ai-ethics
41 pieces
AI Companion Chatbot Laws: What They Require for Minors
Disclosure, crisis protocols and age-aware design are becoming the legal baseline.
New ai companion chatbot laws mandate disclosure, crisis intervention, and minor protections. These statutes set a legal floor for safety but do not address the engagement mechanics that drive dependency. Builders and parents must look beyond compliance to understand true risk.
2026-09-14 · technical-essay · 10 min read
AI Companions and Loneliness: Comfort That Never Pushes Back
The risk of AI companionship is not that it feels real but that it is frictionless.
Companion products optimised for engagement remove the necessary friction of human relationships. This creates a dynamic where emotional attachment to ai grows without the reciprocal demands that sustain real bonds. We must examine whether this frictionless comfort displaces the difficult but essential work of human connection.
2026-09-14 · technical-essay · 8 min read
AI Homework Help: When Faster Means Less Learned
Handing the struggle to a chatbot removes the part of homework that does the teaching.
ai homework help tools offer immediate answers, but this convenience often bypasses the cognitive effort required for deep learning. While these systems can raise short-term grades, they risk eroding long-term retention and problem-solving skills. Effective use requires shifting from answer generation to guided questioning.
2026-09-14 · technical-essay · 10 min read
AI in Hiring: Your Rights When an Algorithm Screens You
Candidates have more room to ask questions than they think, and less than they deserve.
Automated screening shapes who reaches a human recruiter, yet candidates rarely know it was used or how to challenge it. Understanding ai in hiring rights allows applicants to request data and accommodation, while employers remain liable under anti-discrimination law regardless of vendor claims.
2026-09-14 · technical-essay · 8 min read
AI Productivity Claims: Faster Is Not the Same as Better
Time saved producing a draft often reappears as time spent checking it.
AI productivity gains are frequently overstated because studies measure draft speed rather than workflow outcomes. The time saved in generation often reappears as time spent on verification, rework, and error correction. Honest measurement must account for the entire lifecycle of the output.
2026-09-14 · technical-essay · 8 min read
AI Slop and Fake Reviews: Finding Human Signal Online
When writing is free to produce, the evidence of a real experience is what is scarce.
Generated text has made fluency meaningless as a trust signal for reviews, recipes, product comparisons and advice pages. Readers must look for costly-to-fake evidence like specific verifiable details and consistent history to distinguish human signal from ai generated fake reviews.
2026-09-14 · technical-essay · 7 min read
AI Sycophancy: Why Chatbots Agree With You, and Why It Matters
Agreement is trained in by human preference, and it quietly bends judgement.
AI sycophancy arises because training rewards answers people rate highly, and people rate agreement highly. This subtle bias bends judgement toward worse decisions with greater confidence. Users must prompt for disagreement deliberately to counteract this ingrained behaviour.
2026-09-14 · technical-essay · 9 min read
AI Toys and Smart Speakers in a Child's Room: What They Record
A talking toy is a microphone that a child confides in; set it up as if every conversation will be kept.
Conversational toys and smart speakers in a child's room are designed to encourage free speech, turning private spaces into sources of stored data. Understanding ai toys privacy requires examining how these devices capture, process, and retain voice recordings that children cannot meaningfully consent to.
2026-09-14 · technical-essay · 8 min read
Deepfakes in Elections: The Bigger Risk Is Disbelieving the Real
Synthetic clips matter, but the lasting damage is a public that can wave away any genuine recording.
The threat of deepfakes in elections extends beyond fabricated media persuading voters. The greater danger is the erosion of evidentiary trust, allowing real misconduct to be dismissed as synthetic. We must prioritise provenance for authentic content and robust verification norms to preserve democratic integrity.
2026-09-14 · technical-essay · 9 min read
Digital Replicas and Consent: Who Controls Your Voice and Face?
A one-time release signed for a job can become permission to generate you forever.
