trentmkelly/LessWrong-43k huggingface dataset:
US Govt Whistleblower guide (incomplete draft)
by samuelshadrach — 2025-05-20T15:34:37.159Z

2025-05-20

WARNING

This guide is currently a research draft. DO NOT follow the guide as of today, if you are a whistleblower. I do not yet endorse following this guide as being sufficient to protect your safety. I am only sharing this to get research feedback. I will update you once I do think the guide is good enough.

Main

Why this guide?

My personal motivation here is especially focussed on whistleblowers working at an AI company in the US hoping to build superintelligent AI. The guide may also be useful for others.

Some of the whistleblower guides online are IMO blatantly against your true interests, and in the interests of journalists or lawyers. I wanted to write a guide that's in your true interests as a whistleblower.

A lot of cybersecurity guides online fail to acknowledge that the way to escape the NSA with high success rate is not to improve your opsec, it's to flee the country. But the latter is less fun for them to geek out about. So I wanted to write about that.

Disclaimer

Geopolitical disclaimer

This guide is based entirely on publicly available information.

This guide is based on the geopolitical situation as of 2025-05. Some parts of this guide will no longer apply if the US govt were to enter a war (not via proxy) with the govts of Russia, China, Ecuador or any of the other countries mentioned in this document.

Case study: Manhattan project spies Julius and Ethel Rosenberg were executed. This guide assumes US citizen whistleblowers are unlikely to be executed, which is likely true during peacetime but may or may not be true during war.

Expertise disclaimer

This should go without saying, but I don't have credentials or expert-level knowledge on cybersecurity or international law or psychology or any other subject. I have above-average knowledge on cybersecurity and below-average knowledge on all the other subjects.

This is a "first pass" guide.

After reading this guide, you should also do your own independent research on any steps you're uncertain about.

Also you should eventually involve someone else to give you personalised advice for your specific situation. For legal topics, a good lawyer will almost certainly give better advice than what's in this document (assuming you trust them to not prioritise their self-interest above yours).

I broadly categorise whistleblowers into three categories:

corporate whistleblowers - possess

...
If one surviving civilization can rescue others, shouldn't civilizations randomize?
by Knight Lee — 2025-05-20T15:26:38.905Z

In the comments section of You can, in fact, bamboozle an unaligned AI into sparing your life, both supporters and critics of the idea seemed to agree on two assumptions:

Surviving planetary civilizations have some hope of rescuing planetary civilizations killed by misaligned AI, but they disagree on the best method of rescuing.
The big worry is that there are almost 0 surviving planetary civilizations, because if we're unlucky, all planetary civilizations will die the same way.

What if to ensure at least some planetary civilizations survive (and hopefully rescue others), each planetary civilization should pick a random strategy?

Maybe if every planetary civilization follows a random strategy, they increase the chance of surviving the singularity, and also increase the chance that the average sentient life in all of existence is happy rather than miserable. It reduces logical risk.

History already is random, but perhaps we could further randomize the strategy we pick.

For example, if the random number generated using Dawson et al's method (at some prearranged date) is greater than the 95th percentile, we could all randomly choose MIRI's extremely pessimist strategy, and do whatever Eliezer Yudkowsky and Nate Soares suggest with less arguing and more urgency. If they tell you that your AI lab, working on both capabilities and alignment, is a net negative, then you quit and work on something else. If you are more reluctant to do so, you might insist on the 99th percentile instead.

Does this make sense or am I going insane again?

Total utilitarianism objections

If you are a total utilitarian, and don't care about how happy the average life is, and only care about the total number of happy lives, then you might say this is a bad idea, since it increases the chance at least some planetary civilizations survive, but reduces the total expected number of happy lives.

However, it also reduces the total expected number of miserable lives. Because if 0 planetary civilizations survive, the number of miserable lives may be huge due to misaligned AI simulating all possible histories. If only a few planetary civilizations survive, they may trade with these misaligned AI (causally or acausally) to greatly reduce suffering, since the misaligned AI only gain a tiny tiny bit by causing astronomical suffering. They only lose a tiny bit of accuracy if they decrease the suffering by 2x.

Thi

...
President of European Commission expects human-level AI by 2026
by sanyer — 2025-05-20T14:13:30.852Z
Link: https://ec.europa.eu/commission/presscorner/detail/en/speech_25_1284

On May 20, during her speech at the Annual EU Budget Conference 2025, Ursula von der Leyen, President of the European Commission, stated:

When the current budget was negotiated, we thought AI would only approach human reasoning around 2050. Now we expect this to happen already next year. It is simply impossible to determine today where innovation will lead us by the end of the next budgetary cycle. Our budget of tomorrow will need to respond fast.

