This paper introduces reviewability as a framework for improving the accountability of
automated and algorithmic decisionmaking (ADM) involving machine learning. We draw on …

We argue that the phenomena of distributed responsibility, induced acceptance, and
acceptance through ignorance constitute instances of imperfect delegation when tasks are …

In widely used sociological descriptions of how accountability is structured through
institutions, an “actor”(eg, the developer) is accountable to a “forum”(eg, regulatory …

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 …

In 2018, IBM published a white paper touting artificial intelligence (AI) as a way to" reinvent []
the business of government."'With IBM's help, the paper proclaimed, governments will …

Decision-making algorithms are being used in important decisions, such as who should be
enrolled in health care programs and be hired. Even though these systems are currently …

Calls for heightened consideration of fairness and accountability in algorithmically-informed
public decisions-like taxation, justice, and child protection-are now commonplace. How …

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 …

Artificial intelligence and algorithmic decision-making (ADM) technologies are hidden
everywhere in today's modern society. They calculate credit scores, automatically update …

Much attention has focused on algorithmic audits and impact assessments to hold
developers and users of algorithmic systems accountable. But existing algorithmic …