New Framework Developed to Systematically Analyze AI Risks from News Data
A new framework has been developed to systematically analyze AI risks using news data.
Key Points
- • A unified framework for understanding AI risks was introduced, addressing fragmented existing research.
- • An enriched AI risk event database was created to systematically analyze AI incidents.
- • The study emphasizes the need for continuous monitoring by AI practitioners to manage risks effectively.
- • Findings underline the prevalence of discrimination and ethical concerns in AI-related incidents.
A recent study has introduced a unified ontological and explainable framework specifically designed to identify, categorize, and analyze risks associated with artificial intelligence (AI) through the lens of news data. This innovative approach is timely, given the increasing complexities and integrations of AI across numerous sectors, which have escalated concerns about potential risks.
The research unveils an ontological risk model that not only provides a comprehensive representation of AI risks but also facilitates the creation of an enriched AI risk event database. This database is the result of systematically mining and structuring raw news data related to AI incidents, allowing for better analysis of the underlying risks. Utilizing visual analytics alongside explainable machine learning techniques, the study identifies key characteristics and driving factors linked to AI risks, revealing that many incidents are closely tied to ethical and discrimination-related issues.
The fundamental findings underscore the necessity for continuous monitoring of AI-related risks and highlight the critical role of AI practitioners in risk management. The research advocates for informed policy development and the establishment of regulatory frameworks that can address the multifaceted challenges posed by AI technologies in the rapidly evolving landscape of artificial intelligence.