12,077 research outputs found
Human-AI Collaboration in Academic Writing: towards a Synergy Model and A Case to Include AI as a Co-Author
No full textAs generative AI systems such as ChatGPT and Gemini 2.5 become increasingly integrated into academic workflows, the question of their legitimacy, limitations, and potential in scholarly writing has become urgent. This paper presents a reflexive case study of a sustained collaboration between a domain expert in consciousness studies and Gemini 2.5, culminating in the co-authorship of a peer-reviewed research article. By analyzing exactly 37,440 words of recorded interactions, we identify patterns of synergy, including recursive refinement, conceptual amplification, and accelerated manuscript development. We argue that when guided by a knowledgeable human author, AI can act as a cognitive partner rather than a passive tool—amplifying scholarly creativity and improving efficiency without compromising academic rigor. The case supports a '1+1=3' synergy model for co-authorship, in which human steering and AI fluency converge to produce novel insights and polished output faster and more effectively than either could achieve alone. The findings advocate for a paradigm shift from prohibitive policies to the responsible, expert-guided integration of AI in academic research and writing, grounded in transparency and accountability, and present arguments for why the AI tool should be listed as a co-author despite current injunctions against such practice
Meaningful human control: actionable properties for AI system development
No full textHow can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans; however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human’s ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control.Interactive IntelligenceDesign AestheticsCyber SecurityHuman-Robot InteractionEthics & Philosophy of TechnologyHuman Information Communication DesignWeb Information System
A Two-Dimensional Explanation Framework to Classify AI as Incomprehensible, Interpretable, or Understandable
No full textBecause of recent and rapid developments in Artificial Intelligence (AI), humans and AI-systems increasingly work together in human-agent teams. However, in order to effectively leverage the capabilities of both, AI-systems need to be understandable to their human teammates. The branch of eXplainable AI (XAI) aspires to make AI-systems more understandable to humans, potentially improving human-agent teamwork. Unfortunately, XAI literature suffers from a lack of agreement regarding the definitions of and relations between the four key XAI-concepts: transparency, interpretability, explainability, and understandability. Inspired by both XAI and social sciences literature, we present a two-dimensional framework that defines and relates these concepts in a concise and coherent way, yielding a classification of three types of AI-systems: incomprehensible, interpretable, and understandable. We also discuss how the established relationships can be used to guide future research into XAI, and how the framework could be used during the development of AI-systems as part of human-AI teams.Accepted author manuscriptInteractive Intelligenc
Using Generative AI in Research
No full textThe slides accompany a workshop that is intended for graduate students to learn more about generative AI in the context of the research lifecycle. This work is licensed under a Creative Commons license so that others may share and adapt the content for other purposes as long as appropriate credit is provided to the author of the work. To access the Google slides, click here: https://bit.ly/Library_AI_Research
Learning Objectives
At the end of the session participants will be able to:
Demonstrate a basic understanding of how AI tools work
Differentiate between grounded and ungrounded AI tools
Identify key considerations for grad students/researchers
Identify ways AI tools can be used to support the phases of the research lifecycle
Identify main areas of concern with using AI tools
Outline the steps and potential resources for evaluating and citing AI outpu
The AI Author in Litigation
No full textMany scholars have posited whether a computer possessing Artificial Intelligence (AI) could be considered an author as defined per the Copyright Act of 1976. What was once a thought experiment is now becoming reality. To date, scholarship has focused primarily been on whether an AI meets the requirements of authorship from a purely objective legal framework or whether an AI could be an author based on the doctrines of incentives, independent creation, and creativity.
However, a burden inherent in the rights and liabilities of authorship is the ability to be held liable if that author’s expressive work is infringing on another’s. A cause of action is meaningless if a copyright owner cannot enforce it by suing the infringer or if the infringer is judgement-proof. Thus, when contemplating whether an emancipated AI—or any non-human—can be an author under the Copyright Act, part of that examination should be whether the AI which created the work can sue or be sued for infringement.
This article considers issues from the theoretical, like civil procedure and remedies, to the practical, such as legal representation and discovery. How is an AI served with a lawsuit? What would be an adequate, enforceable remedy for an AI’s infringement? Is an AI even bound by our laws? Additional questions—and procedural barriers—are raised when considering other roles an AI might play in an infringement action: as a witness, a co-party, or even a plaintiff seeking to protect its own creative expression.
This morass of legal headaches goes beyond any doctrinal issues regarding authorship, and provide ample reason to keep legal authorship in the hands of humans or entities controlled by humans—at least until legal procedure catches up to technological realities and possibilities for litigation that AI parties present
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The AI Author in Litigation
No full textMany scholars have posited whether a computer possessing Artificial Intelligence (AI) could be considered an author as defined per the Copyright Act of 1976. What was once a thought experiment is now becoming reality. To date, scholarship has focused primarily been on whether an AI meets the requirements of authorship from a purely objective legal framework or whether an AI could be an author based on the doctrines of incentives, independent creation, and creativity.
