#ethics-of-ai — Public Fediverse posts

What Should Remain Human?

https://pagangrove.wordpress.com/2026/10/10/reflections-on-ai-what-should-remain-human/

RE: https://mementomori.social/@Lupposofi/117172111153319697

In his commentary on Giles Howdle's P&T article on the anthropomorphization of AI, Johnny Sakr raises the topical concept of institutional anthropomorphic fallacy, which creates a lack of accountability, https://link.springer.com/article/10.1007/s13347-026-01173-7

Unfortunately, only the abstract is open access, but it is so well written that the message is probably conveyed. The underlying Howdle article is free, https://link.springer.com/article/10.1007/s13347-026-01133-1

Springer's Philosophy & Technology is also a source worth checking out from time to time, as is Ethics and Information Technology, https://link.springer.com/journal/10676

(The three paragraphs above are a machine translation.)

Thanks @Lupposofi

#AI #anthropomorphism #technology #ethics #ethicsOfai #journal #research #responsibility #accountability #society

Kommentissaan Giles Howdlen P&T artikkeliin tekoälyn antropomorfisoinnista, Johnny Sakr nostaa esiin ajankohtaisen, vastuuvajetta synnyttävän institutionaalisen antropomorfisen erheen käsitteen, https://link.springer.com/article/10.1007/s13347-026-01173-7

Harmillisesti vain abstrakti on OA, mutta niin hyvin laadittu, että viesti välittynee. Taustana toimiva Howdlen artikkeli on ilmainen, https://link.springer.com/article/10.1007/s13347-026-01133-1

Springerin Philosophy & Technology on muutenkin silloin tällöin vilkaisemisen väärti lähde, samoin kuin Ethics and Information Technology, https://link.springer.com/journal/10676

#tekoaly #ai #anthropomorphism #technology #ethics #ethicsOfai #journal #research #vastuu #responsibility #accountability #yhteiskunta #society

In SERRC Moti Mizrahi reviews Sven Nyholm's book The Ethics of Artificial Intelligence: A Philosophical Introduction,

https://social-epistemology.com/2026/07/30/a-review-of-nyholms-the-ethics-of-artificial-intelligence-a-philosophical-introduction-moti-mizrahi/

#serrc #ai #tekoaly #ethics #ethicsOfai #book #kirjat #filosofia #etiikka #philosophy #agi #vastuu #responsibility #cognition #consciousness #sentience #aiAgents #human #yhteiskunta #society #ieee #computing #engineers #harm #welfare #magnificaHumanitas #turing #llm

LIR (Lecture Intéressante Recommandée) Le temps de l'obsolescence humaine de Bruno Patino !!
Livre d'analyse et d'espoir quant à la coexistence de l'humain et de l'intelligence artificielle.
L'humain restant un humain libre notamment par la lecture !

texte complet :
https://open.substack.com/pub/matthieucotteret/p/lir-lecture-interessante-recommandee?r=5hqqnw&utm_campaign=post&utm_medium=web

#IA ✨🤖 #IntelligenceArtificielle #numerique #ObsolenceHumaine #goodbydesign #Tech #ethicsofai #ethique #technologie #TechEthics #BookRecommendation #ethics #Innovation

Sehr schöner Artikel von Jan-Philipp Siebold
(aus der Forschungsgruppe Ethik und kritische Theorien der Künstlichen Intelligenz an der Uni Osnabrück)
und Yannic Vitz ​

https://taz.de/Kuenstliche-Intelligenz-und-Demokratie/!6168197/

#EthicsOfAI #KI #AIEthics

RE: https://flipboard.com/@npr/u.s.-news-aspd61boz/-/a-5ZAT5Az8R_-PUXpoj234Ug%3Aa%3A3195441-%2F0

This is a wild story, another example of someone getting swept up by a chatbot’s bias toward affirmation. It told her she was on the path to find true love, and she followed its guidance to meet a real person who never showed up, not once but twice.

#ChatGPT #AgenticAI #LLMs #ChatBot #EthicsOfAI #ComputerScienceEthics

AI Short Term Memory: Why Better Models Still Frustrate Us

AI short term memory is the reason today’s models can feel sharp, helpful, even uncanny, and then suddenly feel inconsistent. Capability has improved fast. The reliability gap remains because continuity is still fragile.

Anyone who has walked a dog will recognise the pattern. A dog can hold a goal for a moment. Heel. Wait. Cross. Focus stays locked in when the street is quiet and the routine is familiar. Then a new stimulus hits and the whole world resets around it. The plan you thought you were sharing disappears, not because the dog is “stupid,” but because attention is narrow and the present moment takes over.

