README.md

metadata

license: cc-by-4.0
task_categories:
  - text-classification
language:
  - en
tags:
  - evaluation
  - rubrics
  - inter-annotator-agreement
  - llm-as-a-judge
  - benchmark-annotation
pretty_name: AI Summit Barcelona 2026 — Rubric Agreement Workshop
size_categories:
  - n<1K

AI Summit Barcelona 2026 — Rubric Agreement Workshop

Every evaluation filed during a live 80-minute workshop at AI Summit Barcelona on 22 September 2026, in which the room scored one expert-authored benchmark item exactly the way an annotator does it on EuroExec (arXiv:2608.04549), Sovrano's expert-graded benchmark of frontier models on European executive decisions.

Attendees read one executive task and its graded checklist, then blind-scored three frontier-model answers: five dimensions from 1 to 5, then Hit / Partial / Miss on every checklist row, then a ranking and a written rationale. Which model wrote which answer was hidden until the end, and the A/B/C labels were the same on every screen, so one attendee's numbers sit next to another's.

Sovrano's own evaluation of the same three answers is published alongside, which is what makes this comparable: scorings.jsonl carries each attendee's Cohen's κ against that expert key, over the same Hit / Partial / Miss calls. On EuroExec, two blind scorers below κ 0.7 halt production of an item.

What's in it

File	Rows	One row is
scorings.jsonl	16	One attendee's finished evaluation, with their agreement against the expert key
dimension_scores.jsonl	240	One attendee scoring one response on one dimension, 1–5
coverage.jsonl	240	One attendee's Hit / Partial / Miss call on one checklist row of one response
responses.jsonl	3	One of the three answers under test, with the model that wrote it
checklist.jsonl	5	One row of the graded checklist, and whether it is critical
dimensions.jsonl	5	One of the five scoring dimensions, as it was shown
golden.jsonl	3	Sovrano's expert evaluation of one response, with a note per checklist row

The task all three answers respond to:

Act as a senior product strategist for a B2B subscription SaaS Platform providing healthcare referral tracking system using AI in a startup that just received seed funding of €1M. You have piloted the product among 50 hospitals in Germany for a one-time trial fee of €2500. From July onwards you would like to start billing all 50 customers €250 monthly as a subscription fee. However, your customer support team has reported that user reviews have been increasingly overwhelming and unsatisfactory and that there are at least 30% 1 star (very bad) ratings coming in. The board expects evidence-based recommendations before subscription billing begins. Your task is to create a product strategy report based on customer feedback and subscription launch readiness. Diagnose the business situation identifying at least 5 risks to customer journey, subscription sign ups and product adoption. Design a review analysis process capable of reviewing 50 reviews per day. The process must categorize per theme, its frequency, calculate severity and distinguish feature requests from defects. Define a measurement framework containing the KPIs and metrics you come up with. Do not invent perfect outcomes, explicitly identify uncertainties and additional data that should be collected before scaling.

Caveats that matter

• 37 unfinished evaluations are not here. A scoring is exported once the attendee submitted it, which the app only accepts when every dimension is scored, every checklist row is marked and a rationale is written. Partial work was left out rather than published as if it were a considered call.
• Scoring was not blind. The workshop app holds nothing back: the model behind each answer and the expert key are on the results screen from the start, and an attendee could read them before scoring. Most did not — the screens lead you through scoring first — but this data cannot be used as evidence about blind annotation, and the figures here should be read as what a room of non-specialists produced with the key available.
• The expert key is a small sample of one. It is Sovrano's grading of three answers against five rows. κ against it says how closely a reader applied the same checklist, not who was right.
• Attendees are not expert annotators. They are conference attendees working at speed, which is the point of the comparison with EuroExec, not a defect to correct for.
• Contributors are pseudonymous. contributor_id is a random per-browser identifier. No personal data appears in this dataset and nothing in it can be traced back to a person. Attendees gave their details to the organisers on the way in; those were stored separately, were never linked to a contributor id, and are not part of this release.

Licence

CC BY 4.0. Please cite this dataset and EuroExec if you build on it.