Practical Solutions for AI Risk Management
Unified Framework for AI Risks
AI-related risks are a concern for policymakers, researchers, and the public. A unified framework is crucial for consistent terminology and clarity, enabling organizations to create thorough risk mitigation strategies and policymakers to enforce effective regulations.
AI Risk Repository
Researchers from MIT and the University of Queensland have developed an AI Risk Repository that compiles 777 risks from 43 taxonomies into an accessible online database. This resource offers a comprehensive framework to understand and manage the various risks posed by AI systems.
Comprehensive AI Risk Database
A comprehensive search was conducted to classify AI risks, resulting in an AI Risk Database with two taxonomies: Causal Taxonomy and Domain Taxonomy. This database aids policymakers, auditors, academics, and industry professionals in filtering and analyzing specific AI risks.
Structured Foundation for AI Risk Mitigation
The study offers detailed resources, including a website and database, to help understand and address AI-related risks. The AI Risk Database categorizes risks into high-level and mid-level taxonomies, aiding in targeted mitigation efforts.
Value of AI Governance Tool
Robust AI Governance Tool
MIT Researchers have released a robust AI governance tool to define, audit, and manage AI risks. This tool provides a foundation for discussion, research, and policy development, aiding in targeted mitigation efforts.
AI Integration for Business Advancement
Discover how AI can redefine your way of work, identify automation opportunities, define KPIs, select an AI solution, and implement gradually. For AI KPI management advice and insights into leveraging AI, connect with us at hello@itinai.com or stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom.
AI for Sales Processes and Customer Engagement
Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com.