Modern AI behaves in a similar way. The model is excellent at what it can see right now. Outside that view, it forgets unless you engineer memory around it. For many people, that makes AI feel both powerful and annoying. The tool can generate a great answer, then lose a constraint you already clarified, or repeat a mistake you already fixed.

This matters more than it seems. As AI becomes embedded in everyday workflows, AI forgetfulness becomes more than a mild irritation. It becomes a system design problem with social consequences. The future is not just smarter outputs. The future is reliable context, durable state, and accountable decisions.

AI Memory Limits and the Context Window Problem

Most frustration starts with a simple technical reality: large language models operate within a context window, which is the information they can use at a given moment. Inside that window, the model can reason, summarize, draft, and plan. Outside it, there is no stable long-term memory unless the product supplies one.

That is why “it understood me five minutes ago” can be true and still end badly. The earlier information might no longer be present. The model cannot “remember” it unless it is reintroduced or stored in some persistent state.

People often interpret that as incompetence. The more accurate diagnosis is AI memory limits. Working memory is not the same thing as durable memory. A model can be highly capable while still being unreliable across multi-step tasks, especially when the task is long, complex, or full of constraints.

This is also why AI can sound confident even when it is missing crucial context. Fluency is not evidence. A model can produce persuasive language while improvising. When the thread drops, the model often does not announce uncertainty. It fills gaps with whatever fits the current prompt and the statistical shape of likely text.

That creates a specific kind of friction. Users end up acting as the memory layer. They repeat constraints. They restate goals. They paste context again. In practice, that turns “AI assistant” into “AI tool that needs constant reminders.”

The near-future question is not whether models will improve. They will. The deeper question is whether AI systems will become trustworthy assistants or remain short-term intelligence with long-term consequences.

AI Reliability Gap: Capability vs Continuity

The improvement curve is real. Models follow instructions better than they used to. They reason more effectively. They handle nuance with fewer obvious errors. Yet the everyday experience can still feel brittle because the core problem is not raw intelligence. It is continuity.

This is the AI reliability gap: the mismatch between what the model can do in a single moment and what you need it to do across time.

Three frustrations tend to show up again and again.

One is thread loss. The system forgets a boundary or a requirement and continues as if it never existed. That is the classic “you already told it, but it didn’t stick” feeling.

Another is inconsistency. The system can produce a strong answer, then later contradict itself, not out of malice, but because different prompts pull it into different local interpretations. Without a stable state, the model is easily redirected by whatever is most salient in the current input.

The third is confidence without accountability. Dogs get instant feedback from the leash. Humans get feedback from consequences. Most AI systems do not. They can be wrong with a steady tone and no immediate correction, which is why AI mistakes feel sharper than normal human error: the system sounds certain even when it is guessing.

Those frustrations remain even as models improve because better capability does not automatically produce better reliability. Reliability comes from engineering: state management, verification, provenance, and the ability to recover when context shifts.

Smarter text is not the finish line. Reliability has to be engineered through state, verification, provenance, and recovery when context shifts.

Long Term Memory for AI and Why It Is Hard

People talk about “AI memory” as if it is a single feature. In practice, there are multiple kinds of memory, and each one solves a different part of the problem.

Working memory is what the model holds inside the current context window. This is where most models shine.

Long-term memory for AI is durable context across sessions: preferences, project constraints, stable decisions, and the history that actually matters. This often needs explicit storage, not just longer chats.

Provenance is memory with receipts: where claims came from, what sources were used, what the system relied on. Without provenance, it is hard to trust outputs in high stakes settings.

Normative constraints are the system remembering what it should not do, even when a prompt tries to push it there. This includes safety, but also practical constraints like “do not change the goal” or “do not invent sources” or “do not ignore previously agreed requirements.”

Many AI products do working memory fairly well. The rest is uneven. Some tools store “memories,” but those memories can be noisy, incomplete, or hard to inspect. Some tools retrieve documents, but do not cite what they used. Some tools keep state, but state becomes a hidden layer the user cannot correct.

That is why the experience can still feel distractible under novelty, especially when new inputs pull attention away from the original goal.

This is not a reason to give up on AI. It is a reason to stop pretending that intelligence alone solves the problem. The missing component is structured memory, along with the ability to edit, correct, and constrain it.

Trustworthy AI Systems and the Futuristic Risk

This is where futurism becomes practical.

