The recent rise in popularity of large language models (LLMs) has prompted considerable concerns about their moral capabilities. Although considerable effort has been dedicated to aligning LLMs with human moral values, existing benchmarks and evaluations remain largely superficial, typically measuring alignment based on final ethical verdicts rather than explicit moral reasoning. In response, this paper aims to advance the investigation of LLMs' moral capabilities by examining their capacity to function as Artificial Moral Assistants (AMAs), systems envisioned in the philosophical literature to support human moral deliberation. We assert that qualifying as an AMA requires more than what state-of-the-art alignment techniques aim to achieve: not only must AMAs be able to discern ethically problematic situations, they should also be able to actively reason about them, navigating between conflicting values outside of those embedded in the alignment phase. Building on existing philosophical literature, we begin by designing a new formal framework of the specific kind of behaviour an AMA should exhibit, individu-ating key qualities such as deductive and abductive moral reasoning. Drawing on this theoretical framework, we develop a benchmark to test these qualities and evaluate popular open LLMs against it. Our results reveal considerable variability across models and highlight persistent shortcomings, particularly regarding abductive moral reasoning. Our work connects theoretical philosophy with practical AI evaluation while also emphasising the need for dedicated strategies to explicitly enhance moral reasoning capabilities in LLMs.

Rising need for AI Ethics workers is leading to exceedingly overworked and woefully underappreciated ... [ ] considerations. If there is one thing that we can almost all entirely agree on, I dare say it might be the abundance of worker burnout. Nary a day goes by that there aren't some blazing headlines about this worker or that worker-related burnout happening here or there. Some attribute burnout to concerns over wanting to keep their job and make a living. Others suggest that the burnout mania got especially underway when remote working became acceptable, pushing workers to potentially work nonstop and not have the conventional leave the office at 6 o'clock basis for curtailing work for the day. A slew of reasons exists and are continually bandied around for worker burnout. Those that work in the realm of Artificial Intelligence (AI) are right there in the worker burnout zone too. Yes, with all that excitement and hoopla about the present and future prospects of AI, there are humans toiling away to craft and field the AI. Software developers that specialize in making AI applications are dearly sought by companies. Once onboard, the AI programmers are bound to discover that there is a lot of AI work going on. Indeed, the odds are that a veritable fifteen pounds of AI are needed and yet the AI teams are barely able to produce five pounds given the team size and AI complexities involved.

Will we be able to achieve trustworthy AI, and if so, how. Trust is everything, so they say. The noted philosopher Lao Tzu said that those who do not trust enough will not be trusted. Ernest Hemingway, an esteemed novelist, stated that the best way to find out if you can trust somebody is by trusting them. Meanwhile, it seems that trust is both precious and brittle. The trust that one has can collapse like a house of cards or suddenly burst like a popped balloon. The ancient Greek tragedian Sophocles asserted that trust dies but mistrust blossoms. French philosopher and mathematician Descartes contended that it is prudent never to trust wholly those who have deceived us even once. Billionaire business investor extraordinaire Warren Buffett exhorted that it takes twenty years to build a trustworthy reputation and five minutes to ruin it. You might be surprised to know that all of these varied views and provocative opinions about trust are crucial to the advent of Artificial Intelligence (AI). Yes, there is something keenly referred to as trustworthy AI that keeps getting a heck of a lot of attention these days, including handwringing catcalls from within the field of AI and also boisterous outbursts by those outside of the AI realm. The overall notion entails whether or not society is going to be willing to place trust in the likes of AI systems. Presumably, if society won't or can't trust AI, the odds are that AI systems will fail to get traction.

Are workers indeed quiet quitting, and if so, where does AI fit into this rising trend? You have almost certainly heard about or seen news reports exclaiming that quiet quitting is here and amongst us all. Yes, indeed, quiet quitting is experiencing its banner headline pronouncements during a seemingly pronounced fifteen minutes of fame. Will the spotlight last longer than a short-lived fad? Will it have endurance and become part of our permanent lexicon? Lots of vital questions abound. I am going to unpack the quiet quitting phenomenon and see what makes the whole matter so notably significant right now. On top of that, I'll introduce a facet that I'm betting most have not realized is getting dragged into the quiet quitting mania. Make sure you are sitting down. The latest dovetailing consideration involves the inclusion of Artificial Intelligence (AI) into the quiet quitting arena. AI is being added to the quiet quitting bandwagon, though not everyone is especially pleased with having AI become inexorably entangled therein. This abundantly raises all sorts of AI Ethics concerns. We will examine how quiet quitting and Ethical AI are going to be at times partners and at other times foes. For my overall ongoing and extensive coverage of AI Ethics and Ethical AI, see the link here and the link here, just to name a few.

According to a research article, artificial intelligence will "likely" end humankind as we know it. Scientists from Google and Oxford assert that AI will compete with humans for limited resources on earth. According to researchers, the eventual triumph of intelligent robots over people is inevitable. The Matrix movie's plot--that machines wage war on humans because of their energy needs--about a battle between humans and machines is no longer just fiction. In a study paper, two Oxford University academics and a Google researcher make the case that the development of advanced AI (artificial intelligence) would result in the extinction of humanity since machines will unavoidably compete with people for resources like food and energy.

