La filosofía alcanza reflexivamente al conjunto de las actividades que se dan en la enseñanza. Este libro demuestra que lejos de ser un saber superior, la filosofía se limita a ser un saber impertinente que fuerza a continuar reflexionando. La obra se organiza en torno a tres núcleos; qué es el saber filosófico, en qué consiste el hecho de educar y qué dirección imprimir a la actividad educadora.

Safeguarding GenAI models against threats and aligning them with security requirements is imperative yet challenging. This chapter provides an overview of the security landscape for generative models. It begins by elucidating common vulnerabilities and attack vectors, including adversarial attacks, model inversion, backdoors, data extraction, and algorithmic bias. The practical implications of these threats are discussed, spanning domains like finance, healthcare, and content creation. The narrative then shifts to exploring mitigation strategies and innovative security paradigms. Differential privacy, blockchain-based provenance, quantum-resistant (...) algorithms, and human-guided reinforcement learning are analyzed as potential techniques to harden generative models. Broader ethical concerns surrounding transparency, accountability, deepfakes, and model interpretability are also addressed. The chapter aims to establish a conceptual foundation encompassing both the technical and ethical dimensions of security for generative AI. It highlights open challenges and lays the groundwork for developing robust, trustworthy, and human-centric solutions. The multifaceted perspective spanning vulnerabilities, implications, and solutions is intended to further discourse on securing society’s growing reliance on generative models. Frontier model security is discussed using Anthropic proposed approach. (shrink)

Generative artificial intelligence (GenAI) is a subset of artificial intelligence (AI) that can generate content such as texts, images, videos, sounds, etc. While GenAI tools have been utilized in various contexts, their utilization in the academic context is still a controversial topic. Scholars observed that many universities have banned GenAI due to the potential for unethical usage. In this opinion article, we promote the utilization of GenAI tools as feedback agents to alleviate challenges in conventional feedback (...) provision. Feedback is a valuable learning source, especially in writing instructions. However, conventional feedback provision is challenging due to the increase in teacher’s workload and social irritation issues in peer feedback. Utilized ethically, GenAI tools can alleviate these challenges. A few critical considerations are briefly presented. (shrink)

Techno-solutionism (Ts) amplifies academic integrity issues endemic to using Generative AI in Open and Distance education (ODE). It (Ts) induces in Higher education (HE) the disposition that technology can and should be employed in every aspect of teaching, learning, and assessment. The prevalence of Ts in ODE and the consequence of undermining academic integrity is found in the surge in published papers. A 2023 study by Nature of over 1600 scientists reports that nearly 30% use GenAI to write papers, (...) and 5% use it in grant applications. A 2024 Originalityai report on 13,000 analysed papers on arXiv indicates a sudden increase of 200.42%, 44.68% and 22.04%, respectively, in Computer Science, Physics and Mathematics in papers published in Jan. 2019 and those published in Nov. 2023. Apr. 2024 paper on arXiv notes Computer Science to be the most prolific in LLM usage with up to 17.5% increase. The problem this study addresses is balancing the challenges and opportunities of GenAI in ODE. Is there a limit to AI use in ODE? How can we upsurge academic integrity in the age of AI? How might we rethink the culture of publish or perish in the age of AI? Adopting the scoping review methodology and discourse analysis, this paper analyses the gains and pains of AI in ODE. Leveraging the critical theory of technology, it recommends clear green and red lines on using AI in teaching, research, and assessment. (shrink)

Generative Artificial Intelligence (GenAI), such as ChatGPT, shows great promise and potential and is gradually being used in mental health care, but it also raises ethical concerns. These relate t...

Generative artificial intelligence (GenAI) promises to revolutionize data-driven fields (Milmo 2023). Building on decades of large language modeling (LLM) (Toner 2023), GenAI can collect, harmonize...

