Require AI systems above a capability threshold to be interpretable

Algorithmic transparency. When a driverless car has an accident, or a consumer’s loan application has been denied, we should be able to ask what’s gone wrong. The big trouble with the black box algorithms that are currently in vogue is that [nobody] ...

The immediate danger is not that machines will act without human oversight; it is that human overseers have no idea what the machines are actually "thinking." State-of-the-art AI systems are essentially "black boxes." We know the inputs and outputs, ...

We need a glass box approach instead of a black box, where safety rules can be viewed and verified. Accountability will become clearer, and ethical behaviour in business will also be encouraged. [...] AI platforms must keep their safety rules clear a...

It's crazy to use these models in high-stakes situations and not understand them.

I am very concerned about deploying such systems without a better handle on interpretability. These systems will be absolutely central to the economy, technology, and national security, and will be capable of so much autonomy that I consider it basic...

Black box AI systems "do not explain their predictions in a way that humans can understand."

Congress should require standardized documentation. Congress should require a model validation report. [...] provide the required assurance prior to deployment.

“If we pursue [our current approach], then we will eventually lose control over the machines. But, we can take a different route that actually leads to AI systems that are beneficial to humans,” said Russell. “We could, in fact, have a better civiliz...

Congress should demand that these systems be explainable. How that plays out will be a matter for innovation.

ensure that the AI system’s output is interpretable by the provider and the user.

Finally, explainability, one of the thorniest and most technically complicated issues we face, but perhaps the most important of all. Explainability is about transparency. When you ask an AI system a question and it gives you an answer, perhaps the a...

RSF, which has already called for the AI Act to includemeasures to protect the right to information, is convinced that that these models must be regulated. But some European countries, including France, are advocating self-regulation based on codes o...

You should know that an automated system is being used and understand how and why it contributes to outcomes that impact you. Designers, developers, and deployers of automated systems should provide generally accessible plain language documentation i...

Governments should adopt a regulatory framework that sets out a procedure, particularly for public authorities, to carry out ethical impact assessments on AI systems to predict consequences, mitigate risks, avoid harmful consequences, facilitate citi...

If you are using AI to make decisions about consumers in any context, consider how you would explain your decision to your customer if asked.

Robots should be fitted with an "ethical black box" to keep track of their decisions and enable them to explain their actions when accidents happen.

We have a very natural instinct to say: we want as much interpretability as possible. One of the sci-fi worry cases is that agents start speaking in languages to each other that we don't understand. [...] I bet there are ways to define boundaries tha...

As autonomous systems and artificial intelligence (AI) become increasingly common in daily life, new methods are emerging to help humans check that these systems are behaving as expected. One method, called formal specifications, uses mathematical fo...

Explainable AI (XAI) is often offered as the answer to the black box problem and is broadly defined as “machine learning techniques that make it possible for human users to understand, appropriately trust, and effectively manage AI.” Around the world...

Forcing an AI to produce a human-understandable explanation is an additional constraint, and it could affect the quality of its decisions.

[C_TEC urged NIST to avoid defining premature requirements] relating to aspirational standards for AI that have not matured or are not developed yet, including concepts such as "explainability," "auditability," "robust accuracy," and "error-free algo...

[...] principles requiring that AI be explicable are misguided. We should be deciding which decisions require explanations. Automation is still an option;

For both business and technical reasons, automatically generated, high-fidelity explanations of most AI decisions are not currently possible. That’s why we should be pushing for the external audit of AI systems responsible for high-stakes decision ma...

Advanced AIs would not be able to accurately explain some of their decisions.

GH: I’m an expert on trying to get the technology to work, not an expert on social policy. One place where I do have technical expertise that’s relevant is [whether] regulators should insist that you can explain how your AI system works. I think that...