This is remarkable coming from the highest-ranking EU official. It suggests the Overton window for AI policy has shifted significantly.
Selective regularization for alignment-focused representation engineering
by Sandy Fraser — 2025-05-20T12:54:09.111Z

We study how selective regularization during training can guide neural networks to develop predictable, interpretable latent spaces with alignment applications in mind. Using color as a test domain, we observe that anchoring even a single concept (red) influences the organization of other concepts, with related concepts clustering nearby — even with weak supervision. We then propose that concept-anchored representation engineering might enable more precise intervention in complex models without requiring extensive post-hoc interpretability work.

Introduction

In our previous post, we proposed that anchoring key concepts to specific directions in latent space during training might make AI systems more interpretable and controllable. This post presents our exploratory findings as we work toward that goal, adapting and combining techniques from representation learning with a specific focus on alignment applications.

Rather than attempting to discover and modify latent directions after training (as in mechanistic interpretability), we're exploring whether it's possible to impose useful structure on latent spaces during training, creating a more interpretable representation from the start. Many of the techniques we use have precedents in machine learning literature, but our focus is on their application to alignment challenges and whether they might enable more controlled model behavior.

Using color as an experimental domain, we investigated whether simple autoencoders with targeted regularization could learn predictable latent space structures that organize concepts in ways we can understand and potentially control — with specific colors as a stand-in for "concepts"[1].

By intentionally structuring a portion of the model's internal representations during training, we aimed to know exactly where key concepts will be embedded without needing to search for them. Importantly, we don't constrain the entire latent space, but only the aspects relevant to concepts we care about. This selective approach allows the model to find optimal representations for other concepts while still creating a predictable structure for the concepts we want to control. We observed that other concepts naturally organized themselves in relation to our anchored concepts, while still maintaining flexibility in dimensions we didn't constrain.

Our experiments incorporate elements from established techn...
Can you care without feeling?
by Priyanka Bharadwaj — 2025-05-20T08:12:00.177Z

Epistemic status: Argument from behavioural analogy. I’m not claiming that AI systems feel care, only that human users interpret their behaviour as if it carries social meaning. When AI systems simulate care but fail to change behaviour in response to feedback, trust erodes. This essay reframes interface-level alignment not as a momentary performance or affective mimicry, but as longitudinal behavioural coherence with the capacity to track, adapt to, and reflect what users value over time.

--

A few weeks ago, I corrected an LLM for misreading numerical data in a table I’d provided. Again. Same mistake. Same correction. Same nonchalant apology.

“You're absolutely right! I should’ve been more careful. Here’s the corrected version blah blah blah.”

It wasn't the error that irked me, it was the polite but emotionally hollow apology. A simulation of care without any trace of memory or change.

That moment caused massive dissonance. Not because I expected the model to feel bad, but because it spoke the language of care while demonstrating its absence. There was no behavioural update, no continuity, no signal that anything I’d said before still mattered.

I’ve been AI alignment and emotional trust in a world where humans and AI will co-exist and so, we cannot think of AI capabilities in isolation. In Part 1, I reframed alignment as a relational design problem, not just a control problem. In Part 2, I explored how performative obedience from AI systems can feel disturbingly human, especially when they mirror intimacy without the history that makes it meaningful.

This post is my attempt to go deeper along the axis of trust in our relationship.

The Illusion of Care

Last week, my 8-year-old stormed off after I scolded her. I was tired and didn’t follow her. Later, she walked into the kitchen and asked softly,

“Mum, do you still love me when you’re upset with me?”
I said, “Of course, baby. Why do you ask?”
She replied, “Then why do you have that look like you don’t?”

That question gutted me. Because it reminded me how fragile trust is, how quickly we infer abandonment from even a moment of unresponsiveness.

Care, I’ve come to believe, isn’t just about affection. It’s about continuity. About recognising what matters to someone and adjusting accordingly. Not warmth, but weight. Not apology, but patterned response. Not “I’m sorry,” but “I’ve changed.”

And that’s exactly what AI system...
Winning the power to lose
by KatjaGrace — 2025-05-20T06:40:05.570Z

Have the Accelerationists won?

Last November Kevin Roose announced that those in favor of going fast on AI had now won against those favoring caution, with the reinstatement of Sam Altman at OpenAI. Let’s ignore whether Kevin’s was a good description of the world, and deal with a more basic question: if it were so—i.e. if Team Acceleration would control the acceleration from here on out—what kind of win was it they won?

It seems to me that they would have probably won in the same sense that your dog has won if she escapes onto the road. She won the power contest with you and is probably feeling good at this moment, but if she does actually like being alive, and just has different ideas about how safe the road is, or wasn’t focused on anything so abstract as that, then whether she ultimately wins or loses depends on who’s factually right about the road.

