AI Governance for Businesses

@article{Schneider2020AIGF,
  title={AI Governance for Businesses},
  author={Johannes Schneider and Rene Abraham and Christian Meske},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.10672}
}
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… 

Figures from this paper

Putting AI Ethics into Practice: The Hourglass Model of Organizational AI Governance

An AI governance framework, the hourglass model of organizational AI governance, is presented, designed to help organizations deploying AI systems translate ethical AI principles into practice and align their AI systems and processes with the forthcoming European AI Act.

Contemporary Approaches for AI Governance in Financial Institutions

Risks and limits connected with the application of AI models in financial modeling and its application in practice are explored and approaches to overcome limitations and potential biases are presented.

Operationalising AI governance through ethics-based auditing: an industry case study

Ethics-based auditing (EBA) is a structured process whereby an entity’s past or present behaviour is assessed for consistency with moral principles or norms. Recently, EBA has attracted much

AI Governance in the System Development Life Cycle: Insights on Responsible Machine Learning Engineering

In this study we explore the incorporation of artificial intelligence (AI) governance to system development life cycle (SDLC) models. We conducted expert interviews among AI and SDLC professionals

Defining organizational AI governance

Artificial intelligence (AI) governance is required to reap the benefits and manage the risks brought by AI systems. This means that ethical principles, such as fairness, need to be translated into

Continuous Auditing of Artificial Intelligence: a Conceptualization and Assessment of Tools and Frameworks

CAAI is defined as a (nearly) real-time electronic support system for auditors that continuously and automatically audits an AI system to assess its consistency with relevant norms and standards.

Ai Governance: are Chief AI Officers and AI Risk Officers Needed?

While AI provides many business opportunities across industries, the organizational implications of AI are still largely unclear. We investigate governance roles related to AI use in practice, and

References

SHOWING 1-10 OF 96 REFERENCES

Regulating Artificial Intelligence Systems: Risks, Challenges, Competencies, and Strategies

Artificial intelligence technology (or AI) has developed rapidly during the past decade, and the effects of the AI revolution are already being keenly felt in many sectors of the economy. A growing

Principles and business processes for responsible AI

Toward artificial governance? The role of artificial intelligence in shaping the future of corporate governance

  • Michael Hilb
  • Political Science
    Journal of Management and Governance
  • 2020
The article explores the impact of the ongoing progress and adaptation of artificial intelligence on the practice of the corporate governance. It applies three lenses to artificial governance—the

Toward an Understanding of Responsible Artificial Intelligence Practices

In this study, 10 responsible AI cases were selected from different industries to better understand the use of responsible AI in practices and four responsible AI practices are identified, including governance, ethically design solutions, risk control and training and education.

A governance model for the application of AI in health care

A governance model is proposed that aims to not only address the ethical and regulatory issues that arise out of the application of AI in health care, but also stimulate further discussion about governance ofAI in health health care.

Designing data governance

An overall framework for data governance is provided that can be used by researchers to focus on important data governance issues, and by practitioners to develop an effective data governance approach, strategy and design.

Artificial Intelligence Regulation: A Meta-Framework for Formulation and Governance

A meta-framework for Artificial Intelligence (AI) regulation that encompasses all stages of international public policy-making, from formulation to sustainable governance is presented, providing a trustworthy lens for societies to think collectively and make informed policy decisions related to what, when, and how the uses and applications of AI should be regulated.

Understanding the interplay of artificial intelligence and strategic management: four decades of research in review

A comprehensive framework is introduced that integrates and synthesizes existing concepts and proposes promising research avenues for studying the quantifiable effects of the interplay of AI and strategic management based on the developed framework.

Model Governance: Reducing the Anarchy of Production ML

This paper motivates the need for, defines the problem of, and proposes a solution for Model Governance in production ML, and shows that through the approach one can meaningfully track and understand the who, where, what, when, and how an ML prediction came to be.

How to Define and Execute Your Data and AI Strategy

Recommendations address the core enablers for data and AI capabilities, from setting the ambition level to hiring the right talent and defining the AI organization and operating model.
...