Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing

01/03/2020
by   Inioluwa Deborah Raji, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/27/2018

Robust Artificial Intelligence and Robust Human Organizations

Every AI system is deployed by a human organization. In high risk applic...
research
10/23/2020

Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure

Rising concern for the societal implications of artificial intelligence ...
research
12/06/2019

An Algorithmic Equity Toolkit for Technology Audits by Community Advocates and Activists

A wave of recent scholarship documenting the discriminatory harms of alg...
research
01/06/2019

Towards Self-constructive Artificial Intelligence: Algorithmic basis (Part I)

Artificial Intelligence frameworks should allow for ever more autonomous...
research
03/01/2021

Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence

The societal and ethical implications of the use of opaque artificial in...
research
12/10/2019

Dynamic Algorithmic Service Agreements Perspective

A multi-disciplinary understanding of the concepts of identity, agency, ...
research
12/28/2016

The Predictron: End-To-End Learning and Planning

One of the key challenges of artificial intelligence is to learn models ...

Please sign up or login with your details

Forgot password? Click here to reset