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The Cognitive Intern: A Design Study of Professional Judgment in Human-AI Collaboration

Type de record:

dataset
Créateur:
Ann
Éditeur:
Zenodo
Hôte:avatar

Qualitative Interviews, AI‑Generated Reports, and Credibility Annotations

1. Data Summary

This dataset captures the first-use experiences of 42 domain-expert professionals in Pakistan with the platfom, a four-stage multi-agent system (MAS) pipeline. It provides a specialized resource for assessing how expert users judge AI credibility, localization, and technical depth in an emerging market context.

2. Data Composition

The dataset is organized by Participant ID (P01–P42) and includes:

  • 42 AI-Generated Reports (Reports/ folder): Business intelligence documents produced by the MAS pipeline.
  • 42 Anonymized Transcripts (Transcripts/ folder): Clean text records of semi-structured interviews where experts evaluated the reports.
  • Metadata (Metadata/metadata.csv): Details on the age, gender, professional domain, and years of experience for all 42 participants.

3. Annotation Definitions

To ensure the analysis is reproducible, the following definitions were used to categorize expert feedback:

  • Domain error: A factual or logical mistake identified by a participant using their specific professional expertise (e.g., P24 identifying the omission of "Chromite").
  • Localization failure: Missing, incorrect, or culturally insensitive information specific to the Pakistani context (e.g., incorrect tax rates or missing local landmarks).

Note: While the broader study also evaluates Efficiency and Process Transparency, "Domain Error" and "Localization Failure" serve as the primary categorical labels for the machine learning benchmark tasks in this dataset.

https://huggingface.co/data… 4. Benchmarking Tasks

This dataset supports the following research tasks:

  1. Credibility Prediction: Using expert transcripts to predict trust levels in specific AI-generated business outputs.
  2. Localization Quality Scoring: Measuring the accuracy of AI-generated cultural, legal, and economic content for non-Western regions.
  3. Process Transparency Analysis: Evaluating user disorientation during complex multi-agent reasoning phases.

https://huggingface.co/data… 5. Ethics & Privacy

  • Informed Consent: All 42 participants provided explicit informed consent prior to the study.
  • Anonymization: All transcripts and reports have been manually scrubbed of real names, company identities, and sensitive contact information.
  • Institutional Oversight: This research was conducted at the Lab, XYZ University

https://huggingface.co/data… 6. Licensing

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.

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doi.org

Languages

Ndasa

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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