easy to use and access, although they come with several minor downsides, including AI not being able to understand complex logic, the need for validation of AI output, and suggested code that could potentially lead to security issues. While there are clear benefits to using AI tools for web development and AI proficiency is a vital skill for web developers, there are still open questions related to the quality of code produced by AI tools.

This study explores the role of artificial intelligence (AI) in perception management as an emerging tool of political soft power. Drawing on the theoretical frameworks of social psychology, strategic communication, and political communication, the research investigates how AI-assisted strategies influence public perception, image, and trust in the context of modern statecraft. The study adopts a qualitative design based on semi-structured interviews with 16 experts—eight from psychology and eight from communication fields—selected through snowball sampling. Data were analyzed using qualitative content analysis to identify recurring patterns and thematic structures. The findings reveal four central themes: (1) AI enhances efficiency and precision in perception campaigns, (2) trust and credibility remain critical yet vulnerable dimensions, (3) ethical and governance dilemmas emerge in AI-mediated communication, and (4) human oversight continues to be essential for maintaining legitimacy. The results suggest that while AI strengthens states’ capacity for strategic influence, overreliance without transparency may undermine the very trust it seeks to build. The study contributes to soft power and communication scholarship by providing expert-based evidence on the psychological and strategic mechanisms of AI-driven perception management. Policy recommendations are offered to promote transparency, accountability, and ethical oversight in AI-enabled diplomatic practices.

Machine learning (ML) is becoming prevalent in embedded AI sensing systems. These “ML sensors” enable context-sensitive, real-time data collection and decision-making across diverse applications ranging from anomaly detection in industrial settings to wildlife tracking for conservation efforts. As such, there is a need to provide transparency in the operation of such ML-enabled sensing systems through comprehensive documentation. This is needed to enable their reproducibility, to address new compliance and auditing regimes mandated in regulation and industry-specific policy, and to verify and validate the responsible nature of their operation. To address this gap, we introduce the datasheet for ML sensors framework. We provide a comprehensive template, collaboratively developed in academia—industry partnerships, that captures the distinct attributes of ML sensors, including hardware specifications, ML model and dataset characteristics, end-to-end performance metrics, and environmental impacts. Our framework addresses the continuous streaming nature of sensor data, real-time processing requirements, and embeds benchmarking methodologies that reflect real-world deployment conditions, ensuring practical viability. Aligned with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability), our approach enhances the transparency and reusability of ML sensor documentation across academic, industrial, and regulatory domains. To show the application of our approach, we present two datasheets: the first for an open-source ML sensor designed in-house and the second for a commercial ML sensor developed by industry collaborators, both performing computer vision-based person detection.

Generalist robot models promise broad applicability across domains but currently require extensive expert demonstrations for task specialization, which is a costly and impractical barrier for real-world deployment. In this article, which summarizes the author's presentation in the New Faculty Highlights Track of the 39^th annual AAAI Conference on Artificial Intelligence, we present algorithms that enable non-expert users to adapt and continually improve robot policies through natural and lightweight feedback modalities, such as preference comparisons, rankings, ratings, natural language, and users' own demonstrations, combining them with active learning strategies to maximize data-efficiency. We further introduce methods for leveraging real-time human interventions as rich training signals, modeling both their timing and absence to refine policies continually. Our approaches achieve substantial gains in sample-efficiency, adaptability, and user-friendliness, demonstrated across simulated and real-world robotic tasks. By aligning robot learning with how humans naturally teach, we hope to move toward autonomous systems that are more personalized, capable, and deployable in everyday environments.

For more than a decade, scientists have been striving to make their datasets available in open repositories, with the goal that they be findable, accessible, interoperable, and reusable (FAIR). Although it is hard for most investigators to remember all the “guiding principles” associated with FAIR data, there is one overarching requirement: The data need to be annotated with “rich,” discipline-specific, standardized metadata that can enable third parties to understand who performed the experiment, who or what the subjects were, what the experimental conditions were, and what the results appear to show. Most areas of science lack standards for such metadata and, when such standards exist, it can be difficult for investigators or data curators to apply them. The Center for Expanded Data Annotation and Retrieval (CEDAR) builds technology that enables scientists to encode descriptive metadata standards as templates that enumerate the attributes of different kinds of experiments and that link those attributes to ontologies or value sets that may supply controlled values for those attributes. These metadata templates capture the preferences of groups of investigators regarding how their data should be described and what a third party needs to know to make sense of their datasets. CEDAR templates describing community metadata preferences have been used to standardize metadata for a variety of scientific consortia. They have been used as the basis for data-annotation systems that acquire metadata through Web forms or through spreadsheets, and they can help correct metadata to ensure adherence to standards. Like the declarative knowledge bases that underpinned intelligent systems decades ago, CEDAR templates capture the knowledge of a community of practice in symbolic form, and they allow that knowledge to be applied in a variety of settings. They provide a mechanism for scientific communities to create shared metadata standards and to encode their preferences for the application of those standards, and for deploying those standards in a range of intelligent systems to promote open science.

