Four standards, four questions

Three of the four make an invisible property legible by putting a mark on it. 7001 is the exception: it grades disclosure rather than issuing a mark.

IEEE 3152-2024 — Transparent Human and Machine Agency Identification

Published standard · approved December 2024, published May 2025 · I chair the working group

The problem

As AI systems answer calls, write messages, and generate media, the line between human and machine agency blurs. The principle behind the standard is a short one: no one should be misled about whether they are dealing with a human being, a machine, or something in between.

What it actually asks for

The standard deliberately does not ask about a system's internals. It zeroes in on who or what is in control of the content or communication. That scoping is what keeps it implementable: you do not have to explain the model to disclose that a model is involved.

It sets out five categories of agency:

1. A human being, unfiltered.
2. An autonomous AI system.
3. A human interacting through an AI machine, where the AI is the interface and the person directs.
4. An AI machine interacting through a human, where the person is the interface and the AI guides.
5. A filter, fictitious construct, or augmented reality: media synthesised or significantly altered.

Categories three and four are the interesting pair, and they are mirror images of each other. The difference between a person using a tool and a person being steered by one is exactly the difference a reader deserves to know about.

The disclosure travels on three channels: graphical symbols for screens and print, audio marks for voice channels, and voiced disclosures for spoken contexts, with sample phrasings supplied in English, German, and Chinese.

One design decision is worth drawing out, because it governs everything else: disclosure should reassure, not alarm. A marker that frightens people away from every automated system would be a failure even if it were perfectly accurate. Note also that the 3152 marks are IEEE intellectual property, and using them requires the appropriate licence from IEEE.

Where it applies

Customer service, telehealth and telepresence robots, media and entertainment, finance, and social media. The working group's motivating cases ran from deepfake detection to call-centre software that shifts a worker's accent in real time, which is a category-four case in its purest form.

The standard at IEEE → · botmark.org, the project site → · Nell's longer explainer for IEEE Computer Society →

IEEE 7001-2021 — Transparency of Autonomous Systems

Published standard · approved December 2021, published March 2022 · I am Vice-Chair

The problem

Everyone agrees autonomous systems should be transparent, and nobody agrees what the word means. The aim of 7001 is to provide “measurable, testable levels of transparency, so that autonomous systems can be objectively assessed and levels of compliance determined”.

The central move

There is no single state called “transparent”. What a bystander needs is not what an accident investigator needs, and neither is what a lawyer needs. So the standard defines its requirements separately for five stakeholder groups: users, the general public and bystanders, safety certification agencies, incident and accident investigators, and lawyers or expert witnesses.

How the levels work

Each group gets a scale from 0 to 5, where 0 is no transparency and 5 is the maximum achievable. Each level is a requirement expressed as a qualitative property, and the test is simply whether that property is demonstrably present or it is not. That binary is what makes “measurable and testable” real rather than aspirational: there is no score to argue about.

For accident investigators the levels are cumulative, a ladder: if a system meets a given level, it also meets the ones beneath it. The published examples run from a recording device that allows capture and playback, to a timestamped log of sensor inputs, user commands, and actuator outputs, to logging the system's high-level decisions, and then to logging the reasons for those decisions. That step, from what it decided to why it decided, is the crux of the whole standard. The reference point is the aircraft flight data recorder, a functionality the working group considers essential in autonomous systems.

For end users the levels are not a ladder. They are options: a designer may choose an interactive visualisation instead of a user manual. Further up, the system is expected to answer “why did you just do that?”, and further up still, “what would you do if … ?”.

Those rows are illustrative rather than exhaustive: they are drawn from the two stakeholder tables worked through in the companion paper. The standard itself carries the full set for all five groups, and is the binding text.

The two artefacts

A System Transparency Specification sets the transparency requirements before a system is designed. A System Transparency Assessment applies the levels to a system that already exists. One is a spec, the other is an audit.

The standard is generic by design: it is meant to cover robots, autonomous vehicles, assisted living robots, drones, and toys, as well as software-only systems such as medical diagnosis AIs, chatbots, loan recommendation systems, and facial recognition.

The standard at IEEE → · The open-access companion paper →

A note on the numbering: “P7001” is the project designation used while the standard was in draft, which is why the companion paper carries it. The published standard is IEEE 7001-2021.

