Working on AI/ML initiatives is the dream of many individuals and companies. Stories of amazing AI initiatives are all over the web and those who claim ownership of these are sought after for speaking, offered handsome positions andcommand respect from their peers.
In reality, AI work is highly uncertain and there are many types of risks that are associated with AI/ML work.
“If you understand where risks may be lurking, ill-understood, or simply unidentified, you have a better chance of catching them before they catch up with you.” — McKinsey 
7 Dimensions of AI Risk
- Strategy Risk
- Financial Risk
- Technical Risk
- People and Process Risk
- Trust and Explainability Risk
- Compliance and Regulatory Risk
- Ethical Risk
 Confronting the risks of artificial intelligence, McKinsey. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/confronting-the-risks-of-artificial-intelligence
 AI and risk management, Deloitte. https://www2.deloitte.com/global/en/pages/financial-services/articles/gx-ai-and-risk-management.html
 Ulrika Jagare. Data Science Strategy for Dummies, 2019.
 Derisking machine learning and artificial intelligence, McKinsey. https://www.mckinsey.com/business-functions/risk/our-insights/derisking-machine-learning-and-artificial-intelligence
 Understanding Model Risk Management for AI and Machine Learning, EY. https://www.ey.com/en_us/banking-capital-markets/understand-model-risk-management-for-ai-and-machine-learning
7 Types of AI Risk and How to Mitigate their Impact was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.