Scorecard of AI Benchmark Quality

Ayrton San Joaquin; Rokas Gipiškis; Ze Shen Chin

Scorecard of AI Benchmark Quality

Ayrton San Joaquin, Rokas Gipiškis, Ze Shen Chin

Correct Metadata for

Use this form to create a GitHub issue with structured data describing the correction. You will need a GitHub account. Once you create that issue, the correction will be reviewed by a staff member.

⚠️ Mobile Users: Submitting this form to create a new issue will only work with github.com, not the GitHub Mobile app.

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>. You may use <b>...</b> for bold, <i>...</i> for italic, and <url>...</url> for URLs.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the PDF.)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

Effective AI risk assessment relies on the quality of evaluations. Currently, there are large quality differences, such as in construct validity and annotation, between existing benchmarks. In this work, we propose a quality scorecard for benchmarks designed to make this diversity easier to navigate. The scorecard employs two main components: dimensions, which provide granular scores of an evaluation under that dimension, and classifications, which correspond to concrete use-cases ranging from research to post-deployment. By establishing a common language and objective methods, this framework aims to aid in transparency and raise the baseline quality of benchmarks used across the ecosystem.

Anthology ID:: 2026.evaleval-1.25
Volume:: Proceedings of the Workshop on Evaluating Evaluations (EvalEval)
Month:: July
Year:: 2026
Address:: San Diego, CA
Editors:: Mubashara Akhtar, Jan Batzner, Leshem Choshen, Avijit Ghosh, Usman Gohar, Jennifer Mickel, Ichhya Pant, Zeerak Talat, Michelle Lin
Venues:: EvalEval | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 128–160
Language:
URL:: https://aclanthology.org/2026.evaleval-1.25/
DOI:
Bibkey:
Cite (ACL):: Ayrton San Joaquin, Rokas Gipiškis, and Ze Shen Chin. 2026. Scorecard of AI Benchmark Quality. In Proceedings of the Workshop on Evaluating Evaluations (EvalEval), pages 128–160, San Diego, CA. Association for Computational Linguistics.
Cite (Informal):: Scorecard of AI Benchmark Quality (San Joaquin et al., EvalEval 2026)
Copy Citation:
PDF:: https://aclanthology.org/2026.evaleval-1.25.pdf

PDF Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{san-joaquin-etal-2026-scorecard,
    title = "Scorecard of {AI} Benchmark Quality",
    author = "San Joaquin, Ayrton  and
      Gipi{\v{s}}kis, Rokas  and
      Chin, Ze Shen",
    editor = "Akhtar, Mubashara  and
      Batzner, Jan  and
      Choshen, Leshem  and
      Ghosh, Avijit  and
      Gohar, Usman  and
      Mickel, Jennifer  and
      Pant, Ichhya  and
      Talat, Zeerak  and
      Lin, Michelle",
    booktitle = "Proceedings of the Workshop on Evaluating Evaluations ({E}val{E}val)",
    month = jul,
    year = "2026",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.evaleval-1.25/",
    pages = "128--160",
    ISBN = "979-8-89176-429-3",
    abstract = "Effective AI risk assessment relies on the quality of evaluations. Currently, there are large quality differences, such as in construct validity and annotation, between existing benchmarks. In this work, we propose a quality scorecard for benchmarks designed to make this diversity easier to navigate. The scorecard employs two main components: dimensions, which provide granular scores of an evaluation under that dimension, and classifications, which correspond to concrete use-cases ranging from research to post-deployment. By establishing a common language and objective methods, this framework aims to aid in transparency and raise the baseline quality of benchmarks used across the ecosystem."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="san-joaquin-etal-2026-scorecard">
    <titleInfo>
        <title>Scorecard of AI Benchmark Quality</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ayrton</namePart>
        <namePart type="family">San Joaquin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Rokas</namePart>
        <namePart type="family">Gipiškis</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ze</namePart>
        <namePart type="given">Shen</namePart>
        <namePart type="family">Chin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Workshop on Evaluating Evaluations (EvalEval)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Mubashara</namePart>
            <namePart type="family">Akhtar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jan</namePart>
            <namePart type="family">Batzner</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Leshem</namePart>
            <namePart type="family">Choshen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Avijit</namePart>
            <namePart type="family">Ghosh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Usman</namePart>
            <namePart type="family">Gohar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jennifer</namePart>
            <namePart type="family">Mickel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ichhya</namePart>
            <namePart type="family">Pant</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Zeerak</namePart>
            <namePart type="family">Talat</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Michelle</namePart>
            <namePart type="family">Lin</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">San Diego, CA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-429-3</identifier>
    </relatedItem>
    <abstract>Effective AI risk assessment relies on the quality of evaluations. Currently, there are large quality differences, such as in construct validity and annotation, between existing benchmarks. In this work, we propose a quality scorecard for benchmarks designed to make this diversity easier to navigate. The scorecard employs two main components: dimensions, which provide granular scores of an evaluation under that dimension, and classifications, which correspond to concrete use-cases ranging from research to post-deployment. By establishing a common language and objective methods, this framework aims to aid in transparency and raise the baseline quality of benchmarks used across the ecosystem.</abstract>
    <identifier type="citekey">san-joaquin-etal-2026-scorecard</identifier>
    <location>
        <url>https://aclanthology.org/2026.evaleval-1.25/</url>
    </location>
    <part>
        <date>2026-07</date>
        <extent unit="page">
            <start>128</start>
            <end>160</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T Scorecard of AI Benchmark Quality
%A San Joaquin, Ayrton
%A Gipiškis, Rokas
%A Chin, Ze Shen
%Y Akhtar, Mubashara
%Y Batzner, Jan
%Y Choshen, Leshem
%Y Ghosh, Avijit
%Y Gohar, Usman
%Y Mickel, Jennifer
%Y Pant, Ichhya
%Y Talat, Zeerak
%Y Lin, Michelle
%S Proceedings of the Workshop on Evaluating Evaluations (EvalEval)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, CA
%@ 979-8-89176-429-3
%F san-joaquin-etal-2026-scorecard
%X Effective AI risk assessment relies on the quality of evaluations. Currently, there are large quality differences, such as in construct validity and annotation, between existing benchmarks. In this work, we propose a quality scorecard for benchmarks designed to make this diversity easier to navigate. The scorecard employs two main components: dimensions, which provide granular scores of an evaluation under that dimension, and classifications, which correspond to concrete use-cases ranging from research to post-deployment. By establishing a common language and objective methods, this framework aims to aid in transparency and raise the baseline quality of benchmarks used across the ecosystem.
%U https://aclanthology.org/2026.evaleval-1.25/
%P 128-160

Download as File

Markdown (Informal)

[Scorecard of AI Benchmark Quality](https://aclanthology.org/2026.evaleval-1.25/) (San Joaquin et al., EvalEval 2026)

Scorecard of AI Benchmark Quality (San Joaquin et al., EvalEval 2026)

ACL

Ayrton San Joaquin, Rokas Gipiškis, and Ze Shen Chin. 2026. Scorecard of AI Benchmark Quality. In Proceedings of the Workshop on Evaluating Evaluations (EvalEval), pages 128–160, San Diego, CA. Association for Computational Linguistics.