AI is moving from a writing assistant into an intermediary layer. It will book appointments, negotiate schedules, filter information, draft official messages, summarize meetings, recommend actions, and sometimes trigger actions automatically. That is delegated agency. It is the beginning of AI as an operating layer between you and the world.

If that layer still has short-term-memory behaviour, small errors become structural.

A missing detail becomes a wrong booking. A misread intent becomes a silent denial. A distorted summary becomes an inaccurate record. A confident hallucination becomes the official explanation that someone later treats as fact. The risk is not only dramatic failure. The risk is quiet normalization of machine-driven misunderstandings.

A second risk is cultural. People adapt to the tool. They reduce nuance. They repeat themselves. They learn to phrase requests to avoid misfires. They start writing for the machine. Over time, that can flatten human thinking and shift agency away from the user toward the system’s preferred patterns.

A third risk is soft control. Systems do not need to ban anything to shape behaviour. Defaults, friction, and selective summaries can steer people without visible coercion. A world of AI intermediaries that forget what matters can become a world where citizens are nudged by accident as often as by design.

Trustworthy AI needs contestability, transparency, and reversibility. Without that, we get smooth tools that quietly degrade autonomy.

Classical Liberalism, Human Agency, and Contestable Decisions

Classical liberalism has an unusually practical message for the AI era. Individuals are moral agents. People deserve dignity, due process, and the ability to contest decisions that shape their lives.

That should remain true even when software is influencing the outcome, not just a human being.

A system that mediates your options must support basic rights of the user:

Clear reasons, not opaque outcomes. The ability to appeal or override. The right to opt out without being punished. Transparency about what the system knows and what it does not know. Accountability for those who deploy it.

Convenience is not a sufficient moral argument. Convenience can coexist with freedom, but it can also erode freedom when it replaces explanation with automation.

This is not anti-technology. It is pro-human. A free society is not one where errors never happen. A free society is one where errors are correctable, power is constrained, and the individual is not treated as a passive input to an optimization engine.

AI short term memory becomes political when systems act on people at scale. The fix is not panic or worship. The fix is design: make the system legible, make it contestable, make it accountable.

Practical Design for AI Memory, Provenance, and Accountability

The next leap is not only a better model. It is a better wrapper around the model.

Reliable AI needs explicit project goals. Constraints should be stored, not implied. The system should retrieve context from durable storage when needed, and it should show what it retrieved. Important actions should generate audit trails. Users should be able to undo and roll back when outcomes matter. Uncertainty should be stated clearly when evidence is missing.

This is the difference between vibes and structure.

A system with accountable memory can say: here is what I used, here is what I assumed, here is what might be wrong, here is how to correct me. That is the foundation of trust.

It also turns frustration into progress. Instead of repeating yourself, you update a stable set of constraints. Instead of arguing with the model, you correct the state. Instead of hoping the system “remembers,” you can point to what it stored.

The dog analogy still holds. A good walk is not achieved by pretending squirrels do not exist. A good walk is achieved by cues, boundaries, and a relationship that can recover from distraction. AI will always have edge cases. A good AI system is one that can recover without dragging the user into constant babysitting.

Tomorrow’s AI should not be a mind that forgets. It should be a tool that keeps receipts.

Better Models Need Better Memory Design

Models will continue to improve. That part is almost certain. Yet the most meaningful improvement in how AI feels day to day will come from reliable continuity.

AI short term memory explains why the tool can feel brilliant and frustrating at the same time. The fix is not only smarter language. The fix is structured memory, provenance, and accountability, plus the right to contest and correct.

If we build AI that respects human agency, it will expand what individuals can do without turning them into passengers. If we build AI that optimizes convenience while hiding its reasoning, we will end up in a world that feels smart, smooth, and quietly unfree.

A dog can be distractible and still be a good companion. An AI can be powerful and still be unreliable. The future is not pretending otherwise. The future is designing systems that remember what matters, and that let humans stay in charge.

AI short term memory has probably bitten you at least once. Drop the most annoying example in the comments. Was it thread loss, inconsistent answers, or confident guessing that cost you time?

Could you do me a small favour and share this post? A like helps, a follow helps, but a share is what really gets the conversation in front of the right people.

Key Takeaways

• AI short term memory causes inconsistency, often leading to frustration and communication breakdowns.
• Large language models operate within a context window, lacking stable long-term memory without specific engineering.
• The AI reliability gap highlights the difference between a model’s capabilities at a moment and its continuity over time.
• Long-term memory for AI includes various types like working memory and provenance, which are crucial for effective operation.
• Trustworthy AI requires structured memory and accountability, ensuring users can contest and correct decisions made by the system.