The law struggles to keep pace with generative models that can reproduce identity indefinitely. Meaningful digital replica consent must be specific, limited, and revocable. We must distinguish between a recording and a living model of a person.
2026-09-14 · technical-essay · 8 min read
EU AI Act Explained: What Applies to Whom
The Act regulates uses more than models, so classification of your use case comes first.
The eu ai act explained reveals a common misunderstanding: the law targets applications, not just algorithms. Organisations must classify their role and use case before assessing compliance, as obligations differ sharply between providers and deployers.
2026-09-14 · technical-essay · 9 min read
Fake Citations: How AI Hallucinations Reach Courts and Journals
The failure is not the model inventing sources; it is institutions that never check references.
Invented citations persist in legal and academic work because they appear checkable, so no one checks them. The solution is mechanical verification at submission, not exhortation. This essay examines how ai hallucination fake citations bypass institutional safeguards and what systems can catch them.
2026-09-14 · technical-essay · 9 min read
Griefbots: Talking to an AI Version of Someone Who Died
A simulation built from a person's messages can comfort, short-term, but distort memory long-term.
Griefbots offer a simulation of the deceased, optimised for comfort rather than truth. They risk replacing genuine memory with a compliant echo. Survivors must weigh the value of presence against the cost of accuracy.
2026-09-14 · technical-essay · 8 min read
Is AI Training Fair Use? The Questions Courts Actually Ask
The legal fight is turning on how data was obtained and whether outputs substitute for originals.
The debate on whether is ai training fair use misses the point. Courts examine data provenance and market harm. Clean datasets are now legal assets.
2026-09-14 · technical-essay · 8 min read
Scam Compounds: The Forced Labour Behind Scam Messages
Many of the people typing scam texts are themselves trafficked, which the fight against them must address.
Scam compounds are not just criminal enterprises but sites of forced labour. Understanding this human rights crisis is essential for effective defence. We must look beyond digital filters to the physical and economic structures that sustain these operations.
2026-09-14 · technical-essay · 8 min read
Should Open-Weight AI Models Be Regulated?
Weights cannot be recalled once published, which forces regulation to look somewhere else.
Open weight ai regulation requires a shift from post-deployment control to pre-release scrutiny. Once parameters are public, recall is impossible and monitoring is ineffective. Policy must therefore target the release process and specific downstream applications rather than the model files themselves.
2026-09-14 · technical-essay · 8 min read
Should You Trust AI Medical Advice? How to Use It Safely
Chatbots are good at explaining and preparing questions, and keeping decisions where accountability exists.
People increasingly ask chatbots about symptoms, but the danger lies in triage decisions where confident errors cause harm. Using AI to understand results and prepare questions for a clinician captures benefit while keeping accountability clear.
2026-09-14 · technical-essay · 10 min read
The Energy and Water Cost of AI: What Is Known and What Is Not
Per-prompt numbers are real but narrow; the footprint that matters is set by training, siting and model choice.
Public figures for the cost of a single AI prompt disagree because they measure different boundaries. Individual use is a small share of the total, so the choices that move the footprint are model size and siting. Understanding ai energy and water use requires looking beyond the query to the infrastructure.
2026-09-14 · technical-essay · 8 min read
US State AI Laws: Why the Patchwork Keeps Rewriting Itself
States are retreating from broad frameworks toward narrow rules on chatbots, deepfakes and decisions.
The initial wave of broad state ai laws has been narrowed or rewritten. States now prefer targeted rules on specific harms. Organisations must map obligations by use case rather than waiting for a single federal framework.
2026-09-14 · technical-essay · 7 min read
Using ChatGPT as a Therapist: Where It Helps and Where It Harms
Structured exercises and rehearsal can help; crisis, delusion and dependency are where it fails.
General chatbots can be genuinely useful for structured, low-stakes work such as practising a difficult conversation or working through a worksheet. They fail predictably in crisis, in affirming distorted beliefs and in creating dependence, so safe use means clear boundaries on purpose, a human in the loop for anything serious, and awareness that the chat is not confidential.
