  Demand is growing for more accountability regarding the technological systems
that increasingly occupy our world. However, the complexity of many of these
systems - often systems-of-systems - poses accountability challenges. A key
reason for this is because the details and nature of the information flows that
interconnect and drive systems, which often occur across technical and
organisational boundaries, tend to be invisible or opaque. This paper argues
that data provenance methods show much promise as a technical means for
increasing the transparency of these interconnected systems. Specifically,
given the concerns regarding ever-increasing levels of automated and
algorithmic decision-making, and so-called 'algorithmic systems' in general, we
propose decision provenance as a concept showing much promise. Decision
provenance entails using provenance methods to provide information exposing
decision pipelines: chains of inputs to, the nature of, and the flow-on effects
from the decisions and actions taken (at design and run-time) throughout
systems. This paper introduces the concept of decision provenance, and takes an
interdisciplinary (tech-legal) exploration into its potential for assisting
accountability in algorithmic systems. We argue that decision provenance can
help facilitate oversight, audit, compliance, risk mitigation, and user
empowerment, and we also indicate the implementation considerations and areas
for research necessary for realising its vision. More generally, we make the
case that considerations of data flow, and systems more broadly, are important
to discussions of accountability, and complement the considerable attention
already given to algorithmic specifics.
