  Rising concern for the societal implications of artificial intelligence
systems has inspired a wave of academic and journalistic literature in which
deployed systems are audited for harm by investigators from outside the
organizations deploying the algorithms. However, it remains challenging for
practitioners to identify the harmful repercussions of their own systems prior
to deployment, and, once deployed, emergent issues can become difficult or
impossible to trace back to their source. In this paper, we introduce a
framework for algorithmic auditing that supports artificial intelligence system
development end-to-end, to be applied throughout the internal organization
development lifecycle. Each stage of the audit yields a set of documents that
together form an overall audit report, drawing on an organization's values or
principles to assess the fit of decisions made throughout the process. The
proposed auditing framework is intended to contribute to closing the
accountability gap in the development and deployment of large-scale artificial
intelligence systems by embedding a robust process to ensure audit integrity.

Computers and Society