arXiv subjects

Rene Abraham

Publications and source records attributed to Rene Abraham.

3 recordsLinked to original sources

arXiv2025

AI Exchange Platforms

The rapid integration of Artificial Intelligence (AI) into organizational technology frameworks has transformed how organizations engage with AI-driven models, influencing both operational performance and strategic innovation. With the advent of foundation models, the importance of structured platforms for AI model exchange has become paramount for organizational efficacy and adaptability. However, a comprehensive framework to categorize and understand these platforms remains underexplored. To address this gap, our taxonomy provides a structured approach to categorize AI exchange platforms, examining key dimensions and characteristics, as well as revealing interesting interaction patterns between public research institutions and organizations: Some platforms leverage peer review as a mechanism for quality control, and provide mechanisms for online testing, deploying, and customization of models. Our paper is beneficial to practitioners seeking to understand challenges and opportunities that arise from AI exchange platforms. For academics, the taxonomy serves as a foundation for further research into the evolution, impact, and best practices associated with AI model sharing and utilization in different contexts. Additionally, our study provides insights into the evolving role of AI in various industries, highlighting the importance of adaptability and innovation in platform design. This paper serves as a critical resource for understanding the dynamic interplay between technology, business models, and user engagement in the rapidly growing domain of AI model exchanges pointing also towards possible future evolution.

cs.SE↗

arXiv2024

Governance of Generative Artificial Intelligence for Companies

Generative Artificial Intelligence (GenAI) like ChatGPT has swiftly entered organizations without adequate governance, posing both opportunities and risks. Limited research addresses organizational governance from both technical and business perspectives. This gap is particularly relevant for international businesses, where differences in regulation, language, and business environments complicate governance. While multiple frameworks for AI governance exist, this needed diversity is lacking for GenAI. This review paper fills this gap by surveying recent literature to better understand the fundamental characteristics of GenAI and to adapt existing governance frameworks specifically to GenAI. The resulting framework delineates scope, objectives, and governance mechanisms designed to both harness business opportunities and mitigate risks associated with GenAI integration. We theorize a distinctive property of GenAI governance: its scope is endogenous to use. Unlike conventional organizational AI, for which governance is organized around a fixed artifact (e.g., model, intended use), GenAI allows users to reconfigure the artifact (e.g., its behavior and risk). Consequently, the object of governance is not fixed ex ante but is partly constituted through use.

cs.AI↗

arXiv2020

Artificial Intelligence Governance for Businesses

Artificial Intelligence (AI) governance regulates the exercise of authority and control over the management of AI. It aims at leveraging AI through effective use of data and minimization of AI-related cost and risk. While topics such as AI governance and AI ethics are thoroughly discussed on a theoretical, philosophical, societal and regulatory level, there is limited work on AI governance targeted to companies and corporations. This work views AI products as systems, where key functionality is delivered by machine learning (ML) models leveraging (training) data. We derive a conceptual framework by synthesizing literature on AI and related fields such as ML. Our framework decomposes AI governance into governance of data, (ML) models and (AI) systems along four dimensions. It relates to existing IT and data governance frameworks and practices. It can be adopted by practitioners and academics alike. For practitioners the synthesis of mainly research papers, but also practitioner publications and publications of regulatory bodies provides a valuable starting point to implement AI governance, while for academics the paper highlights a number of areas of AI governance that deserve more attention.

cs.AI↗