This hands-on session takes academic concepts and their formulation in policy initiatives
around algorithmic accountability and explainability and tests them against real cases. In …

As research on algorithms and their impact proliferates, so do calls for scrutiny/accountability
of algorithms. A systematic review of the work that has been done in the field of'algorithmic …

Abstract On April 3rd, 2022, a group of US lawmakers introduced the Algorithmic
Accountability Act of 2022. The legislation proposed by Democrats requires sizable entities …

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 …

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

Ethical principles for algorithms are gaining importance as more and more stakeholders are
affected by" high-risk" algorithmic decision-making (ADM) systems. Understanding how …

This article offers several contributions to the interdisciplinary project of responsible
research and innovation in data science and AI. First, it provides a critical analysis of current …

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

Impact assessments have received particular attention on both sides of the Atlantic as a tool
for implementing algorithmic accountability. The aim of this paper is to address how Data …

Central to a number of scholarly, regulatory, and public conversations about algorithmic
accountability is the question of who should have access to documentation that reveals the …