Reviewers for AAAI 2027 are encountering a common issue where submitted papers, despite making empirical claims, lack accompanying code or data for verification. While AAAI's guidelines suggest providing code, the practical enforcement and the necessity of code release are debated among reviewers. Some argue that missing code should not be an automatic rejection, citing legitimate reasons like funding or intellectual property, and that reviewers often lack the time to audit code anyway. However, the inability to verify empirical results significantly impacts reviewer confidence. AI
IMPACT Highlights challenges in reproducibility and verification within AI research, potentially impacting the quality and trustworthiness of published findings.
RANK_REASON Discussion of paper submission and review process for an academic conference. [lever_c_demoted from research: ic=1 ai=1.0]
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