EvidenceSheet

MP-3.1 Potential benefits of intended AI system functionality and performance are examined and documented

Potential benefits of intended AI system functionality and performance are examined and documented. Claimed benefits are examined and written down rather than assumed, so they can later be weighed against measured costs

4
artefacts
0
held by a system
1
at each review
hard
to go live
Document repository
where the evidence lives
teal = a system already holds it · olive = produced at each review

system holds itEvidence a system already holds

none for this control

periodic reviewEvidence produced at each review

  • Evidence the benefit analysis is revisited against realised outcomes · Document repository

governing documentDocuments that govern the control

  • Documented analysis of the benefits the system is expected to deliver · Document repository
  • The performance basis on which each claimed benefit rests · Document repository
  • Identification of who receives the benefit · Document repository

First move

This control is evidenced by people and documents, not systems. Put the document under version control with an owner and review date, and log each review as a record with reviewer and date. Do not try to automate it.

Common gaps auditors find

Do this for your whole sheet

Paste the rows you run your controls from and get this mapping for every control at once, with the periodic-review ones flagged and a first move per row. No account for the first run.

Build my evidence sheet

MP-2.3 Scientific integrity and TEVV considerations are identified and documented, including those related to experimental design, data collection and selection, system trustworthiness, and construct validation · MP-3.2 Potential costs, including non-monetary costs, which result from expected or realized AI errors or system functionality and trustworthiness are examined and documented, as connected to organizational risk tolerance