Pith. sign in

REVIEW 2 cited by

SPHERE: An Evaluation Card for Human-AI Systems

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 2504.07971 v1 pith:GDDC2FSO submitted 2025-03-24 cs.HC cs.AI

classification cs.HCcs.AI
keywords evaluationhuman-aispheresystemscardconductedpracticesareas
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In the era of Large Language Models (LLMs), establishing effective evaluation methods and standards for diverse human-AI interaction systems is increasingly challenging. To encourage more transparent documentation and facilitate discussion on human-AI system evaluation design options, we present an evaluation card SPHERE, which encompasses five key dimensions: 1) What is being evaluated?; 2) How is the evaluation conducted?; 3) Who is participating in the evaluation?; 4) When is evaluation conducted?; 5) How is evaluation validated? We conduct a review of 39 human-AI systems using SPHERE, outlining current evaluation practices and areas for improvement. We provide three recommendations for improving the validity and rigor of evaluation practices.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

  1. L2-Bench: An Evaluation Benchmark for Measuring LLM Capabilities in Second Language Education

    cs.CY 2026-07 conditional novelty 7.0 of 10

    L2-Bench provides a 1,000-task, expert-validated rubric benchmark showing that frontier LLMs score 50–86% on applied second-language learning-design competencies, with notable weakness on open-ended tasks.

  2. A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy

    cs.AI 2025-06 conditional novelty 4.0 of 10

    A position paper arguing that LLM-based human-agent systems, not fully autonomous agents, should be the immediate goal for AI development.

Pith tools