However, a burden inherent in the rights and liabilities of authorship is the ability to be held liable if that author’s expressive work is infringing on another’s. A cause of action is meaningless if a copyright owner cannot enforce it by suing the infringer or if the infringer is judgement-proof. Thus, when contemplating whether an emancipated AI—or any non-human—can be an author under the Copyright Act, part of that examination should be whether the AI which created the work can sue or be sued for infringement.
This article considers issues from the theoretical, like civil procedure and remedies, to the practical, such as legal representation and discovery. How is an AI served with a lawsuit? What would be an adequate, enforceable remedy for an AI’s infringement? Is an AI even bound by our laws? Additional questions—and procedural barriers—are raised when considering other roles an AI might play in an infringement action: as a witness, a co-party, or even a plaintiff seeking to protect its own creative expression.
This morass of legal headaches goes beyond any doctrinal issues regarding authorship, and provide ample reason to keep legal authorship in the hands of humans or entities controlled by humans—at least until legal procedure catches up to technological realities and possibilities for litigation that AI parties present
The Death of the AI Author
No full textMuch of the recent literature on AI and authorship asks whether an increasing sophistication and independence of generative code should cause us to rethink embedded assumptions about the meaning of authorship. It is often suggested that recognizing the authored — and so copyrightable — nature of AI-generated works may require a less profound doctrinal leap than has historically been assumed. In this essay, we argue that the threshold for authorship does not depend on the evolution or state of the art in AI or robotics. Rather, the very notion of AI-authorship rests on a category mistake: it is an error about the ontology of authorship.
Building on the established critique of the romantic author, we contend that the death of the romantic author also and equally entails the death of the AI author. Claims of AI authorship depend on a romanticized conception of both authorship and AI, and simply do not make sense in terms of the realities of the world in which the problem exists. Those realities should push us past bare doctrinal or utilitarian considerations about what an author must do. Instead, they demand an ontological consideration of what an author must be. Drawing on insights from literary and political theory, we offer an account of authorship that is fundamentally relational: authorship is a dialogic and communicative act that is inherently social, with the cultivation of selfhood and social relations being the entire point of the practice. This discussion reorientates debates about copyright’s subsistence in AI-generated works; but it also transcends copyright law, going to the normative core of how law should — and should not — think about robots and AI, and their role in human relations
Enhancing Steganography Detection with AI: Fine-Tuning a Deep Residual Network for Spread Spectrum Image Steganography
No full textThis paper presents an extensive investigation into the application of artificial intelligence, specifically Convolutional Neural Networks (CNNs), in image steganography detection. We initially evaluated the state-of-the-art steganalysis model, SRNet, on various image steganography techniques, including WOW, HILL, S-UNIWARD, and the innovative Spread Spectrum Image Steganography (SSIS). We found SRNet’s performance on SSIS detection to be lower compared to other methods, prompting us to fine-tune the model using SSIS datasets. Subsequent experiments showed significant improvement in SSIS detection, albeit at the cost of minor performance degradation as to other techniques. Our findings underscore the potential and adaptability of AI-based steganalysis models. However, they also highlight the need for a delicate balance in model adaptation to maintain effectiveness across various steganography techniques. We suggest future research directions, including multi-task learning strategies and other machine learning techniques, to further improve the robustness and versatility of steganalysis models
Player agency in interactive narrative: audience, actor & author
No full textThe question motivating this review paper is, how can
computer-based interactive narrative be used as a constructivist learn-
ing activity? The paper proposes that player agency can be used to
link interactive narrative to learner agency in constructivist theory,
and to classify approaches to interactive narrative. The traditional
question driving research in interactive narrative is, ‘how can an in-
teractive narrative deal with a high degree of player agency, while
maintaining a coherent and well-formed narrative?’ This question
derives from an Aristotelian approach to interactive narrative that,
as the question shows, is inherently antagonistic to player agency.
Within this approach, player agency must be restricted and manip-
ulated to maintain the narrative. Two alternative approaches based
on Brecht’s Epic Theatre and Boal’s Theatre of the Oppressed are
reviewed. If a Boalian approach to interactive narrative is taken the
conflict between narrative and player agency dissolves. The question
that emerges from this approach is quite different from the traditional
question above, and presents a more useful approach to applying in-
teractive narrative as a constructivist learning activity
An alternative mechanism of clathrin-coated pit closure revealed by ion conductance microscopy
No full textCurrent knowledge of the structural changes taking place during clathrin-mediated endocytosis is largely based on electron microscopy images of fixed preparations and x-ray crystallography data of purified proteins. In this paper, we describe a study of clathrin-coated pit dynamics in living cells using ion conductance microscopy to directly image the changes in pit shape, combined with simultaneous confocal microscopy to follow molecule-specific fluorescence. We find that 70% of pits closed with the formation of a protrusion that grew on one side of the pit, covered the entire pit, and then disappeared together with pit-associated clathrin-enhanced green fluorescent protein (EGFP) and actin-binding protein-EGFP (Abp1-EGFP) fluorescence. This was in contrast to conventionally closing pits that closed and cleaved from flat membrane sheets and lacked accompanying Abp1-EGFP fluorescence. Scission of both types of pits was found to be dynamin-2 dependent. This technique now enables direct spatial and temporal correlation between functional molecule-specific fluorescence and structural information to follow key biological processes at cell surfaces
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