Reviewer package accompanying a manuscript submitted for double-blind peer review.The manuscript proposes a five-element record of each human adjudication of a machine-generated classification in AI-assisted spatial analysis workflows: decision point, machine proposal, human disposition (affirm / reject / modify), stated justification, and downstream consequence. This deposit contains the record itself rather than a description of it, so that the paper’s central claim — that such adjudications should be open to inspection — can be checked against the artifact.Contents:schema.json — the five-element specification, with the determination each element supports and the design requirements for append-only integrity, human-readable primacy, and coupling to lock events.blank_template.csv — an empty implementation template for reuse.trace_A.json — a worked adjudication from a documentary registry pipeline, in which a human rejects a generative model’s exclusion verdict.trace_B.json — a worked adjudication from a rule-based pipeline, in which a human modifies a pattern-matched classification.negative_case.json — an unlogged exclusion, included to show what the record makes visible by contrast.README.md — field definitions and reuse notes.The two traces come from pipelines whose proposers differ in kind — one a stochastic generative model, one a deterministic rule set — so the schema is demonstrated across both.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
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