This article explores the challenges surrounding generative artificial intelligence (GenAI) in public administrations and its impact on human‒machine interactions within the public sector. First, it aims to deconstruct the reasons for distrust in GenAI in public administrations. The risks currently linked to GenAI in the public sector are often similar to those of conventional AI. However, while some risks remain pertinent, others are less so because GenAI has limited explainability, which, in return, limits its uses in (...) public administrations. Confidentiality, marking of GenAI outputs and errors are specific matters for which responses should be technical as well as cultural, as they are pushing the boundaries of our instrumental conceptions of machines. Second, this article proposes some paradigm shifts in the perspective of using GenAI in public administrations due to the radical change caused by its language-based nature. GenAI represents a profound break from the “numerical” nature of AI systems implemented in public administrations to date. The transformative impact of GenAI on the intellectual production of the state raises fears of the replacement, or rather enslavement, of civil servants to machines. The article argues for the development of critical thinking as a specific skill for civil servants who have become highly specialized and will have to think with a machine that is eclectic by nature. It anticipates a transformation in the political nature of public administrations, which should lead to more considerations for the strategic stake related to training corpus and for our conceptualization of the neutrality of AI. (shrink)

This participatory study, conducted in an introductory psychology class, recounts self-reflections of 22 undergraduate students and their instructor engaging in an GenAI-mediated storyboard generation process. It relies on Gordon Pask’s conversation theory, structuring out the nature of interactions between students, instructor, and GenAI, and then uses a qualitative narrative to describe these conversational feedback loops constituting the creation of draft and final storyboards. Results suggest students engaged in cyclical feedback driven processes to master their creations, used elements of (...) photography related to the relationships between objects in frame, image processing, and cultural objects/themes to generate narratives. Issues were faced with generating text on images, and the consistency and style of generated frames as they interacted with GenAI tools. This study provides guidelines to university teaching professionals to design effective activities mediated by text-to-image GenAI. (shrink)

In this short paper, I respond to Keith Raymond Harris’ paper “Synthetic Media, The Wheel, and the Burden of Proof”. In particular, I examine his arguments against two prominent approaches employed to deal with synthetic media such as deepfakes and other GenAI content, namely, the “reactive” and “proactive” approaches. In the first part, I raise a worry about the problem Harris levels at the reactive approach, before providing a constructive way of expanding his worry regarding the proactive approach.

The potential of using personalized Large Language Models (LLMs) or “generative AI” (GenAI) to enhance productivity in academic research, as highlighted by Porsdam Mann and colleagues (Porsdam Mann...

Deepfakes are synthetic media, such as pictures, music and videos, created with generative artificial intelligence (GenAI) tools, a technique that builds upon machine learning and multi-layered neural networks trained on large datasets. Today, anyone can create deepfakes online, without any knowledge about the underpinning technology. This fact opens up creative opportunities at the same time as it creates individual and societal challenges. Some identified challenges are fake news, bullying, defamation, media manipulation and democracy damage. The aim of this study (...) was to analyse and discuss how deepfakes could affect entertainment, education and digital news media in positive and negative ways. The chosen research strategy was a qualitative cross-sectional study to create a snapshot of current attitudes in blogs, online discussion fora and from a selected expert panel. Firstly, blogs and internet discussion fora on deepfakes and AI were analysed inductively. Secondly, results from the previous step were used to create a question scheme for semi-structured interviews with some selected experts that have relevant knowledge in the field of AI and deepfakes. Finally, interviews answered were analysed thematically using the Technology Affordances and Constraints Theory as an analytical lens. Findings show that deepfakes at the same time have a wide variety of affordances, but also some critical constraints to consider. In the affordance part, there are the potential use of deepfakes for speculative and provoking concepts, not only as entertainment, but also to illustrate educational scenarios that are hard or impossible to capture in traditional videos, images or sound recordings. Furthermore, there are many affordances for entertainment and the creation of environments where realism is not an important condition. Some examples are video game graphics, animated film background music in games and music videos. However, the conclusion is that the findings comprise more of constraints than of affordances, where the serious negative aspect of deepfakes can lead to psychological, financial and social harm. Many images and videos in news media have already been manipulated during the last decades, but deepfakes can take this to a new and more problematic level where the overall trust might disappear. The author’s recommendation is to further increase the focus on source criticism and that discussions on which sources to trust should be an early introduced part of educational programmes. (shrink)

OpenAI’s Chat Generative Pre-training Transformer (ChatGPT), Google’s Bard and other generative artificial intelligence (GenAI) technologies can greatly enhance the capability of healthcare profess...