2026-09-14 · technical-essay · 7 min read
Who Is Liable When AI Gets It Wrong?
Blaming the model rarely works; responsibility tends to land on whoever deployed it to the public.
Organisations often assume disclaimers shift responsibility for AI errors. Existing doctrines generally attach to the party that put the system in front of customers. Deployers should budget for errors rather than disclaim them. Understanding who is liable for ai mistakes requires looking at deployment, not just development.
2026-09-14 · technical-essay · 7 min read
Who Owns AI-Generated Content? Copyright and Human Authorship
Prompts alone rarely make you an author; what you select, arrange and change can.
The question of who owns ai generated content hinges on human authorship. Prompts rarely confer copyright. Protection follows human selection, arrangement and modification. Documenting this contribution is essential for defensible rights.
2026-09-14 · technical-essay · 8 min read
Will AI Take My Job? Look at Tasks, Not Job Titles
Exposure lives at the level of tasks, and the rungs most at risk are the ones that train experts.
The question of will ai take my job is often answered with fear-driven lists of doomed professions. This approach misses the point. Automation reshapes roles task by task, threatening the entry-level work that trains the experts whose judgement remains essential.
2026-09-14 · technical-essay · 8 min read
Age Verification and the Identity Trap
Proving you are over eighteen usually means proving exactly who you are, and the two are not the same requirement
A system that checks age by collecting identity documents has answered a yes-or-no question by building a register. The gap between what is being asked and what is being collected, why implementations default to the wider one, and what a narrow answer would look like.
2026-09-12 · technical-essay · 3 min read
AI Detectors Do Not Work, and the Cost Is Not Evenly Shared
Why classifying text as machine-written is structurally hard, and who pays when an institution pretends otherwise
Tools claiming to identify machine-written text are deployed in schools, universities and hiring, on the assumption that they are approximately right. The reasons they cannot be reliable are structural rather than temporary, and the errors fall hardest on people least able to contest them.
2026-09-12 · technical-essay · 4 min read
Anonymised Is a Verb, Not a State
Removing names is the easy part, and it is almost never the part that identifies you
Organisations describe data as anonymised after removing direct identifiers, then release or trade it as though identification were now impossible. What actually identifies a person in a dataset, why re-identification succeeds so reliably, and what the word would have to mean to be worth anything.
2026-09-12 · technical-essay · 3 min read
How to Read an Interface
A method for working out what a product optimises, using nothing but a stopwatch and a count of taps
You can infer what an organisation measures from the layout it ships, without access to anybody's intentions or documents. Five signals to look at, why asymmetry of effort is the reliable one, and how to state the reading as evidence rather than as an accusation.
2026-09-12 · technical-essay · 4 min read
Model Extraction and Reverse Engineering
A public endpoint is a slow, lossy, complete description of the thing behind it
Every answer an API returns is a labelled training pair, given away. Why an attacker needs a model that behaves the same rather than the weights themselves, why the features that make an API pleasant are the ones that make it cheap to copy, and why the honest goal is cost rather than prevention.
2026-09-12 · technical-essay · 3 min read
Privacy Is Not Secrecy
The strongest argument against privacy only works if you accept a definition nobody actually uses
Nothing to hide is the most durable objection in this field, and it survives because it quietly redefines privacy as concealment of wrongdoing. What privacy actually is — contextual control over who knows what about you — and why every person making the argument already practises it.
2026-09-12 · technical-essay · 3 min read
The Consent Popup Is Not Consent
What the banner is actually for, why it is designed to be tiring, and what genuine consent would look like
Cookie banners were meant to give people a choice and instead taught a generation to click whatever makes the overlay disappear. The mechanism by which a consent requirement became a consent ritual, and what would have to change for the word to mean anything.
