Pith. sign in

REVIEW 1 cited by

Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2207.01482 v1 pith:4TGX5GQZ submitted 2022-06-10 cs.CY cs.HCcs.LG

Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance

classification cs.CY cs.HCcs.LG
keywords systemstransparentapproachregulatorytechnologiststransparencywhataddress
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

Increasingly, laws are being proposed and passed by governments around the world to regulate Artificial Intelligence (AI) systems implemented into the public and private sectors. Many of these regulations address the transparency of AI systems, and related citizen-aware issues like allowing individuals to have the right to an explanation about how an AI system makes a decision that impacts them. Yet, almost all AI governance documents to date have a significant drawback: they have focused on what to do (or what not to do) with respect to making AI systems transparent, but have left the brunt of the work to technologists to figure out how to build transparent systems. We fill this gap by proposing a novel stakeholder-first approach that assists technologists in designing transparent, regulatory compliant systems. We also describe a real-world case-study that illustrates how this approach can be used in practice.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. White Box Evidence Packages for Policy Audit Reports

    cs.CY 2026-07 conditional novelty 6.0

    In a 60-case controlled audit study, adding white-box model evidence to an LLM auditor increased citation volume but weakened passage grounding and raised evidence misuse, while a shuffled control showed reports can s...