IEEE 3173-2026 — Endocrine Disrupting Chemical Hazard Labelling

Approved standard · approved February 2026 · I chair the working group

The problem

Poison you cannot taste, on a timescale you cannot see. Endocrine disruptors rarely present as a poisoning. They surface as idiopathic hormone imbalance: cancers, birth defects, infertility, obesity, mood and cognitive change. Symptoms that look like ordinary degenerative disease, or like nothing anyone would call a disease at all. Worse, the damage carries, reaching children and grandchildren who were never exposed.

The case for a mark of their own comes in four parts. The harm is chronic rather than acute, so it never registers as the poisoning event a warning label normally covers. It is inherited. The chemicals are persistent, staying contaminated on a civilisational timescale. And it is unlabelled: the generic serious health hazard symbol says nothing about persistence, nothing about inheritance, and nothing about harm that accrues over a lifetime. The risk lived in a datasheet instead of on the product.

What the standard specifies

It specifies the design of a hazard symbol for chemicals known or presumed to be endocrine disruptors, and for those suspected of being so. That three-tier gradation of known, presumed, and suspected is written into the scope. It includes example implementations for labelling chemicals, electrical and mechanical components, consumer products, and hazardous areas. It also introduces a second symbol indicating the absence of endocrine disruptors, for optional use on products and in awareness-building.

The hazard mark is a trefoil. At its centre sits a stylised dioxin molecule, the archetype of a persistent organic pollutant. Around it are three radiative blades standing for congenital defects, infertility, and chronic disease: the three harms an endocrine disruptor carries through a life, and onward into the next. The trefoil geometry is a deliberate borrowing from the radiological and biological hazard marks, so that the symbol is instinctively unnerving to anyone who has never been taught what it means. Danger. Stay away.

Its counterpart does the opposite job. One mark to warn, one to reassure: the same trefoil redrawn in soft, rounded, living forms, green where the hazard mark is amber. You can tell them apart before you can read either.

The variant that thinks past us

The marks ship in four styles: colour, fill, outline, and weathered. The last one is the interesting one, and it is built to outlast the people who drew it. Forever chemicals oblige us to think past our own civilisation. A drum buried today will still be dangerous when the language on its label has been forgotten, and the sign above it has spent a century in the weather. So the standard ships the mark as it will look eroded, corroded, and half legible. If the geometry still reads as a warning after that, the symbol has done its job.

How it fits what already exists

It does not replace the labelling systems in use. It supplies a symbol that slots into each of them: the GHS red diamond on drums and containers, ISO 7010 warning triangles for contaminated zones, ANSI Z535 panels on machinery, and the EDC-Free mark on consumer products. It is drawn to sit inside the existing signage systems rather than compete with them, so a regulator, a shipper, or a factory can adopt it without inventing anything new. The marks are offered freely; adopters follow the conditions of use set out in the standard, so that the mark means the same thing everywhere it appears. A symbol that means different things in different places is worse than no symbol.

The standard at IEEE → · endohazard.org, the project site →

IEEE CertifAIEd, formerly ECPAIS

IEEE certification programme · I chair the Transparency Experts Focus Group

A note on the name, because it causes genuine confusion: this programme began as ECPAIS, the Ethics Certification Program for Autonomous and Intelligent Systems, and is now IEEE CertifAIEd. They are the same body of work. Older papers, bios, and committee records say ECPAIS; the certification you can buy today says CertifAIEd.

The problem

An organisation can assess the ethical impact of its AI system and then have nowhere to take the result. Before this programme there was no trusted standards body offering a badge or mark that would let a company demonstrate to customers, stakeholders, and the public that it had been formally and publicly validated as accountable, trustworthy, or beneficial by an expert body of peers. Systems already deployed need to communicate whether they are deemed safe or trusted, visibly, to people who will never read the assessment.

What it produces

The programme built criteria and processes for a mark across four areas: transparency, clear and explainable design and operational choices; accountability, human oversight and responsibility for AI outcomes; algorithmic bias, preventing unfair or harmful outputs; and privacy, safeguarding personal data and identity. Those four remain the pillars of CertifAIEd. I chair the focus group responsible for the transparency criteria, which were the first of the four completed, and they were developed in deliberate alignment with the IEEE P7000 series, including 7001.

I chair the Transparency Experts Focus Group within the programme, not the programme itself.

IEEE CertifAIEd →

Why these four

They look like separate subjects, and one of them appears to have wandered in from chemistry. The through-line is that each takes a property that matters and that people cannot see, and makes it legible at the moment of decision: whether a voice on the phone is a person, whether a system can say why it acted, whether a bottle contains something that will affect a grandchild, whether an organisation's claims about its AI have been checked by anyone. Transparency is not a virtue you feel. It is infrastructure, and it has to be built one mark at a time.

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