Ethical Risk Factors and Mechanisms in Artificial Intelligence Decision Making

Area of Science:

• Computer Science
• Ethics
• Sociology

Background:

• Artificial intelligence (AI) offers societal benefits but presents ethical challenges, including algorithmic discrimination, data bias, and accountability issues.
• Qualitative research is crucial for understanding the nuanced ethical risks inherent in AI decision-making processes.

Purpose of the Study:

• To identify and analyze the ethical risk factors in AI decision-making.
• To construct a risk-factor model for AI ethical risks using grounded theory.
• To explore risk interaction mechanisms via system dynamics and propose management strategies.

Main Methods:

• Qualitative research and grounded theory for risk factor identification and model construction.
• System dynamics modeling to explore the interaction mechanisms between AI ethical risks.
• Analysis of risk governance elements for effective intervention.

Main Results:

• Technological uncertainty, incomplete data, and management errors identified as primary sources of AI ethical risks.
• Risk governance interventions effectively mitigate social risks stemming from algorithmic, technological, and data-related issues.
• A comprehensive risk-factor model for AI decision-making ethical risks was developed.

Conclusions:

• Proposing governance strategies for AI ethical risks from management, research, and development perspectives.
• Highlighting the importance of addressing technological, data, and management factors to ensure responsible AI.
• Emphasizing the role of risk governance in mitigating societal risks associated with AI implementation.