REVIEW 5 cited by
Accountability of AI Under the Law: The Role of Explanation
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
read the original abstract
The ubiquity of systems using artificial intelligence or "AI" has brought increasing attention to how those systems should be regulated. The choice of how to regulate AI systems will require care. AI systems have the potential to synthesize large amounts of data, allowing for greater levels of personalization and precision than ever before---applications range from clinical decision support to autonomous driving and predictive policing. That said, there exist legitimate concerns about the intentional and unintentional negative consequences of AI systems. There are many ways to hold AI systems accountable. In this work, we focus on one: explanation. Questions about a legal right to explanation from AI systems was recently debated in the EU General Data Protection Regulation, and thus thinking carefully about when and how explanation from AI systems might improve accountability is timely. In this work, we review contexts in which explanation is currently required under the law, and then list the technical considerations that must be considered if we desired AI systems that could provide kinds of explanations that are currently required of humans.
Forward citations
Cited by 5 Pith papers
-
Governing Well in the Algorithmic Age: The Foundations of Digital Statecraft
Digital statecraft: a framework claiming legitimate governance in the algorithmic age requires states to hold technical ability and legitimate authority together, bounded by a Non-Delegable Core of decisions no algori...
-
MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems
A neuro-symbolic pipeline decomposes user questions, delegates to model explainers, and synthesizes natural-language explanations, achieving moderate stage-wise scores on a diabetes dataset.
-
Statistical Hypothesis Testing for Auditing Robustness in Language Models
A permutation-based hypothesis test on pairwise semantic similarities detects whether LLM outputs shift under arbitrary input or model perturbations.
-
Towards Transparent Ethical AI: A Roadmap for Trustworthy Robotic Systems
The paper argues transparency is fundamental to trustworthy robotics and proposes a framework connecting technical transparency tools to ethical outcomes such as accountability and informed consent.
-
Towards Transparent AI: A Survey on Explainable Large Language Models
A review that groups LLM explainability methods by transformer architecture and discusses their evaluation and applications.
Discussion (0). Sign in to comment.