  The speed and scale at which machine learning (ML) systems are deployed are
accelerating even as an increasing number of studies highlight their potential
for negative impact. There is a clear need for companies and regulators to
manage the risk from proposed ML systems before they harm people. To achieve
this, private and public sector actors first need to identify the risks posed
by a proposed ML system. A system's overall risk is influenced by its direct
and indirect effects. However, existing frameworks for ML risk/impact
assessment often address an abstract notion of risk or do not concretize this
dependence.
  We propose to address this gap with a context-sensitive framework for
identifying ML system risks comprising two components: a taxonomy of the first-
and second-order risks posed by ML systems, and their contributing factors.
First-order risks stem from aspects of the ML system, while second-order risks
stem from the consequences of first-order risks. These consequences are system
failures that result from design and development choices. We explore how
different risks may manifest in various types of ML systems, the factors that
affect each risk, and how first-order risks may lead to second-order effects
when the system interacts with the real world.
  Throughout the paper, we show how real events and prior research fit into our
Machine Learning System Risk framework (MLSR). MLSR operates on ML systems
rather than technologies or domains, recognizing that a system's design,
implementation, and use case all contribute to its risk. In doing so, it
unifies the risks that are commonly discussed in the ethical AI community
(e.g., ethical/human rights risks) with system-level risks (e.g., application,
design, control risks), paving the way for holistic risk assessments of ML
systems.