AI romance blooms as Japan woman weds virtual partner of her dreams

By Kim Kyung-Hoon and Satoshi Sugiyama OKAYAMA, Japan, Dec 17 (Reuters) – Music played in a wedding hall in western Japan as Yurina Noguchi, wearing a white gown and tiara, dabbed away her tears, taki…
#Japan #JP #JapanNews #artificialintelligence #ethicsofAI #Klaus #KlausVerdure #news #Relationship #WesternJapan #YurinaNoguchi
https://www.alojapan.com/1429079/ai-romance-blooms-as-japan-woman-weds-virtual-partner-of-her-dreams/

https://www.alojapan.com/1429079/ai-romance-blooms-as-japan-woman-weds-virtual-partner-of-her-dreams/ AI romance blooms as Japan woman weds virtual partner of her dreams #ArtificialIntelligence #EthicsOfAI #Japan #JapanNews #Klaus #KlausVerdure #news #Relationship #WesternJapan #YurinaNoguchi By Kim Kyung-Hoon and Satoshi Sugiyama OKAYAMA, Japan, Dec 17 (Reuters) – Music played in a wedding hall in western Japan as Yurina Noguchi, wearing a white gown and tiara, dabbed away her tears, taking in the words of her husband-to-be: an AI-generated

I created a starter pack for #PhilosophyOfAI and #DataEthics here: go.bsky.app/72k3w4X #EthicsOfAI

RE: https://bsky.brid.gy/convert/ap/at://did:plc:3bybp6nqszlhuan2ch2cmv66/app.bsky.graph.starterpack/3kvub2rrfms2k

… "It’s hard to reconcile that GenAI requires more water than 180,000 USA households each day to complete tasks that we can do ourselves as humans."*

* see source of this data in article footnotes.

#EthicsOfAI #AI
https://cosocial.ca/@deborahh/114886479287479693

👀 "GenAI’s ownership, production & execution entail a series of ethical issues that seem to be at odds with some of the goals & values of both arts & health sectors.

… Discussion is needed about whether & how GenAI can be incorporated ethically & responsibly …

This article presents 5 ethical tensions for GenAI’s use in the arts &/or health practices, & suggested actions, drawing upon evidence regarding GenAI’s impacts."

- Karen McPhail-Bell, PhD, ACC
https://drkmcb.com/blog/talkaboutai
#AI #EthicsOfAI

I finally read a big part of it last night. Very enlightening.
#Floridi #EthicsOfAI

Ethical evolution in the age of AI: Why fear and complacency are not options
Artificial Intelligence is not the problem; humans are

https://uxdesign.cc/ethical-evolution-in-the-age-of-ai-why-fear-and-complacency-are-not-options-2f8fd35eec16

#aiethics #ethicalai #ethicsofai #algoethics #computerethics #ethicaltech #ethicaldesign #humanity #AI #humanity

Algorithmic Nudges Don’t Have to Be Unethical

https://hbr.org/2021/04/algorithmic-nudges-dont-have-to-be-unethical

#aiethics #ethicalai #ethicsofai #algoethics #ethicaldesign #unethical #nudge

Ethical Contribution: Maximizing the Common Good

https://ssrn.com/abstract=4418009

#aiethics #ethicalai #ethicsofai #algoethics #computerethics #ethicaltech #ethicaldesign #humanity

In addition to being an environmental catastrophe AI doesn't bloody work. It's a mechanical turk which hinges on giving cPTSD to Kenyan workers.

https://www.wired.com/story/low-paid-humans-ai-biden-modern-day-slavery/

#AI #EthicsOfAI

#TheMetalDogArticleList
#THEMINDFULMELD

The Electric Eye Opens - Reign of Chaos as AI Joins The Band

by The Metal Dog
The world of heavy metal has always been at the cutting edge, pioneering innovations that push the boundaries of musical creativity and, for some, musical taste. From the era of Black Sabbath...

https://www.themindfulmeld.com/post/the-electric-eye-opens-reign-of-chaos-as-ai-joins-the-band

#Metal #AI #ArtificialIntelligenceMusic #Suno #TheMetalDog #ReignOfChaos #AIComposition #AuthenticityInMusic #EthicsOfAI #InnovationInMusic #MetalEvolution

Back at University made me think of new explanations of AI
You know the saying that people owning a hammer think every problem can be solved by hammering ?
So #AI is like a hammer producing machine and so people not only think to solve any problem with one hammer . they get to create thousands of hammers.
Maybe it can be upgraded to a tools producing factory. So you could try thousands of different variations of tools on your problem. One of them might solve it.

