AI evidence extraction

Reimagining the Journal Editorial Process: An AI-Augmented Versus an AI-Driven Future

Galit Shmueli, Soumya Ray · Journal of the Association for Information Systems · 2024

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

2.80

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.17705/1jais.00864

Methodology & findings

Study design

Thought experiment comparing two contrasting scenarios (AI-augmented versus AI-driven futures) for journal editorial processes.

Main result

The paper presents a thought experiment contrasting two futures for AI in journal editorial processes. The authors find that "The AI-augmented scenario envisions systems providing algorithmic predictions and recommendations to enhance human decision-making, offering enhanced efficiency while maintaining human judgment and accountability," whereas "the AI-driven scenario, meanwhile, imagines a fully autonomous and iterative AI" that "risks failing to align with academic values and norms, perpetuating data biases, and neglecting the important social bonds and community practices embedded in and strengthened by the human-led editorial process."

Reports effect sizes.

Research paradigm

Interpretivist/Critical

Author conclusions

The authors conclude that "We conclude by cautioning against the lure of an AI-driven, metric-focused approach, advocating instead for a future where AI serves as a tool to augment human capacity and strengthen the quality of academic discourse. But more broadly, this thought experiment allows us to distill what the editorial process is about: the building of a premier research community instead of chasing metrics and efficiency. It is up to us to guard these values."

Risk of bias

As a thought experiment and editorial piece, traditional bias assessment frameworks do not apply. Potential limitations include: author perspective bias in framing scenarios, lack of stakeholder input data (editors, reviewers, authors were not surveyed), and speculative nature of projections.

Open questions raised

The paper identifies a gap in understanding how AI tools should be integrated into journal editorial processes in ways that align with academic values, transparency, accountability, and community practices. It calls for deeper consideration of the balance between efficiency gains and preservation of human judgment and academic norms.
The paper identifies the need for debate and careful consideration of AI implementation in academic editorial processes, particularly regarding algorithm transparency, appropriate machine learning methods, data privacy and security, and the preservation of academic community values and practices.
The paper identifies the need for careful deliberation about algorithm transparency, appropriate machine learning methods, data privacy and security, and alignment of AI tools with academic values and norms in journal editorial processes.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 77%

Explore related topics

AI Peer Review Peer Review Position Paper