Abstract--Large Language Models (LLMs) are increasingly integrated into software engineering (SE) tools for tasks that extend beyond code synthesis, including judgment under uncertainty and reasoning in ethically significant contexts. We present a fully automated framework for assessing ethical reasoning capabilities across 16 LLMs in a zero-shot setting, using 30 real-world ethically charged scenarios. Each model is prompted to identify the most applicable ethical theory to an action, assess its moral acceptability, and explain the reasoning behind their choice. Responses are compared against expert ethicists' choices using inter-model agreement metrics. Our results show that LLMs achieve an average Theory Consistency Rate (TCR) of 73.3% and Binary Agreement Rate (BAR) on moral acceptability of 86.7%, with interpretable divergences concentrated in ethically ambiguous cases. A qualitative analysis of free-text explanations reveals strong conceptual convergence across models despite surface-level lexical diversity. These findings support the potential viability of LLMs as ethical inference engines within SE pipelines, enabling scalable, auditable, and adaptive integration of user-aligned ethical reasoning. Our focus is the Ethical Interpreter component of a broader profiling pipeline: we evaluate whether current LLMs exhibit sufficient interpretive stability and theory-consistent reasoning to support automated profiling. Autonomous systems are increasingly becoming an integral part of our daily lives across diverse domains [1], [2]. These systems can operate independently without any human intervention and make decisions acting on behalf of their users [3]-[6]. Their rapid growth brings both opportunities and challenges. From a software engineering perspective, as these systems become pervasive, a key challenge is designing systems that, beyond meeting technical requirements, also account for ethical considerations [7]-[11]. Recently, various studies have focused on the ethical implications of these software-intensive systems on individuals and society [10], [12]-[15]. Software engineering ethics encompasses principles and rules that guide engineers' decisions throughout the design and development process [16]. V arious approaches have also been introduced that ensure that systems align with broad ethical values like fairness, transparency, and safety [17]-[22].

In biomedical science, review by a Research Ethics Committee (REC) is an indispensable way of protecting human subjects from harm. However, in social science and the humanities, mandatory ethics compliance has long been met with scepticism as biomedical models of ethics can map poorly onto methodologies involving complex socio-political and cultural considerations. As a result, tailored ethics training and support as well as access to RECs with the necessary expertise is lacking in some areas, including parts of Europe and low- and middle-income countries. This paper suggests that Generative AI can meaningfully contribute to closing these gaps, illustrating this claim by presenting EthicAlly, a proof-of-concept prototype for an AI-powered ethics support system for social science and humanities researchers. Drawing on constitutional AI technology and a collaborative prompt development methodology, EthicAlly provides structured ethics assessment that incorporates both universal ethics principles and contextual and interpretive considerations relevant to most social science research. In supporting researchers in ethical research design and preparation for REC submission, this kind of system can also contribute to easing the burden on institutional RECs, without attempting to automate or replace human ethical oversight.

The rise of autonomous AI agents, capable of perceiving, reasoning, and acting independently, signals a profound shift in how digital ecosystems operate, govern, and evolve. As these agents proliferate beyond centralized infrastructures, they expose foundational gaps in identity, accountability, and ethical alignment. Three critical questions emerge: Identity: Who or what is the agent? Accountability: Can its actions be verified, audited, and trusted? Ethical Consensus: Can autonomous systems reliably align with human values and prevent harmful emergent behaviors? We present the novel LOKA Protocol (Layered Orchestration for Knowledgeful Agents), a unified, systems-level architecture for building ethically governed, interoperable AI agent ecosystems. LOKA introduces a proposed Universal Agent Identity Layer (UAIL) for decentralized, verifiable identity; intent-centric communication protocols for semantic coordination across diverse agents; and a Decentralized Ethical Consensus Protocol (DECP) that could enable agents to make context-aware decisions grounded in shared ethical baselines. Anchored in emerging standards such as Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), and post-quantum cryptography, LOKA proposes a scalable, future-resilient blueprint for multi-agent AI governance. By embedding identity, trust, and ethics into the protocol layer itself, LOKA proposes the foundation for a new era of responsible, transparent, and autonomous AI ecosystems operating across digital and physical domains.

