AI Impact Credential
A structured data credential concept used to standardize the description of the environmental impact of AI workloads. It includes fields such as workload information, model identity, environmental metrics, methodology, provenance and uncertainty.
Timeline (last 90 days)
The CarbonLayer team is exploring the concept of an 'AI Impact Receipt', attempting to answer whether the way AI environmental impact is described can be standardized without standardizing misleading assumptions.
In the third part of its article series, CarbonLayer has already discussed the fields an AI Impact Receipt should contain: workload information, model identity, latency, environmental metrics, methodology, provenance, and uncertainty.
A useful AI Impact Receipt specification should distinguish four evidence types: measured values (supported by a documented measurement process with a clearly defined boundary), modeled values (estimated using assumptions, coefficients or computational models), derived values (calculated from other inputs, whose original evidence type must remain traceable), and unavailable (a value that currently cannot be sufficiently supported).
Without methodology versioning, trend changes will be hard to interpret; a future receipt might contain a methodology ID, version number, boundary description and reference link, and the goal is not to hide changes but to make them visible, avoiding the silent mixing of incompatible results.
All Facts (4)
In the third part of its article series, CarbonLayer has already discussed the fields an AI Impact Receipt should contain: workload information, model identity, latency, environmental metrics, methodology, provenance, and uncertainty.
50%UnverifiedA useful AI Impact Receipt specification should distinguish four evidence types: measured values (supported by a documented measurement process with a clearly defined boundary), modeled values (estimated using assumptions, coefficients or computational models), derived values (calculated from other inputs, whose original evidence type must remain traceable), and unavailable (a value that currently cannot be sufficiently supported).
50%UnverifiedWithout methodology versioning, trend changes will be hard to interpret; a future receipt might contain a methodology ID, version number, boundary description and reference link, and the goal is not to hide changes but to make them visible, avoiding the silent mixing of incompatible results.
50%UnverifiedThe CarbonLayer team is exploring the concept of an 'AI Impact Receipt', attempting to answer whether the way AI environmental impact is described can be standardized without standardizing misleading assumptions.
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