Trying to overcome human untoward biases by replacing with AI is not as straightforward as it might ... [ ] seem. Humans have got to know their limitations. You might recall the akin famous line about knowing our limitations as grittily uttered by the character Dirty Harry in the 1973 movie entitled Magnum Force (per the spoken words of actor Clint Eastwood in his memorable role as Inspector Harry Callahan). The overall notion is that sometimes we tend to overlook our own limits and get ourselves into hot water accordingly. Whether due to hubris, being egocentric, or simply blind to our own capabilities, the precept of being aware of and taking into explicit account our proclivities and shortcomings is abundantly sensible and helpful. Let's add a new twist to the sage piece of advice. Artificial Intelligence (AI) has got to know its limitations. What do I mean by that variant of the venerated catchphrase? Turns out that the initial rush to get modern-day AI into use as a hopeful solver of the world's problems has become sullied and altogether muddied by the realization that today's AI does have some rather severe limitations. We went from the uplifting headlines of AI For Good and have increasingly found ourselves mired in AI For Bad. You see, many AI systems have been developed and fielded with all sorts of untoward racial and gender biases, and a myriad of other such appalling inequities.

Is it okay or is it questionable for those salting AI Ethicists that seek to get hired by a firm ... [ ] solely to from-within stoke Ethical AI precepts? Salting has been in the news quite a bit lately. I am not referring to the salt that you put into your food. Instead, I am bringing up the "salting" that is associated with a provocative and seemingly highly controversial practice associated with the interplay between labor and business. You see, this kind of salting entails the circumstance whereby a person tries to get hired into a firm to ostensibly initiate or some might arguably say instigate the establishment of a labor union therein. I will cover first the basics of salting and then will switch to an akin topic that you might be quite caught off-guard about, namely that there seems to be a kind of salting taking place in the field of Artificial Intelligence (AI). This has crucial AI Ethics considerations. For my ongoing and extensive coverage of AI Ethics and Ethical AI, see the link here and the link here, just to name a few. Now, let's get into the fundamentals of how salting typically works. Suppose that a company does not have any unions in its labor force. One means would be to take action outside of the company and try to appeal to the workers that they should join a union. This might involve showcasing banners nearby to the company headquarters or sending the workers flyers or utilizing social media, and so on. This is a decidedly outside-in type of approach. Another avenue would be to spur from within a spark that might get the ball rolling.

AI Ethics and the question of whether to ask AI if it is sentient. If I ask you whether you are sentient, you will undoubtedly assert that you are. Allow me to double-check that assumption. Perhaps the question itself seems a bit silly. The chances are that in our daily lives, we would certainly expect fellow human beings to acknowledge that they are sentient. This could be a humorous-inducing query that is supposed to imply that the other person is maybe not paying attention or has fallen off the sentience wagon and gone mentally out to lunch momentarily, as it were. Imagine that you walk up to a rock that is quietly and unobtrusively sitting on a pile of rocks and upon getting close enough to ask, you go ahead and inquire as to whether the rock is sentient. Assuming that the rock is merely a rock, we abundantly anticipate that the erstwhile but seemingly oddish question will be answered with rather stony silence (pun!).

Engineering methods are centered around traditional notions of decomposition and recomposition that rely on partitioning the inputs and outputs of components to allow for component-level properties to hold after their composition. In artificial intelligence (AI), however, systems are often expected to influence their environments, and, by way of their environments, to influence themselves. Thus, it is unclear if an AI system's inputs will be independent of its outputs, and, therefore, if AI systems can be treated as traditional components. This paper posits that engineering general intelligence requires new general systems precepts, termed the core and periphery, and explores their theoretical uses. The new precepts are elaborated using abstract systems theory and the Law of Requisite Variety. By using the presented material, engineers can better understand the general character of regulating the outcomes of AI to achieve stakeholder needs and how the general systems nature of embodiment challenges traditional engineering practice.

Can AI learn ethical precepts on its own? Aristotle famously stated that educating the mind without educating the heart is no education at all. You could interpret that insightful remark to suggest that learning about ethics and moral behavior is keenly vital for humankind. In the classic nature versus nurture debate, one must ask how much of our ethical mores are instinctively native while how much is learned over the course of our living days. Toddlers are observant of fellow humans and presumably glean their ethical foundations based on what they see and hear. The same can be said of teenagers. For open-minded adults, they too will continue to adjust and progress in their ethical thinking as a result of experiencing the everyday world. Of course, explicitly teaching someone about ethics is also par for the course. People are bound to learn about ethical ways via attending classes on the topic or perhaps by going to events and practices of interest to them. Ethical values can be plainly identified and shared as a means to aid others in formulating their own structure of ethics. In addition, ethics might be subtly hidden within stories or other instructional modes that ultimately carry a message of what ethical behavior consists of. That's how humans seem to imbue ethics. I realize such a question might seem oddish. We certainly expect humans to incorporate ethics and walk through life with some semblance of a moral code. It is a simple and obvious fact.