Desde el 22 de noviembre de 2022, con la liberación de ChatGPT, la inteligencia artificial generativa (GenAI) ha emergido como un tema central en el ámbito educativo. Este artículo explora la evolución de los lenguajes políticos en torno a la GenAI en la educación, analizando las dinámicas de poder y los umbrales de historicidad que han moldeado su desarrollo. A través de una perspectiva de historia crítica conceptual praxeológica, siguiendo las metodologías de Elias Palti y basándose en experiencias (...) docentes, se cuestiona el uso problemático del término "Inteligencia Artificial" y se examinan los presupuestos epistemológicos que subyacen a las interpretaciones dominantes sobre la GenAI en la educación. Se analizan los "tipos ideales" que promueven una visión tecnocéntrica y optimista de la GenAI, como la personalización del aprendizaje, la democratización del acceso y la eficiencia administrativa. El artículo critica estos enfoques por perpetuar desigualdades, profundizar el determinismo tecnológico y minimizar las dimensiones sociales, culturales y éticas de la educación. Además, se exploran los procesos de dominación y las relaciones de saber-poder asociados con la implementación de la GenAI, destacando la dependencia tecnológica, la dominación epistémica y la vigilancia sobre estudiantes y docentes. Finalmente, se aboga por la necesidad de promover narrativas de soberanía digital y autodeterminación tecnológica, especialmente en el sur global, para desarrollar soluciones tecnológicas que respondan a contextos locales y fomentar un debate más crítico y pluralista sobre el papel de la IA en la educación. (shrink)

The emergence of Large Language Models (LLMs) has significantly influenced the landscape of Higher Education, with their adoption by students and educators escalating rapidly since OpenAI introduced ChatGPT in November 2022. By means of a rapid literature review the authors examined the current state of generative artificial intelligence (GenAI) tools and their application in diverse learning and teaching settings within Higher Education. A comprehensive analysis was conducted, including peer-reviewed academic literature, conference calls, and insights from social media discussions. This (...) investigation culminated in the development of the ENIGMA framework, comprising six key activities, namely Engage, Navigate, Individualise, Guide, Moderate, and Adapt. The study underscores the necessity for further research into the seamless integration of AI technologies by educators and students in educational settings. (shrink)

Concerns around Generative Artificial Intelligence (GenAI) in higher education have so far largely centred on assessment integrity, resulting in fundamental questions about students’ broader engagement with these tools remaining underexplored. This paper reports on the findings of a survey that forms part of a wider study, comprising the first empirical investigation of GenAI use by university students as a method of engaging with their academic readings. Our survey of 101 students shows that over half of all students surveyed (...) used GenAI to some degree as a method of engaging with their unit readings. Our findings suggest that students turn to these tools in response to time constraints and conceptual difficulties, while maintaining complex attitudes toward their use: most welcome their availability, yet few report trusting or relying on GenAI-generated interpretations or summaries of texts. Importantly, our data reveals substantial demographic variations in usage patterns, with international students and those taking subjects as electives showing significantly higher rates of AI use to assist with reading. This suggests GenAI tools may be serving as important mediators for attempting to overcome epistemic barriers to learning, particularly for students who face additional linguistic or disciplinary challenges. (shrink)

The pace of technological change continues to outstrip the evolution of democratic institutions, creating an urgent need for innovative approaches to democratic reform. However, the experimentation bottleneck - characterized by slow speed, high costs, limited scalability, and ethical risks - has long hindered progress in democracy research. This paper proposes a novel solution: employing generative artificial intelligence (GenAI) to create synthetic data through the simulation of digital homunculi, GenAI-powered entities designed to mimic human behavior in social contexts. By (...) enabling rapid, low-risk experimentation with alternative institutional designs, this approach could significantly accelerate democratic innovation. I examine the potential of GenAI-assisted research to mitigate current limitations in democratic experimentation, including the ability to simulate large-scale societal interactions and test complex institutional mechanisms. While acknowledging potential risks such as algorithmic bias, reproducibility challenges, and AI alignment issues, I argue that the benefits of synthetic data are likely to outweigh their drawbacks if implemented with proper caution. To address existing challenges, I propose a range of technical, methodological, and institutional adaptations. The paper concludes with a call for interdisciplinary collaboration in the development and implementation of GenAI-assisted methods in democracy research, highlighting their potential to bridge the gap between democratic theory and practice in an era of rapid technological change. (shrink)

