English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Rising concern for the societal implications of artificial intelligencesystems has inspired a wave of academic and journalistic literature in whichdeployed systems are audited for harm by investigators from outside theorganizations deploying the algorithms. However, it remains challenging forpractitioners to identify the harmful repercussions of their own systems priorto deployment, and, once deployed, emergent issues can become difficult orimpossible to trace back to their source. In this paper, we introduce aframework for algorithmic auditing that supports artificial intelligence systemdevelopment end-to-end, to be applied throughout the internal organizationdevelopment lifecycle. Each stage of the audit yields a set of documents thattogether form an overall audit report, drawing on an organization's values orprinciples to assess the fit of decisions made throughout the process. Theproposed auditing framework is intended to contribute to closing theaccountability gap in the development and deployment of large-scale artificialintelligence systems by embedding a robust process to ensure audit integrity.

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