AI transparency
In plain wordsOpenness about AI: telling people when they are dealing with one, how a system was built and tested, and why it reached a particular decision.
The principle and practice of disclosing enough about an AI system for outsiders to understand and scrutinize how it was built and how it is being used. It operates at several levels: disclosure that AI is in use (labeling chatbots and synthetic mediasynthetic media, n.: Images, video, audio or text created or substantially altered by AI rather than recorded or written directly by people, whether for art, entertainment, business or deception.); documentation of how a system was built, including training data, evaluations, known limitations and energy use, through model cardsmodel card, n.: A short document published with an AI model explaining what it is for, how it was trained and tested, how well it works for different groups of people, and its known limits., system cardssystem card, n.: A public report released with an AI model that explains what it can do, how it was tested for safety, what risks were found and what protections were put in place. and datasheetsdatasheet for datasets, n.: A standard document that travels with a dataset, describing why it was made, what is in it, how it was collected and labeled, and what it should and should not be used for.; explainabilityexplainability, n.: How well the reasons behind an AI system's outputs can be put into terms that people can understand, check and, if needed, challenge. of individual outputs; and openness about governance, such as usage policies, incidents and auditAI audit, n.: An independent check of an AI system, or of the company using it, against agreed standards — for example, testing whether a hiring tool treats women and men fairly. results. Transparency is a precondition for accountability but not a substitute for it, and it can conflict with privacy, security and trade secrecy. Indexes such as Stanford's Foundation Model Transparency Index score developers on such disclosures.
Under the new transparency rules, the bank must tell applicants when an AI system scored their file.
Origin
'Algorithmic transparency' entered policy debate in the mid-2010s, helped by Frank Pasquale's The Black Box Society (2015); transparency then appeared in nearly every AI ethics guideline of 2016–2019. The EU AI Act (2024) made parts of it law through documentation duties and the disclosure rules of Article 50.
Usage note
Transparency (openness about a system) differs from interpretabilityinterpretability, n.: The effort to understand how an AI system arrives at its outputs — what information it uses and how it combines it — instead of treating it as a sealed black box. (understanding its internal mechanisms) and explainabilityexplainability, n.: How well the reasons behind an AI system's outputs can be put into terms that people can understand, check and, if needed, challenge. (accounts of particular outputs), although the words are often used loosely. Disclosure no one can act on is sometimes criticized as transparency theater.
Related
- explainabilityexplainability, n.: How well the reasons behind an AI system's outputs can be put into terms that people can understand, check and, if needed, challenge.
- model cardmodel card, n.: A short document published with an AI model explaining what it is for, how it was trained and tested, how well it works for different groups of people, and its known limits.
- system cardsystem card, n.: A public report released with an AI model that explains what it can do, how it was tested for safety, what risks were found and what protections were put in place.
- transparency obligationstransparency obligations, n.: EU rules requiring that people be told when they are dealing with AI: chatbots must say they are bots, AI-made content must be marked, and deepfakes must be labeled.
- right to explanationright to explanation, n.: The idea that a person affected by a decision made or shaped by an algorithm — a refused loan, a rejected job application — is entitled to a meaningful account of why.
- algorithmic accountabilityalgorithmic accountability, n.: The idea that the people and organizations behind automated decision systems must be able to explain and justify those systems' effects, answer for the harm they cause, and fix it.
- AI-generated content labelingAI-generated content labeling, n.: Marking text, images, audio or video as made by AI — with a visible notice, hidden data in the file, or both — so that people and platforms can tell synthetic content from real.
- interpretabilityinterpretability, n.: The effort to understand how an AI system arrives at its outputs — what information it uses and how it combines it — instead of treating it as a sealed black box.
Sources
- Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press.
- Bommasani, R. et al. (2023). The Foundation Model Transparency Index. Stanford Center for Research on Foundation Models.
Mentioned in
AI ethicsAI ethics, n.: The study of what is right and wrong in how AI is built and used: who it may harm, who is responsible when it fails, and which values it should respect., explainabilityexplainability, n.: How well the reasons behind an AI system's outputs can be put into terms that people can understand, check and, if needed, challenge., AI literacyAI literacy, n.: Knowing enough about AI to use it sensibly and judge it critically: what it can and cannot do, how it goes wrong, and how it affects people., black boxblack box, n.: A system whose inner workings are hidden or too complex to understand: we can see what goes in and what comes out, but not why it gave a particular answer., model cardmodel card, n.: A short document published with an AI model explaining what it is for, how it was trained and tested, how well it works for different groups of people, and its known limits., system cardsystem card, n.: A public report released with an AI model that explains what it can do, how it was tested for safety, what risks were found and what protections were put in place., transparency obligationstransparency obligations, n.: EU rules requiring that people be told when they are dealing with AI: chatbots must say they are bots, AI-made content must be marked, and deepfakes must be labeled., trustworthy AItrustworthy AI, n.: AI that deserves people's trust because it works reliably, obeys the law, treats people fairly and can be checked, rather than AI that people merely happen to trust., datasheet for datasetsdatasheet for datasets, n.: A standard document that travels with a dataset, describing why it was made, what is in it, how it was collected and labeled, and what it should and should not be used for., value-sensitive designvalue-sensitive design, n.: A way of designing technology that deliberately identifies the human values at stake, such as privacy, fairness or autonomy, and builds them in from the start instead of patching problems later.