We present an ethical decision-making framework that refines a pre-trained reinforcement learning (RL) model using a task-agnostic ethical layer. Following initial training, the RL model undergoes ethical fine-tuning, where human feedback is replaced by feedback generated from a large language model (LLM). The LLM embodies consequentialist, deontological, virtue, social justice, and care ethics as moral principles to assign belief values to recommended actions during ethical decision-making. An ethical layer aggregates belief scores from multiple LLM-derived moral perspectives using Belief Jensen-Shannon Divergence and Dempster-Shafer Theory into probability scores that also serve as the shaping reward, steering the agent toward choices that align with a balanced ethical framework. This integrated learning framework helps the RL agent navigate moral uncertainty in complex environments and enables it to make morally sound decisions across diverse tasks. Our approach, tested across different LLM variants and compared with other belief aggregation techniques, demonstrates improved consistency, adaptability, and reduced reliance on handcrafted ethical rewards. This method is especially effective in dynamic scenarios where ethical challenges arise unexpectedly, making it well-suited for real-world applications.

People are known to judge artificial intelligence using a utilitarian moral philosophy and humans using a moral philosophy emphasizing perceived intentions. But why do people judge humans and machines differently? Psychology suggests that people may have different mind perception models of humans and machines, and thus, will treat human-like robots more similarly to the way they treat humans. Here we present a randomized experiment where we manipulated people's perception of machine agency (e.g., ability to plan, act) and experience (e.g., ability to feel) to explore whether people judge machines that are perceived to be more similar to humans along these two dimensions more similarly to the way they judge humans. We find that people's judgments of machines become more similar to that of humans when they perceive machines as having more agency but not more experience. Our findings indicate that people's use of different moral philosophies to judge humans and machines can be explained by a progression of mind perception models where the perception of agency plays a prominent role. These findings add to the body of evidence suggesting that people's judgment of machines becomes more similar to that of humans motivating further work on dimensions modulating people's judgment of human and machine actions.

Prior work has explicated the coloniality of artificial intelligence (AI) development and deployment. One process that that work has not engaged with much is alignment: the tuning of large language model (LLM) behavior to be in line with desired values based on fine-grained human feedback. In addition to other practices, colonialism has a history of altering the beliefs and values of colonized peoples; this history is recapitulated in current LLM alignment practices. We suggest that AI alignment be decolonialized using three proposals: (a) changing the base moral philosophy from Western philosophy to dharma, (b) permitting traditions of argument and pluralism in alignment technologies, and (c) expanding the epistemology of values beyond instructions or commandments given in natural language.

Adam Smith developed a version of moral philosophy where better decisions are made by interrogating an impartial spectator within us. We discuss the possibility of using an external non-human-based substitute tool that would augment our internal mental processes and play the role of the impartial spectator. Such tool would have more knowledge about the world, be more impartial, and would provide a more encompassing perspective on moral assessment.

Why should moral philosophers, moral psychologists, and machine ethicists care about computational complexity? Debates on whether artificial intelligence (AI) can or should be used to solve problems in ethical domains have mainly been driven by what AI can or cannot do in terms of human capacities. In this paper, we tackle the problem from the other end by exploring what kind of moral machines are possible based on what computational systems can or cannot do. To do so, we analyze normative ethics through the lens of computational complexity. First, we introduce computational complexity for the uninitiated reader and discuss how the complexity of ethical problems can be framed within Marr's three levels of analysis. We then study a range of ethical problems based on consequentialism, deontology, and virtue ethics, with the aim of elucidating the complexity associated with the problems themselves (e.g., due to combinatorics, uncertainty, strategic dynamics), the computational methods employed (e.g., probability, logic, learning), and the available resources (e.g., time, knowledge, learning). The results indicate that most problems the normative frameworks pose lead to tractability issues in every category analyzed. Our investigation also provides several insights about the computational nature of normative ethics, including the differences between rule- and outcome-based moral strategies, and the implementation-variance with regard to moral resources. We then discuss the consequences complexity results have for the prospect of moral machines in virtue of the trade-off between optimality and efficiency. Finally, we elucidate how computational complexity can be used to inform both philosophical and cognitive-psychological research on human morality by advancing the Moral Tractability Thesis (MTT).

In the late 1940s, pioneering computer genius Alan Turing proposed that a computer can be said to possess artificial intelligence if it can fool a human into thinking it is real by mimicking human responses under specific conditions. But what if human responses are greed, hatred and ruthlessly self-serving dominance over others? At the heart of that question is fear of technology, one which science fiction writer Isaac Asimov had already attempted to pacify in his 1942 Three Laws of Robotics, the first of which is that a robot may not allow a human being to come to harm. The question is, do we - the public - believe and trust that Artificial Intelligence will not be used, by either authorities or corporations, for their own benefit rather than ours? As writer and broadcaster Paul Mason tells CGTN, "The basic philosophical problem posed by artificial intelligence is this: On whose behalf are we developing this stuff? And what kind of society does it assume it's going to create?"

Many readers will remember the Knight Industries Two Thousand (or KITT) from the 1980s – the fascinating self-aware car driven by the Knight Rider – the Hoff. That car was programmed using sophisticated Artificial Intelligence and machine learning. The best science fiction (and it was science fiction in the 1980s) should become science fact shortly afterwards. It is that time because autonomous vehicles are shortly to storm the world stage. We are about to undergo a paradigm shift from passive response-based systems in cars (such as cruise control, lane-change warning alarms, obstacle alarms and so on) to fully active systems.

Alien invasion is a constant theme of Hollywood science fiction, from War of the Worlds to Independence Day. But Harvard psychologist Steven Pinker, author of the new book Enlightenment Now, argues that highly developed civilizations tend toward peace and tolerance, and that advanced aliens are much more likely to be friendly. "I think it's not inconceivable that wars between countries will go the way of slave auctions and dueling, just be seen as too ridiculous for any reasonable country to engage in," Pinker says in Episode 296 of the Geek's Guide to the Galaxy podcast. But wouldn't alien brains be so different from ours that it would make mutual understanding impossible? On the contrary, since aliens would have been subject to the same evolutionary pressures as us, they would probably possess an appreciation of science--and maybe even beauty--similar to ours. "It's conceivable that other intelligences have a sense of beauty that is not wildly different from ours," Pinker says, "because they too might be expected to be attuned to counter-entropic forces and patterns in nature."

Notes from Reality is a series of posts on AI and its impact on humans, what can be done today, and what may happen tomorrow. No one has all the answers, but we are trying to arrive at the right questions. In this post, I interviewed Dr. David Bray, Harvard Executive In-Residence and Eisenhower Fellow. This effort began with the post "Let the New Machine Age Begin." The process to write that led to another interview with Dr. Bray and Michael Krigsman, a noted analyst, which is posted here.

DB: Imagine what the next 5 years will bring: The term "mobile computing" will eventually become a dated term, replaced by "ubiquitous computing" as the internet will be everywhere. These changes include the transportation we take on land, in the air, and at sea; the clothes and devices we wear, sensors at work, at home, in our environment, and (if we chose) in us for medical purposes as well. DB: Also right behind and coupled with the Internet of Everything: 3D mass fabricators enabling individuals to affordably "print" and modify at the molecular level tangible substances based on digital designs. Maker Faires around the world already exist showcasing the early stages of what 3D fabricators can do in the hands of artists, engineers, and hobbyists. As Co-Chair of the IEEE Committee focused on Artificial Intelligence and Innovative Policies, I firmly believe exponential changes like the era we're in offer great opportunities for society -- as well as great challenges.