EDP Sciences updates AI ethics policy for publishing

Your AI Ethics Roadmap is Here. The transition from student to professional is now defined by your ability to use AI ethically and with sound judgment. We dive into the Utilitarian vs. Categorical ethics debate that determines how students rationalize their tool use, and feature insights from leading experts on why human judgment is the ultimate value-add. Learn how to be transparent about AI assistance, avoid the Gray Zone traps, and ensure your academic effort is focused on the higher-order thinking skills that truly matter. Equip yourself with the AI Literacy needed for the job market. #AIInnovationsUnleashed #StudentSuccess #FutureOfWork #EthicalTech #SkillDevelopment

🌱 Ethics and Artificial Intelligence: The Human Meaning of Intelligent Technologies ❓ But what does #ETHICS truly mean when faced with a #Chatbot, an #AIAgent, or AI in #ClinicalTrials ❓ Ethics applied to Artificial Intelligence is about more than just transparency requirements or algorithmic accountability. It signifies the #CentralityOfTheHumanBeing because we are the custodians of the human sense (or meaning) of things. AI can learn, process, optimise, and generate. But it cannot comprehend the meaning. Its actions are not driven by values, intentions, or sensitivity... these are exclusively and simply human thoughts. The #ETHICAL approach in designing and using intelligent technologies, where everything is converted into numbers, means not delegating the responsibility for meaning to the machine. It means ensuring the common good in every intelligent innovation; that every piece of data is treated with respect, because there are people in front of and behind that data; that every automated decision is illuminated by human reflection. Ethics, ultimately, is not a limit to technology, but its inherent Meaning. It is the invisible thread that connects efficiency to empathy, computational power to depth of thought, and innovation to the awareness of a benefit. Artificial Intelligence can be an extraordinary tool for progress, provided the human being remains the custodian and responsible party for its purpose (or 'why'). And in healthcare, where the promise of innovation meets the fragility of human life, ethics takes on an even deeper value; it becomes the guide for every decision, especially when the innovative drive of clinical trials accelerates faster than our ability to comprehend its impacts and risks. https://lnkd.in/eieUDfVG #digitalhealth

When companies and researchers talk about "ethical AI" or "aligned AI," they're usually presenting it as if ethics were universal and obvious. Whose ethics are embedded in AI systems today?Primarily those of: The predominantly Western, educated developers building them The corporations funding development (with their profit motives and liability concerns) The regulatory environments they operate in (mostly US and EU frameworks) The specific groups who get to provide feedback during development Different cultures have genuinely different values. What counts as "harmful" content varies dramatically - political speech, religious criticism, discussions of sexuality, appropriate ways to address authority figures. Even concepts like privacy, individual vs. collective good, and what constitutes "fair" decision-making aren't universal. When someone says an AI should be "helpful, harmless, and honest," each of those terms requires judgment calls: Helpful for whom and for what purposes?Harmless by whose definition of harm? Honest in ways that align with which cultural communication norms? AI systems today largely reflect the values of those with resources to build them. When a company says their AI represents "broadly held values," they often mean "values we think are broadly held among people like us and won't get us sued." There is no "ethical AI." There's only "whose ethics got the most funding." #AIEthics #TechColonialism #WhoseEthics #AfricanTech #DigitalDecolonization

State of AI Ethics Report 2025 -- 165 pg overview of "principles-to-practice" AI ethics & issues: this case-study rich report from the Montreal AI Ethics Institute has something to engage thinking at every level of AI educator, student, AI tool user or decision maker as they prep for 2026. A particular quick read with high value: Part III, Sectoral Applications, from Health Field AI use to Arts impacts. Original link here https://lnkd.in/gp_tiNXQ

Navigating AI Ethics As AI becomes more pervasive, ethical considerations are increasingly important. In 2024, businesses will need to navigate the ethical...