The recent success of Generative AI (GenAI) has heralded a new era in content creation, dissemination, and consumption. This technological revolution is reshaping our understanding of content, challenging traditional notions of authorship, and transforming the relationship between content producers and consumers. As we approach an increasingly AI-integrated world, examining the implications of this paradigm shift is crucial. This article explores the future of content in the age of GenAI, analysing the evolving definition of content, the transformations brought about (...) by GenAI systems, and emerging models of content production and dissemination. By examining these aspects, we can gain valuable insights into the challenges and opportunities that lie ahead in the realm of content creation and consumption and, hopefully, manage them more successfully. (shrink)

In this editorial essay, we argue that Generative Artificial Intelligence programs (GenAI) draw on what we term a “hypercommons”, involving collectively produced inputs and labour that are largely invisible or untraceable. We argue that automatizing the exploitation of common inputs, in ways that remix and reconfigure them, can lead to a crisis of academic authorship in which the moral agency involved in scholarly production is increasingly eroded. We discuss the relationship between the hypercommons and authorship in terms of moral (...) agency and the ethics of academic production, speculating on different responses to the crisis of authorship as posed by GenAI. (shrink)

The application of generative artificial intelligence (GenAI) technologies in the field of propaganda influences information creation, dissemination, and reception, and introduces new ethical challenges. This paper revisits the philosophical discourses of Jacques Ellul on technology and propaganda, placing them within the context of the rise of today’s generative AI technologies. Ellul identified the First Industrial Revolution as the initial juncture in the history of human technology that formed technique as a social phenomenon, which subsequently shaped the nature of propaganda (...) as a technique. Subsequent developments in computer technology in the latter half of the 20th century enabled the formation of a technological system. This raises the question: Could generative AI represent another pivotal moment in the evolution of the technological system, and what are the ethical implications of propaganda technology in this context? This article seeks to illuminate current discussions on GenAI technology and propaganda ethics with Ellul’s insightful theoretical insights. In terms of research methodology, this study relies on textual interpretation and classical hermeneutics, including three processes: syntactic text interpretation, historical background, and situational application. Ellul’s research delves into the intrinsic links and inherent ethical dimensions between propaganda and technology, examining their comprehensive and enduring impacts. This normative perspective is crucial for a deep understanding of contemporary propaganda within the framework of emerging technologies, helping us to transcend the escalating spiral of propaganda technology and counter-propaganda techniques. By incorporating propaganda facilitated by generative AI technologies into the overall development logic of the technological society, this approach explores its ethical implications from a more macroscopic and holistic perspective. (shrink)

This book explores the revolutionary intersection of Generative AI (GenAI) and cybersecurity. It presents a comprehensive guide that intertwines theories and practices, aiming to equip cybersecurity professionals, CISOs, AI researchers, developers, architects and college students with an understanding of GenAI’s profound impacts on cybersecurity. The scope of the book ranges from the foundations of GenAI, including underlying principles, advanced architectures, and cutting-edge research, to specific aspects of GenAI security such as data security, model security, application-level security, (...) and the emerging fields of LLMOps and DevSecOps. It explores AI regulations around the globe, ethical considerations, the threat landscape, and privacy preservation. Further, it assesses the transformative potential of GenAI in reshaping the cybersecurity landscape, the ethical implications of using advanced models, and the innovative strategies required to secure GenAI applications. Lastly, the book presents an in-depth analysis of the security challenges and potential solutions specific to GenAI, and a forward-looking view of how it can redefine cybersecurity practices. By addressing these topics, it provides answers to questions on how to secure GenAI applications, as well as vital support with understanding and navigating the complex and ever-evolving regulatory environments, and how to build a resilient GenAI security program. The book offers actionable insights and hands-on resources for anyone engaged in the rapidly evolving world of GenAI and cybersecurity. (shrink)