Will AI replace social scientists? The real issue concerns reshaping rather than replacement. Confronting the integration of large language models (LLMs) into academic training establishes “prompt engineering” as the core interface for collaboration, defining it as a method to translate sociological thinking into precise instructions. LLMs are becoming essential partners across the research spectrum. They transform qualitative analysis from a solitary craft into a dialogical coding process and assist in theoretical localization and the construction of localized measurement scales. Beyond text analysis, they provide a low-cost virtual testbed for experimental design through “silicon samples” and enable the deduction of complex social interactions via “generative agents.” In the quantitative realm, they act as translators connecting research intentions with statistical code. Ultimately, the core challenge facing researchers is not technical. It lies in proactively cultivating a critical “literacy for human-AI collaboration” to master this paradigm shift.

Generative AI's growing use in higher education research requires strong protocols for responsible use. This need arises from the potential for misuse and the current uncertainty around ethical concerns and intellectual property. The lack of clear rules about openness in AI use, along with the “black box” nature of many AI systems, raises worries about reproducibility and the possibility of biased or fake results. This paper focuses specifically on generative AI tools (e.g., LLMs like ChatGPT, research-specific platforms like Elicit/SciSpace). The paper presents the ETHICAL protocol (i.e., Establish your purpose, Thoroughly explore options, Harness the appropriate tool, Inspect and verify output, Cite and reference accurately, Acknowledge AI usage transparently, and Look over publisher's guidelines), a detailed guide designed to direct researchers in the ethical and responsible inclusion of generative AI in their work. The protocol was created through a multi-step process, including a scientometric review of current trends, a systematic review of researcher experiences, and a policy analysis of 74 documents from various stakeholders (authorities, universities, publishers, and publication manuals). This analysis shaped the creation of a seven-heading, nine-item checklist covering key aspects of responsible AI use, from setting clear research goals to checking outputs and openly acknowledging AI help. The ETHICAL protocol gives practical examples and detailed explanations for each item, highlighting the importance of AI literacy and careful choice of suitable tools. It also stresses the vital need for checking AI-generated content to lessen the risk of errors and made-up information (“hallucinations”). The resulting protocol offers a practical and easy-to-use guide for researchers, encouraging responsible AI practices and upholding academic integrity. The ETHICAL protocol offers a helpful tool for managing the complex area of AI in research, ultimately leading to more open, reliable, and ethically sound scholarly work. Its broad acceptance could greatly improve the responsible use of AI in higher education, building trust and furthering knowledge growth.

Proportional representation is a foundational principle in social choice theory, ensuring that groups influence collective decisions in proportion to their size. While it has traditionally been studied in the context of political elections, recent work in computational social choice has broadened its scope to a variety of voting frameworks. This article showcases how proportional representation can be formalized and applied beyond these frameworks, spotlighting AI domains where it naturally takes shape. In particular, we focus on two such domains: clustering and AI alignment. In clustering, proportionality ensures that sufficiently large and cohesive groups of data points or agents are adequately represented in the selection of cluster centers or group assignments, to both centroid-based and noncentroid-based paradigms. In AI alignment, particularly in reinforcement learning from human feedback (RLHF), proportionality provides a principled framework for aggregating heterogeneous preferences by designing committees of reward functions that reflect annotators' viewpoints in proportion to their prevalence. We also discuss additional promising applications, including client selection in federated learning and forming committees of pre-trained models in meta-learning, and argue that incorporating proportional representation into AI systems provides a mathematically rigorous foundation for aligning algorithmic outcomes with the breadth of human viewpoints.

Artificial Intelligence (AI) systems increasingly shape many aspects of daily life, influencing our jobs, finances, healthcare, and online content. This expansion has led to the rise of human–AI systems, where humans communicate, collaborate, or otherwise interact with AI, such as using AI outputs to make decisions. While these systems have shown potential to enhance human capabilities and improve performance on benchmarks, evidence suggests that they often underperform compared to AI-only or human-only approaches in experiments and real-world applications. Here, we argue that human–AI systems should be developed with a greater emphasis on human-centered factors—such as usability, fairness, trust, and user autonomy—within the algorithmic design and evaluation process. We advocate for integrating human-centered principles into AI development through human-centered algorithmic design and contextual evaluation with real users. Drawing on interdisciplinary research and our tutorial at two major AI conferences, we highlight examples and strategies for AI researchers and practitioners to embed these principles effectively. This work offers a systematic synthesis that integrates technical, practical, and ethical insights into a unified framework. Additionally, we highlight critical ethical considerations, including fairness, labor, privacy, and human agency to ensure that systems meet performance goals while serving broader societal interests. Through this work, we aim to inspire the field to embrace a truly human-centered approach to algorithmic design and deployment.