Added the following to guidance for Transparency and Explainability:
"ML actors should also provide clear explanations of the benefits and risks of different ways of accessing ML capabilities (e.g. client vs server-side inference)."
This is in response to Jonathan Bingham's input in the brainstorming session:
"I’m thinking here about how to explain what the Web ML APIs themselves do, and what the tradeoffs are to running ML in the browser vs what the web app would do instead, like run the same or different ML on the server."
"The difficulty of explaining Web ML’s benefits and drawbacks may lead people to make choices that are worse for them. Eg, they might turn off Web ML, not understanding that it’s better for privacy to keep the data local. (I’m thinking here about the transparency and explainability of the API, not the ML model.)"
Added the following to guidance for Transparency and Explainability:
"ML actors should also provide clear explanations of the benefits and risks of different ways of accessing ML capabilities (e.g. client vs server-side inference)."
This is in response to Jonathan Bingham's input in the brainstorming session:
"I’m thinking here about how to explain what the Web ML APIs themselves do, and what the tradeoffs are to running ML in the browser vs what the web app would do instead, like run the same or different ML on the server."
"The difficulty of explaining Web ML’s benefits and drawbacks may lead people to make choices that are worse for them. Eg, they might turn off Web ML, not understanding that it’s better for privacy to keep the data local. (I’m thinking here about the transparency and explainability of the API, not the ML model.)"