It is commonly accepted that clinicians are ethically obligated to disclose their use of medical machine learning systems to patients, and that failure to do so would amount to a moral fault for which clinicians ought to be held accountable. Call this "the disclosure thesis." Four main arguments have been, or could be, given to support the disclosure thesis in the ethics literature: the risk-based argument, the rights-based argument, the materiality argument, and the autonomy argument. In this article, I argue that each of these four arguments are unconvincing, and therefore, that the disclosure thesis ought to be rejected. I suggest that mandating disclosure may also even risk harming patients by providing stakeholders with a way to avoid accountability for harm that results from improper applications or uses of these systems.

"If I use an AI tool for research or to help me create something, should I cite it in my completed work as a source? How do you properly give attribution to AI tools when you use them?" The straightforward answer is that if you're using generative AI for research purposes, disclosure is probably not necessary. Yet, attribution is probably required if you use ChatGPT or another AI tool for composition. Anytime you're feeling ethically conflicted about disclosing your engagement with AI software, here are two guiding questions I think you should ask yourself: Did I utilize AI for research or composition?

Can AI be used ethically for school work? It depends upon who you ask -- quite literally. That's because less than two years after ChatGPT was originally released in November 2022, the attitudes towards AI in the classroom still vary widely. High schools have viewed AI as a crutch at best, and at worst as a tool for cheating. But several universities leave generative AI use entirely up to the discretion of the person teaching the course.

Google's AI chatbot Bard launched for Australian users on Thursday as the company showcased its advancements in artificial intelligence and pledged to roll out the technology ethically. Until now, Bard was only available in the US and the UK, but on Thursday at the company's annual I/O conference Google announced it would open up the chatbot to users in more than 180 countries around the world, including Australia. Bard is the chat program built on Google's large language model, PaLM2, similar to how ChatGPT is built on OpenAI's GPT. It can provide information, write code, translate languages and analyse images. As part of future advancements to Bard announced by Google on Thursday, Bard will provide visual responses in addition to text-based responses. Using Google's Lens application, in the future users will be able to upload images to be analysed by Bard.

The Biden administration said Thursday that the U.S. government will ask for public guidance on its own plans to use AI inside government agencies. The Office of Management and Budget (OMB) said that the agency "will be releasing draft policy guidance on the use of AI systems by the U.S. government for public comment." It comes as administration officials including Vice President Kamala Harris meet with the chief executives of Alphabet, Anthropic, Microsoft, and OpenAI--the four companies at the heart of AI development in the United States. "AI is one of the most powerful technologies of our time, but in order to seize the opportunities it presents, we must first mitigate its risks," the administration said in a statement. "President Biden has been clear that when it comes to AI, we must place people and communities at the center by supporting responsible innovation that serves the public good, while protecting our society, security, and economy."

Artificial Intelligence (AI) has come a long way in recent years, with rapid advancements in machine learning, natural language processing, and deep learning algorithms. These technologies have led to the development of powerful generative AI systems such as ChatGPT, Midjourney, and Dall-E, which have transformed industries and impacted our daily lives. However, alongside this progress, concerns over the potential risks and unintended consequences of AI systems have been growing. In response, the concept of AI capability control has emerged as a crucial aspect of AI development and deployment. In this blog, we will explore what AI capability control is, why it matters, and how organizations can implement it to ensure AI operates safely, ethically, and responsibly.

Linh C. Ho has held executive leadership roles for a number of global tech companies and currently serves as Chief Growth Officer at Zelros. It is no surprise that technology as ubiquitous as artificial intelligence (AI) would eventually require ethical guardrails. Just this past fall, the White House announced a Blueprint for an AI Bill of Rights. In it, the administration proposes a five-part framework for companies using automated systems in their operations: effective and safe systems; data privacy; protections against algorithmic discrimination; notice and explanation; and human alternatives, consideration and fallback. Together, the five principles in the Bill of Rights form an overlapping set of backstops--safeguards intended to help keep the American public free from any harm caused by the unchecked use of AI and other emerging technologies.