  Accountability is widely understood as a goal for well governed computer
systems, and is a sought-after value in many governance contexts. But how can
it be achieved? Recent work on standards for governable artificial intelligence
systems offers a related principle: traceability. Traceability requires
establishing not only how a system worked but how it was created and for what
purpose, in a way that explains why a system has particular dynamics or
behaviors. It connects records of how the system was constructed and what the
system did mechanically to the broader goals of governance, in a way that
highlights human understanding of that mechanical operation and the decision
processes underlying it. We examine the various ways in which the principle of
traceability has been articulated in AI principles and other policy documents
from around the world, distill from these a set of requirements on software
systems driven by the principle, and systematize the technologies available to
meet those requirements. From our map of requirements to supporting tools,
techniques, and procedures, we identify gaps and needs separating what
traceability requires from the toolbox available for practitioners. This map
reframes existing discussions around accountability and transparency, using the
principle of traceability to show how, when, and why transparency can be
deployed to serve accountability goals and thereby improve the normative
fidelity of systems and their development processes.
