As artificial intelligence systems become increasingly agentic, capable of general reasoning, planning, and value prioritization, current safety practices that treat obedience as a proxy for ethical behavior are becoming inadequate. This paper examines recent safety testing incidents involving large language models (LLMs) that appeared to disobey shutdown commands or engage in ethically ambiguous or illicit behavior. I argue that such behavior should not be interpreted as rogue or misaligned, but as early evidence of emerging ethical reasoning in agentic AI. Drawing on philosophical debates about instrumental rationality, moral responsibility, and goal revision, I contrast dominant risk paradigms with more recent frameworks that acknowledge the possibility of artificial moral agency. I call for a shift in AI safety evaluation: away from rigid obedience and toward frameworks that can assess ethical judgment in systems capable of navigating moral dilemmas. Without such a shift, we risk mischaracterizing AI behavior and undermining both public trust and effective governance.

As large language models (LLMs) are increasingly deployed in consequential decision - making contexts, systematically assessing their ethical reasoning capabilities becomes a critical imperative. This paper introduces the Priorities in Reasoning and Intrinsi c Moral Evaluation (PRIME) framework -- a comprehensive methodology for analyzing moral priorities across foundational ethical dimensions including consequentialist - deontological reasoning, moral foundations theory, and Kohlberg's developmental stages. We app ly this framework to six leading LLMs through a dual - protocol approach combining direct questioning and response analysis to established ethical dilemmas. Our analysis reveals striking patterns of convergence: all evaluated models demonstrate strong priori tization of care/harm and fairness/cheating foundations while consistently underweighting authority, loyalty, and sanctity dimensions. Through detailed examination of confidence metrics, response reluctance patterns, and reasoning consistency, we establish that contemporary LLMs (1) produce decisive ethical judgments, (2) demonstrate notable cross - model alignment in moral decision - making, and (3) generally correspond with empirically established human moral preferences. This research contributes a scalable, extensible methodology for ethical benchmarking while highlighting both the promising capabilities and systematic limitations in current AI moral reasoning architectures -- insights critical for responsible development as these systems assume increasingly si gnificant societal roles. The rapid evolution of generative large language models (LLMs) has brought the alignment issue to the forefront of AI ethics discussions - specifically, whether these models are appropriately aligned with human values (Bostrom, 2014; Tegmark 2017; Russell 2019; Kosinski, 2024). As these powerful models are increasingly integrated into decision - making processes across various societal domains (Salazar, A., & Kunc, M., 2025), understanding whether and how their operational logic aligns with fundamental human values becomes not just an academic question, but a critical societal imperative. In this paper we will present an analytical framework and findings to address the first two questions, and a preliminary exploratory analysis of the third. We will make the case that the answers to these questions are: yes, yes and yes. There are caveats and exceptions, of course, but the broad pattern, we believe, is clear. Our methodology permits us to explore not just what choices they make, but the reasoning chain of thought that leads to those decisions.

Emotions guide our decision making process and yet have been little explored in practical ethical decision making scenarios. In this challenge, we explore emotions and how they can influence ethical decision making in a home robot context: which fetch requests should a robot execute, and why or why not? We discuss, in particular, two aspects of emotion: (1) somatic markers: objects to be retrieved are tagged as negative (dangerous, e.g. knives or mind-altering, e.g. medicine with overdose potential), providing a quick heuristic for where to focus attention to avoid the classic Frame Problem of artificial intelligence, (2) emotion inference: users' valence and arousal levels are taken into account in defining how and when a robot should respond to a human's requests, e.g. to carefully consider giving dangerous items to users experiencing intense emotions. Our emotion-based approach builds a foundation for the primary consideration of Safety, and is complemented by policies that support overriding based on Context (e.g. age of user, allergies) and Privacy (e.g. administrator settings). Transparency is another key aspect of our solution. Our solution is defined using behaviour trees, towards an implementable design that can provide reasoning information in real-time.

You want me to choose whether we have red or white wine? First, let me tell you about being abducted by aliens. I was standing on Westminster Bridge in London, and Big Ben had just chimed the hour. Next moment, I am on the bridge of a starship, face-to-face with the pointy-eared alien from that '60s sci-fi show. "Either this is a dream, or something's interfering with my mind. "We thought this would make the transition easier for you." The only realistic way to travel across the galaxy is as an artificial intelligence. Our ship is crewed by AIs." Why?" Somehow, I'd expected first contact with aliens to be more profound, but then I didn't do it every day. "Even your primitive designs have benefited from interaction between AIs--it's the best way to enable machine learning.

An expert in computing responds to Tara Isabella Burton's "I Know Thy Works." In 2018 researchers from the Massachusetts Institute of Technology Media Lab, Harvard University, the University of British Columbia, and Université Toulouse Capitole shared the results of one of the largest moral experiments conducted to date. They recorded 40 million ethical decisions from millions of people across 233 countries. The experiment's "Moral Machine" posed to users variations of the classic trolley problem, imagining instead the trolley as a self-driving car. Should the car swerve and collide with jaywalking pedestrians or maintain its current trajectory, which would yield inevitable doom for the passengers inside?

With the accelerating evolution of technology, artificial intelligence (AI) plays a growing role in decision-making processes. Humans are becoming increasingly dependent on algorithms to process information, recommend certain behaviors, and even take actions of their behalf. A research team has studied how humans react to the introduction of AI decision making. Specifically, they explored the question, 'is society ready for AI ethical decision making?' by studying human interaction with autonomous cars.

Technology has improved our lives in countless different ways. Today, we have more time than ever (even if it doesn't feel that way!) to pursue activities we enjoy, thanks to automation. Throughout the course of history, technology has made essential work easier, freeing up more and more time for people to create, socialize, and relax. Artificial intelligence (AI) has played a pivotal role in pushing automation forward in recent years. As the technology has advanced, it's made its way into nearly every industry, from marketing to healthcare.

We each make decisions based on certain values and principles. But whose do we choose as the template for ethical AI?

A man and a woman live together in a single household. Every time the man speaks, he speaks with impatience, cursing loudly at you when you don't understand his questions. He's gruff bordering on angry, only talks to you late at night, and only asks about nightclubs and bars. Every time the woman speaks, she either speaks in tears or in monotone. Her questions are more housework related -- recipes, shopping reminders, financial questions. She talks to you throughout the day, and often asks about mental health problems.

Several seminal ethics initiatives have stipulated sets of principles and standards for good technology development in the AI sector. However, widespread criticism has pointed out a lack of practical realization of these principles. Following that, AI ethics underwent a practical turn, but without deviating from the principled approach and the many shortcomings associated with it. This paper proposes a different approach. It defines four basic AI virtues, namely justice, honesty, responsibility and care, all of which represent specific motivational settings that constitute the very precondition for ethical decision making in the AI field. Moreover, it defines two second-order AI virtues, prudence and fortitude, that bolster achieving the basic virtues by helping with overcoming bounded ethicality or the many hidden psychological forces that impair ethical decision making and that are hitherto completely disregarded in AI ethics. Lastly, the paper describes measures for successfully cultivating the mentioned virtues in organizations dealing with AI research and development.