what variables measure management practices influence on AI ethics ?

Introduction

The influence of management practices on AI ethics is a critical area of research, especially as AI systems become more integrated into various sectors. Understanding the variables that measure this influence can help ensure that AI systems are developed and deployed in a manner that is ethical, fair, and trustworthy.

Key Insights

Compliance with Ethical Principles:
- Ensuring compliance with ethical principles such as privacy, fairness, and explainability is crucial for the quality and trustworthiness of AI systems. These principles directly impact the accuracy and fairness of AI decisions, especially when dealing with different types of data (tabular vs. text).
Managerial Implications in Financial AI:
- In financial portfolio management, the use of AI introduces ethical dilemmas related to responsibility, accountability, and risk. Effective management practices must include new performance metrics, competence evaluations, and incentive structures for AI developers to address these ethical concerns.
Knowledge, Attitude, and Practice (KAP) in Ethics Implementation:
- The Knowledge-Attitude-Practice (KAP) model is a comprehensive framework for measuring the implementation of ethics among AI researchers. This model includes dimensions such as knowledge, attitude, and practical experience, which are essential for ensuring ethical AI development and deployment.

Conclusion

The influence of management practices on AI ethics can be measured through several key variables. Compliance with ethical principles like privacy, fairness, and explainability is fundamental. In specific sectors like financial portfolio management, new performance metrics and incentive structures are necessary to address ethical dilemmas. Additionally, the Knowledge-Attitude-Practice (KAP) model provides a robust framework for assessing the implementation of ethics among AI researchers. Together, these insights highlight the multifaceted approach required to manage AI ethics effectively.

What variables measure management practices influence on AI ethics ?

Summary

Introduction

Key Insights

Conclusion

Sources and full results

On the compliance with ethical principles in AI

Compliance with ethical principles in AI, such as privacy, fairness, and explainability, significantly impacts the quality of AI system decisions and the accuracy of AI system decisions.

Artificial Intelligence and Ethics in Portfolio Management

AI-enhanced algorithms in financial portfolio management raise ethical dilemmas related to human versus machine responsibility, accountability, and risk, requiring a new dimension of performance measurement and management.

Development, Reliability, and Structural Validity of the Scale for Knowledge, Attitude, and Practice in Ethics Implementation Among AI Researchers: Cross-Sectional Study

The KAP-EI scale effectively measures knowledge, attitude, and practice of ethics implementation among medical AI researchers, with good reliability and structural validity.

AI virtues - The missing link in putting AI ethics into practice

This paper proposes four basic AI virtues, prudence and fortitude, to improve ethical decision-making in AI research and development.

The Influence of AI Technology Acceptance and Ethical Awareness towards Intention to Use

AI technology acceptance and ethical awareness influence users' intention to use AI, with perceived usefulness and ease of use significantly influencing their intention to use AI.

Implementing AI Ethics in Practice: An Empirical Evaluation of the RESOLVEDD Strategy

The RESOLVEDD strategy, when forced upon developers, facilitated ethical consideration and increased responsibility in AI system development projects.

The Current State of Industrial Practice in Artificial Intelligence Ethics

High-level guidelines and tools for managing artificial intelligence ethics can help industry organizations create more ethical AI systems, as shown by a survey of 211 software companies.

“This is Just a Prototype”: How Ethics Are Ignored in Software Startup-Like Environments

AI ethics in software development are currently ignored in startup-like environments, but existing good practices like documentation and error handling can support its implementation.

Ethical Management of Artificial Intelligence

The EMMA framework provides a starting point for research on AI-related ethical concerns and highlights future research opportunities.

Principles alone cannot guarantee ethical AI

Principles alone cannot guarantee ethical AI, as AI development lacks common aims, fiduciary duties, professional history, and accountability mechanisms compared to medicine.

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