AP4AI: accountability principles for artificial intelligence in the internal security domain

AKHGAR, Babak, BAYERL, Petra Saskia, MOUNIER, Grégory, LINDEN, Ruth and WAITES, Ben (2022). AP4AI: accountability principles for artificial intelligence in the internal security domain. European Law Enforcement Research Bulletin, 22 (6).

Preview

PDF Akhgar-AP4AI_Accountability_Principles(VoR).pdf - Published Version Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview

Abstract

The challenge for internal security practitioners including law enforcement and the justice sector is to determine how to capitalise on the opportunities offered by Artificial Intelligence (AI) and Machine Learning to improve the way investigators, prosecutors, judges or border guards carry out their mission of keeping citizens safe and rendering justice while, at the same time, safeguarding and demonstrating true accountability of AI use towards society. The AP4AI (Accountability Principles for Artificial Intelligence) Project addresses this challenge by offering a global Framework for AI Accountability for Policing, Security and Justice. The AP4AI Framework is grounded in empirically verified Accountability Principles for AI as carefully researched and accessible standard, which supports internal security practitioners in implementing AI and Machine Learning tools in an accountable and transparent manner and in line with EU values and fundamental rights. The principles are universal and jurisdiction-neutral to offer guidance for internal security and justice practitioners globally in support of existing governance and accountability mechanisms through self-audit, monitoring and review. This paper presents the project approach as well as current results of the project and their relevance for the internal security domain.

Item Type:	Article
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	07 Dec 2022 16:10
Last Modified:	12 Oct 2023 08:04
URI:	https://shura.shu.ac.uk/id/eprint/31123

Actions (login required)

View Item

Downloads

Publication Metrics