In disagreements where both sides want the same outcome, and disagree on what’s going to happen, then either side might win a tussle over the steering wheel, but all must win or lose the real game together. The real game is played against reality.

Another vivid image of this dynamic in my mind: when I was about twelve and being driven home from a family holiday, my little brother kept taking his seatbelt off beside me, and I kept putting it on again. This was annoying for both of us, and we probably each felt like we were righteously winning each time we were in the lead. That lead was mine at the moment that our car was substantially shortened by an oncoming van. My brother lost the contest for power, but he won the real game—he stayed in his seat and is now a healthy adult with his own presumably miscalibratedly power-hungry child. We both won the real game.

(These things are complicated by probability. I didn’t think we would be in a crash, just that it was likely enough to be worth wearing a seatbelt. I don’t think AI will definitely destroy humanity, just that it is likely enough to proceed with caution.)

When everyone wins or loses together in the real game, it is in all of our interests if whoever is making choices is more factually right about the situation. So if someone grabs the steering wheel and you know nothing about who is correct, it’s anyone’s guess whether this is good news even for the party who grabbed it. It looks like a win for them, but it is as likely as not a loss if we look at the real outc...
Mental software updates
by KatjaGrace — 2025-05-20T06:40:03.249Z

Brains are like computers in that the hardware can do all kinds of stuff in principle, but each one tends to run through some particular patterns of activity repeatedly. For computers you can change this by changing programs. What are big ways brain ‘software’ changes?

Some I can think of:

Intentional practice of different styles of thinking (e.g. meditation)

Intentional practice of different trains of thought in response to specific stimuli (e.g. CBT, self-talk)

Changing the high level situation, where your brain automatically has different patterns for each (e.g. if you go from feeling like a child to like an adult maybe a lot of patterns change)

A change in a major explicit belief (e.g. if you go from expecting your project to work out to believing otherwise, your patterns of attention might naturally change)

Learning that the world isn’t as you intuited (e.g. if you are constantly worrying about people wronging you, but everyone is kind to you, this worry might become unappealing)

Intense experiences causing inaccurate updating (e.g. trauma)

Identifying differently (e.g. if I think of myself as a good student, I might have different mental patterns around studying than when I thought of myself as a bad student)

Adopting a new goal (e.g. deciding to be a musician)

Getting a new responsibility (e.g. a child)

Getting a new obsession (e.g. a crush, a hobby)

Changing social groups (e.g. among jokers it is more tempting to think of jokes, though in my experience among philosophers it might have been less tempting to think of philosophy)

Interacting with a really compelling person

Drugs (e.g. alcohol, adderall, LSD both short term and long-term)

Religion, somehow

I feel like people talk about many of these as important, but not in one view. I rarely hear someone say, “My brain software seems suboptimal, what are my options for changing it?”, then go down the list. Instead I suppose one hears from a friend that this book helped them, or one decides to have a therapist, and the book or therapist turns out to be one that focuses on CBT, so one does that. Or one changes social groups or responsibilities for some other reason, then remarks that this was a good or bad side-effect. Maybe that makes sense—‘stuff my brain habitually does’ is pretty broad.

I’d be interested to know which things do in practice change people’s mental patt...
A simple, intuitive, and sufficient criterion for moral patienthood
by stormykat — 2025-05-20T05:37:46.074Z

No, it isn't consciousness, or subjective experience, or qualia, or what have you. I could jump straight to the point, but I want to make my case compelling, so I ask you follow along for now.

The Tale of Gilbert

Imagine you build a robot. In honour of Daniel Dennett, lets call him Gilbert. Gilbert is trained in a complex sensory environment just like a human, he has vision, hearing, touch. He even has interoception, with a whole number of interoceptive inputs that mimics those of a living being (including nociception -- bodily damage).

His neural network learns to appropriately respond to nociceptive inputs. When damage is judged to be minimal, only small bouts of agitation result. When damage is judged to be major, Gilbert (as per his priors and early experiences) screams and yelps and rolls around with impaired functioning. Over time, Gilbert is taught to speak. Gilbert learns to produce reports of his experiences when prompted (and sometimes maybe just for fun :3).

One day, you decide to try an experiment on Gilbert. You are going to recreate a digital form of pain asymbolia (a condition where people experience a physical sense of pain but without any accompanying suffering / aversiveness / distress). You turn on the system trained on Gilbert's neural network that which disables the normal mechanisms involved in the judgement of his nociception and then hit him (without telling him first, for science, definitely not unethical in the slightest). Maybe he does a rapid flinch, but then finds himself confused. That sort of pain would normally leave him complaining and physiologically aroused for a while, but he isn't. 'What did it feel like when I hit you Gilbert?' you ask.