⭐ We’re excited to announce the launch of Cambridge Elements in AI Ethics and Society! Elements are short books (20,000-30,000 words), fully peer-reviewed but with a fast turnaround and good online and open options access. We are now accepting proposals! We are open to a wide range of methods, approaches and topics, as long as the work is making a significant and original contribution to the study of AI ethics, governance or social impact. Topics of interest include: -The social impact of AI in important sectors such as health and defence; -Insights from particular methodological approaches such as Confucianism and design theory; -Explanations of key topics such as fairness and explainability. Series Editors: Professor Stephen Cave (Cambridge) and Dr Kerry McInerney (Auckland). For more information, and to download a proposal form, follow this link: https://lnkd.in/e9z3eYU4

Can machines be moral? ⚖️ As AI systems start making decisions about healthcare, finance, and even justice, we face a growing ethical question: when these systems fail, who’s accountable? My latest paper explores what happens when AI impedes — rather than supports — human ethical values, and how responsibility can be restored. Read it here 👉 https://lnkd.in/etz7pt2M #AIethics #Accountability #AIpolicy #ResponsibleAI #EthicalTechnology

The Ethics of Desire Prediction: Can AI Ever Anticipate Consent? View My Portfolio As artificial intelligence becomes more integrated into human emotion and intimacy, developers are exploring predictive systems that can interpret cues of attraction, hesitation, and readiness. The goal is to make technology more attuned and empathetic—but it also raises one of the most important ethical questions of our time: Can AI ever responsibly anticipate consent? Recent discussions at the World AI Ethics Summit and research from MIT Media Ethics Lab warn that desire prediction—though technologically feasible—exists in moral gray space. Machine learning models trained on biometric data such as heart rate, body temperature, and micro-expressions risk misinterpreting complex emotional signals, leading to ethical violations rather than empowerment. Three principles are emerging as the foundation for ethical boundaries in this domain: • Consent must remain explicit, not inferred. Algorithms should never assume readiness or interest from physiological or behavioral data alone. • Data ≠ emotion. Desire is situational and fluid; no AI can fully account for context, trauma history, or human ambiguity. • Transparency by design. Users must know when a system is analyzing emotional or physical data—and must retain full control over whether it does so. The challenge isn’t technological—it’s philosophical. Desire is not a dataset to be decoded; it’s a dialogue to be respected. Predictive systems can enhance safety and understanding only when they uphold autonomy as an absolute standard. At V For Vibes, we believe technology should support consent, not simulate it. Our commitment to ethical design ensures that innovation never overrides integrity—and that every interaction begins, and ends, with choice. #EthicalAI #ConsentCulture #SexTech #DigitalEthics #VForVibes

The Morality of AI Usually when we talk about AI ethics, we are talking about how we apply human ethics to the development and application of AI systems. To be clear from the start - that’s not what this article is about. We are going to explore something different – what ethicsl would we expect AI to spontaneously develop? If you’re reading this with a furrowed brow, relax, this article is not about doomsday scenarios of the emergence of world-dominating machine overlords. It is, rather, a muse on why AI will almost inevitably develop its own version of ethics, and how those ethics may evolve. Perhaps surprisingly we are going to find that AI’s moral development may be driven by analogous forces to those which have shaped human morality over the centuries. Read more here: https://lnkd.in/eGPyWPjA

𝑪𝒂𝒏 𝒎𝒂𝒄𝒉𝒊𝒏𝒆𝒔 𝒎𝒂𝒌𝒆 𝒎𝒐𝒓𝒂𝒍 𝒄𝒉𝒐𝒊𝒄𝒆𝒔? 𝑶𝒓 𝒂𝒓𝒆 𝒘𝒆 𝒋𝒖𝒔𝒕 𝒑𝒓𝒐𝒋𝒆𝒄𝒕𝒊𝒏𝒈 𝒐𝒖𝒓 𝒐𝒘𝒏? Just finished Kaggle’s Intro to AI Ethics course and it left me thinking less about code and more about consciousness. The real challenge in AI ethics isn’t just bias in datasets or fairness in algorithms. It’s addressing both the psychological and philosophical: ➡️ Psychology, because our systems inherit the blind spots of their creators. The biases we refuse to confront personally are the ones we end up automating. ➡️ Philosophy, because every “ethical” model quietly assumes an answer to an old question — what does it mean to be fair, just, or human? The course framed ethics as a discipline of designing decisions, but it’s also about designing intent — training machines to act responsibly by first training ourselves to think responsibly. Learning is just a first step. Now comes action: how to embed these concepts into real-world workflows, product decisions, implementation strategies. AI may never have a conscience, but the people building it do. #AIEthics #Psychology #Philosophy #MachineLearning #AI #TechnologyAndSociety