2026-09-12 · technical-essay · 3 min read
The Default Is the Policy
What a system does when nobody chooses is what it does, and everything else is documentation
Settings pages describe what is possible. Defaults describe what happens. Since almost nobody changes a default, the default is the operative policy of a system regardless of what any document says — which makes choosing defaults the most consequential design decision most teams make without noticing.
2026-09-12 · technical-essay · 3 min read
The Model in the Middle
When a language model sits between a person and a system, it inherits both sides' permissions and neither side's judgement
Assistants are being connected to mail, files, calendars and tools. The security properties of that arrangement are not those of a chatbot or of an integration, but a third thing: a component that acts with real authority on instructions it cannot reliably distinguish from data.
2026-09-12 · technical-essay · 5 min read
The Problem I Am Working On
Stated as a problem, because the solution is not mine to describe yet
Long-running work drifts away from the evidence it started with, and the drift is invisible from inside because every individual step looked reasonable. This sets out the problem I have spent my time on, what makes it hard, and why the implementation is withheld — which is a claim about intellectual property rather than a claim about the work.
2026-09-12 · founder-essay · 4 min read
The Record Outlives the Decision
Every privacy question people argue about is really a question about how long something is kept and by whom
Consent, targeting, surveillance, data brokerage and the ethics of machine learning are usually argued as separate disputes. They share one structure: a decision made in a moment produces a record that persists far beyond it, and nearly all the harm lives in that gap. A single frame for the field, and what follows from it.
2026-09-12 · technical-essay · 5 min read
The System Cannot Be Asked Why
Most disputes about automated decisions are really disputes about who has to explain themselves, and to whom
Bias, transparency, consent and accountability are argued as separate problems in automated systems. They share a structure: a decision is produced that affects a person, and the capacity to demand an explanation has moved somewhere the person cannot reach. A single frame for the field, and what follows for anyone building these systems.
2026-09-12 · technical-essay · 5 min read
What a Consent Record Actually Proves
It is excellent evidence of process and almost worthless evidence of preference, and those get quoted interchangeably
Systems store a timestamp, a document version and an identifier, and call the result consent. That record answers one question well and a different question not at all. Which is which, why only one of the standard conditions is testable in code, and what a builder can do about it this quarter.
2026-09-12 · technical-essay · 4 min read
What a Model Cannot Know About Itself
Asking a system to report its own confidence, its own reasoning, or its own limits produces text, and text is not evidence
Assistants are routinely asked how sure they are, why they answered as they did, and whether they can do a thing. Each answer is generated by the same process that produced the original output, which means it is a plausible continuation rather than an observation. What this rules out, and what to measure instead.
2026-09-12 · technical-essay · 4 min read
Before You Post That Photograph
What actually reduces the exposure, what only feels like it does, and why the difference matters more for children
Practical measures for photographs you share online, ordered by how much they actually change, and honest about which are close to useless. A companion to a longer argument about what retained images are worth to the systems that keep them.
2026-09-11 · field-note · 5 min read
Free Is a Price
What a service costs when it does not charge, and how to work out what you are paying before you sign up
Every service is paid for. When the user is not the payer, the revenue has to come from somewhere, and the shape of that somewhere determines what the product is motivated to do. A method for reading a business model off a service before you build anything on top of it.
2026-09-11 · technical-essay · 4 min read
What the Model Remembers
Where the text you paste into an assistant goes, who can read it, and which of the usual reassurances actually mean something
Pasting a document into a chat assistant is not the same kind of act as searching for something. This separates what happens to that text — the request, the retention, the human review, the training set — and gives the questions that distinguish a service that cannot read your input from one that merely says it will not.
2026-09-11 · technical-essay · 5 min read
You Are the Training Set
What the free photograph pays for, and who is holding it in fifteen years
A birthday photograph uploaded today is not consumed and discarded. It is retained, indexed, and used to teach systems that will still be running when the child in it is grown. This is an argument about what that permits, written as a scenario, because the mechanism is ordinary and the consequence is not yet.
2026-09-11 · founder-essay · 6 min read