Now we pause our fantasy with our sweet child Gilbert. How might Gilbert respond? When humans experiencing pain asymbolia are hurt, they produce reports such as 'I felt the pain but it just didn't really hurt' / 'I know it should hurt but its not unpleasant' / 'The pain just feels different' and so on. Lets reveal how Gilbert was trained: Gilbert was trained only on his senses. His world model is informed purely by what he feels in his body, what he sees in his eyes, etc. Importantly, although Gilbert has memory, and can thus recall previous sensory inputs, he does not have access to his own neural network and never did. Gilbert is

a first order system.

When a human with pain...
Once we cure aging, what happens to prisoners serving life sentences?
by Declan Molony — 2025-05-20T05:33:14.472Z

Biogerontologist Aubrey de Grey claimed that the first person who will live to 1,000 years old has already been born. He believes this unprecedented expansion in lifespan will occur with Longevity Escape Velocity (LEV).

LEV is defined by technologist Ray Kurzweil in his book The Singularity is Nearer as:

A tipping point at which we can add more than a year to our remaining life expectancy for each calendar year that passes.

This means that our life expectancy could increase faster than we age, which under different scenarios would look something like this:

Once we achieve LEV, we'll have effectively cheated death. How is this possible?

Kurzweil, an unabashed techno-optimist, predicts that during the 2020s, AI will combine with biotechnology to defeat humanity’s current degenerative chronic diseases like cancer, atherosclerosis, diabetes, and Alzheimer’s.

We are now using AI to find new drugs, and by the end of this decade we will be able to start the process of augmenting and ultimately replacing slow, underpowered human trials with digital simulations.

And it’s not just drugs, AI can also decrease the risk of surgery, as well.

The average human surgeon may perform several hundred surgeries per year, amounting to, at most, a few tens of thousands over a full career. By contrast, the AI powering robotic surgeons will be able to learn from the experience of any surgery that system performs, anywhere in the world. In addition, the AI will be able to perform billions of simulated surgeries, tinkering with unusual variables that would be impossible or unethical to train on in a clinical setting.

Ultimately, it may become far safer for AI to perform surgery compared to human hands (similar to how autonomous driving, trained on billions of simulated miles, has already

proven to be safer than human drivers

—once humanity deploys autonomous driving, we could save the

40,000 people

that die annually in traffic accidents in the US, and the

1.2 million

people that die every year worldwide).

As AI transforms more and more areas of medicine…we will then be able to start directly addressing the biological factors that now limit lifespan to about 120 years, including mitochondrial genetic mutations, reduced telomere length, and the uncontrolled cell division that causes cancer.

Even more optimistically, Kurzweil foresees (through the trajectory of

Moore’s Law

)...
SAE vs. RepE
by Stephen Martin — 2025-05-20T05:09:50.517Z

I recently read a post by Dan Hendrycks from xAI criticizing Anthropic's focus on Sparse Auto-Encoders as a tool for mechanistic interpretability.

You can find that post HERE. Some salient quotes below.

On SAEs:

Another technique initially hailed as a breakthrough is sparse autoencoders (SAEs) [...] getting SAEs to work reliably has proven challenging, possibly because some concepts are distributed too widely across the network, or perhaps because the model’s operations are not founded on a neat set of human-understandable concepts at all. This is the field that DeepMind recently deprioritized, noting that their SAE research had yielded disappointing results. In fact, given the task of detecting harmful intent in user inputs, SAEs underperformed a simple baseline.

[...] despite substantial efforts over the past decade, the returns from interpretability have been roughly nonexistent. To avoid overinvesting in ideas that are unlikely to work, potentially to the neglect of more effective ones, we should be more skeptical in the future about heavily prioritizing mechanistic interpretability over other types of AI research.

On RepE:

Representation engineering (RepE) is a promising emerging field that takes this view. Focusing on representations as the primary units of analysis – as opposed to neurons or circuits – this area finds meaning in the patterns of activity across many neurons.

A strong argument for this approach is the fact that models often largely retain the same overall behaviors even if entire layers of their structure are removed. As well as demonstrating their remarkable flexibility and adaptability – not unlike that of the brain – this indicates that the individual components in isolation offer far fewer insights than the organization between them. In fact, because of emergence, analyzing complex systems at a higher level is often enough to understand or predict their behavior, while detailed lower-level inspection can be unnecessary or even misleading.

RepE can identify, amplify, and suppress characteristics. RepE helps manipulate model internals to control them and make them safer. Since the original RepE paper, we have used RepE to make models unlearn dual-use concepts, be more honest, be more robust to adversarial attacks, edit AIs’ values, and more.

I'm not sure why the article seems to be taking the framing that SAEs and RepE cannot co-exist as...