Abstract
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.
Highlights
Technology is increasingly the subject of public discussion and regulatory attention
ACCOUNTABILITY BENEFITS We explore some ways in which decision provenance can assist specific accountability considerations of algorithmic systems, before the section that explores machine learning (ML)-driven
We argue that mechanisms for decision provenance, that operate throughout, is becoming ever more important for assisting accountability in these systems arrangements
Summary
Technology is increasingly the subject of public discussion and regulatory attention. It is already common for data to flow from users via a mobile app to the app’s provider, potentially on to other third-parties (e.g. payment processors) This environment represents an interconnected system-of-systems, of which data is a driver. The data flows that drive these interconnected systems are often invisible or opaque This makes it difficult to exercise oversight and to determine where something went wrong and who is responsible, or in some cases, even to identify the entities involved. We reiterate that data flow is highly relevant to accountability discussions, and mechanisms such as decision provenance are an important complement to the considerable work that focuses on the algorithmic specifics
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