APPRAISE: a Governance Framework for Innovation with Artificial Intelligence Systems

As artificial intelligence (AI) systems increasingly impact society, the EU Artificial Intelligence Act (AIA) is the first legislative attempt to regulate AI systems. This paper proposes a governance framework for organizations innovating with AI systems. Building upon secondary research, the framework aims at driving a balance between four types of pressures that organizations, innovating with AI, experience, and thereby creating responsible value. These pressures encompass AI/technology, normative, value creation, and regulatory aspects. The framework is partially validated through primary research in two phases. In the first phase, a conceptual model is proposed that measures the extent to which organizational tasks result in AIA compliance, using elements from the AIA as mediators and strategic variables such as organization size, extent of outsourcing, and offshoring as moderators. 34 organizations in the Netherlands are surveyed to test the conceptual model. The average actual compliance score of the 34 participants is low, and most participants exaggerate their compliance. Organization size is found to have significant impact on AIA compliance. In phase 2, two case studies are conducted with the purpose of generating in-depth insights to validate the proposed framework. The case studies confirm the interplay of the four pressures on organizations innovating with AI, and furthermore substantiate the governance framework.

Authors

Institutions

Publication Details

Journal
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Published
2024-10-16
DOI
https://doi.org/10.1609/aies.v7i1.31640
Citations
3
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
1.59
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

APPRAISE: a Governance Framework for Innovation with Artificial Intelligence Systems

Diptish Dey, Debarati Bhaumik
3 citations
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Big Data and Business Intelligence
1.59
article

APPRAISE: a Governance Framework for Innovation with Artificial Intelligence Systems

Diptish Dey, Debarati Bhaumik
article en
3 citations

Abstract

As artificial intelligence (AI) systems increasingly impact society, the EU Artificial Intelligence Act (AIA) is the first legislative attempt to regulate AI systems. This paper proposes a governance framework for organizations innovating with AI systems. Building upon secondary research, the framework aims at driving a balance between four types of pressures that organizations, innovating with AI, experience, and thereby creating responsible value. These pressures encompass AI/technology, normative, value creation, and regulatory aspects. The framework is partially validated through primary research in two phases. In the first phase, a conceptual model is proposed that measures the extent to which organizational tasks result in AIA compliance, using elements from the AIA as mediators and strategic variables such as organization size, extent of outsourcing, and offshoring as moderators. 34 organizations in the Netherlands are surveyed to test the conceptual model. The average actual compliance score of the 34 participants is low, and most participants exaggerate their compliance. Organization size is found to have significant impact on AIA compliance. In phase 2, two case studies are conducted with the purpose of generating in-depth insights to validate the proposed framework. The case studies confirm the interplay of the four pressures on organizations innovating with AI, and furthermore substantiate the governance framework.

Proceedings of the AAAI/ACM Conference on AI Ethics and SocietyVol. 7
Amsterdam University of Applied Sciences (NL)
Openalex Percentile: Top 12%
Big Data and Business Intelligence
1.59
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.