Open Ethics builds transparency labels to close AI accountability gap
Without structured AI transparency, consumers lack the information they need to make informed decisions about the products they use
Artificial intelligence (AI) companies consistently disclose only what benefits them commercially, leaving consumers, investors and regulators without the information they need to make meaningful decisions about the systems they use.
Open Ethics is a non-profit initiative with a mission to make AI systems more accountable and understandable. Co-founded by Alice Pavaloiu and Nikita Lukianets, it builds open-source tools that allow product owners to describe their AI solutions in a standardized, accessible way.
“Most organizations who fill out these disclosures are going to use them in two ways,” Pavaloiu told TechJournal.uk in an interview in London. “The first is to create a public disclosure to become more accountable, align with compliance frameworks and strengthen their reputation with investors. The second, which is an indirect benefit, is to start having this conversation about transparency internally and create a database of incidents.”
“The end user wants to know whether the system hallucinates or whether their data is safe. An auditor or software developer wants architectural detail. An investor looks at totally different transparency metrics,” she said.
Most organizations have never formally audited their own AI failures. The disclosure process, even before anything is made public, forces that conversation to happen.
Because labels are self-disclosures, organizations can in principle submit inaccurate information. Open Ethics is working to address this by partnering with third-party auditors to cross-check published disclosures against the systems they describe.
The initiative’s portfolio spans four disclosure tools: the Open Ethics Label, a standardized transparency label for AI products; the Open Ethics Data Passport (OEDP), which maps dataset construction and tests for bias; the Open Ethics Maturity Model (OEMM), a five-level ethical governance framework; and the Public Surveillance Transparency Project, which applies the same principles to cameras in public spaces.
Label as mirror
Pavaloiu spoke about the suite of disclosure products Open Ethics has developed and the obstacles that remain.
She and Lukianets came to the transparency problem after identifying persistent gaps in communication between AI providers and the public, and between the technology industry and government.
When they began engaging with organizations around five or six years ago, they found both sides operating in silos: governments unable to form policy without industry input, and companies resistant to openness for fear of commercial exposure.
Their response was to look at how other industries handle consumer information. They visited supermarkets and household appliance stores to study product labels and research which information genuinely helps a consumer decide whether to use a product.
“We were thinking about these very long privacy policies, obviously written in legal language, quite dry and very hard to understand. We said, what about labels? They are quite compact and offer a transparent view inside the product,” Pavaloiu said.
“We realized that technology should be regulated equally to food, but it does not come with a label. So we said, why would we not create one.”
The Open Ethics Label is generated through a structured form that covers three pillars: training data, algorithms and the decision space. An optional fourth section covers data processing, retention and human-in-the-loop practices.
Once submitted, Open Ethics adds a timestamp, issues a cryptographic SHA3-512 signature to ensure data integrity, generates a machine-readable JSON file and returns embeddable HTML for the organization’s website.
“Designing the label was not actually hard. Having it adopted by organizations in a voluntary way: this is where our challenges began,” she said.
The label is not an ethical certificate, and Open Ethics is deliberate about that. Whatever an organization inputs, the protocol outputs in a standardized, readable form.
“We always say the Open Ethics Label is not a certificate. It is rather just a mirror. Whatever they input, we output in a standardized way. This is all the magic that goes behind it,” Pavaloiu said.
The Open Ethics Data Passport goes deeper into the structure of AI systems. Where the label is an invitation to transparency, the passport is a detailed snapshot of how a training dataset was constructed and how a system was built.
Pavaloiu said the data passport was designed to deliver built-in transparency and allow organizations to spot systemic bias in trained AI models across all phases of the machine learning lifecycle.
“This is where it gets its strength from,” she said. “Think about a self-driving car with many components and sensors. If every vendor of every AI component generates a data passport at the point of assembly or sale, the car can come with a compounded data passport of every single component. Whenever something goes wrong, you have an entire map: a starting point for testing for bias.”
The human bias problem
Technical bias in AI systems has attracted significant attention and a growing set of tools. Pavaloiu argues that human bias, embedded during the data labeling process, is a far larger and harder problem that the industry has barely begun to address.
“We are addressing technical bias, and there are quite a lot of good tools on the market that do that. But what about human bias? This is the far bigger and harder problem to address,” she said.
Open Ethics is developing a hypothesis: if a project owner identifies values relevant to their application, for example dignity in a medical AI context, and hires data labelers who already hold those values, the resulting dataset should carry less human bias.
The challenge is designing a value assessment methodology that cannot be gamed by respondents.
“If we just ask them: are you trustworthy? Everyone will say yes to every value. Harvard has an implicit association test, essentially a speed test where the person answers based on whatever their mind associates in a split second,” Pavaloiu said. “It bypasses the conscious and takes you straight into the subconscious. This could be a way to identify values rather than simply asking.”
Open Ethics has been self-funded by Pavaloiu and Lukianets since the initiative launched. A membership scheme introduced around a year ago set fees at 50 euros per year, designed to cover basic running costs. A free contributor tier ensures students and early-career practitioners can participate.
“We realized we would probably need more than two minds to scale and drive impact across the globe. We have welcomed 100 members and contributors: AI experts, regulators, legislators, AI enthusiasts and people from law, philosophy and the medical field,” she said.
The most contentious project in the portfolio takes the disclosure model into physical space. The Public Surveillance Transparency Project proposes that institutions operating cameras in public locations post a printed label beside each camera with a QR code linking to a full disclosure.
Pavaloiu said the idea grew partly from personal experience. When she lived in Estonia, delivery robots roamed city streets, drawing smiles from pedestrians and stopping obediently at crossings. But the machines were fitted with 360-degree cameras, and no information was posted anywhere about who operated them, what data they collected or where it went.
“We never thought privacy and safety need to be opposing forces. There is a very big imbalance in power and information between the person who is surveilling and the person being surveilled,” she said. “Usually we do not have much indication. We do not know what type of surveillance it is, whether it is biometric, who the beneficiary of the data is, how long it is stored, or whether it is sold to third parties.”
The project aims to answer those questions at the point of surveillance itself. Under the proposal, anyone passing a camera in a public library, bank ATM or any other institution could scan a QR code on a posted label and immediately access a full disclosure of how their data is being handled.
The project has faced the strongest resistance of any Open Ethics initiative. Pavaloiu said she hoped stronger regulatory requirements, including provisions in the EU AI Act, would create the conditions that voluntary adoption has so far failed to deliver.
Open Ethics remains focused on advancing transparency standards across the industry. Its ultimate aim is a digital space where AI systems can clearly explain themselves to the people who use them.