But everyone using thousands of tools every day is a lot of tools to produce for a problem that could be easily and quickly solved with a pen and a paper.

What do you think?

#EthicsOfAI

" #AI can promise anything. In just a few months AI will be able to do anything (since 1960 AIs capable of doing anything a human can do better has always been 10 years away), so why invest in our infrastructures and systems? AI is gonna fix it. Why bother doing the hard and painful work to fight the climate crisis when you can just claim an AI is gonna fix it soon™?"

https://tante.cc/2024/02/28/ai-and-democracy/

#EthicsOfAI

The "Practice" Singularity: As the nerds know, what we call "AI" today isn't really AI. But we *are* getting a preview of the ethical and social problems (<ahem>A beta of Universal Basic Income for people replaced by bots</ahem>).

One huge difference is that a *real* AI would solve all those problems on its own.

fAI (faux AI) is going to make the transition difficult. Humans are going to make it difficult.

I'm telling you: It's too soon.

#AI #GPT #Ethics #EthicsOfAI

I’ll be giving a short presentation on #GenerativeAI and assessment next week based on my experience teaching #Philosophy and #EthicsOfAI at #UCD. #HigherEd #academia

Weirdly (or perhaps not, given that it is presumably trained on thousands of them), #ChatGPT writes essays like a below-average undergraduate: overblown language, obvious non sequiturs, and lack of detailed argument. It worries me that students will try to learn from it. #AI #EthicsOfAI

Assessing student essays for an assignment in which I asked them to critique #AI-generated text is proving to be a bit meta. In some cases they’ve asked #ChatGPT to evaluate their own ideas, so it’s not always clear who (or what) is assessing whom! 😵‍💫 #PhilosophyOfAI #EthicsOfAI

AI agents for facilitating social interactions and wellbeing

https://montrealethics.ai/ai-agents-for-facilitating-social-interactions-and-wellbeing/

#Wellbeing #ethicalai #ethicsofai #relationships

Keynes talked about the promise of automation to increase leisure time and reduce human labor. The problem is Keynes didn't account for the vampiric nature of capitalism to extract every last drop of productivity out of its labor force. With machines acting as a force multiplier, every second of your labor is more valuable and therefore more worthwhile to exploit.

#economics #EthicsInAI #ai #EthicsOfAI #automation

"Why not shift the perspective to another future—a period of resilient small tech using minimum viable configurations, powered by renewable energy, which does not require theft, exploitation and monopoly regimes over digital means of production? This would mean untraining our selves from an idea of the future dominated by some kind of digital-oligarch pyramid scheme, run on the
labour of hidden microworkers, in which causal effect is replaced by rigged correlations" text by Hito Steyerl:
https://newleftreview.org/issues/ii140/articles/hito-steyerl-mean-images
#AIEthics #EthicsOfAI

"As any survivor of human trafficking will tell you, #privacy is safety. They cannot be separated when you are being hunted by the predator you’ve escaped. At odds with this is the fundamental ethos of the tech giants: collect as much #data as possible, manipulate it, trade it, and find a way to profit from it."

https://www.opendemocracy.net/en/beyond-trafficking-and-slavery/how-big-tech-and-ai-are-putting-trafficking-survivors-at-risk/

#AIEthics #EthicsOfAI #MissedQuoteBoost of https://ohai.social/@sonjdol/110541477451382775

@ngaylinn

Don't mind me. Just tagging this:

#AI #AIEthics #AIEthicsTwitter #Techbros #EthicsOfAI

For people who might want to find this illuminating thread. Thank you.

It's pretty grim.

@atomicpoet

Every day they just move that bar lower.
Young adults and older children at risk of suicide are very vulnerable.

Previous studies have shown that as soon as they find out they're talking to an AI most people (reasonably) drop out.

#AITherapy #AIEthics #AIEthicsTwitter #AIJobs #AI #Techbros #EthicsOfAI #EthicsAI #therapy #CounsellingAI #counselling

@BobWilliams I used to obsess about nuclear war as a teenager in the 80's. AI is the next most terrifying thing to me. There is so little control over something that has so many potential unforeseen consequences. And the fact the machines will code is super concerning. Whose going to vet what they create. And for creators one of the last nodes for income will be disappearing before long.

#EthicsOfAI

@bibliotecaria

Gaahhhh. It's horrendous.

#EthicsOfAI #EthicsAI #counselling #CounsellingAI #AICounselling