One of the main AI risks is to think they don't exist. Systems thinking, comprehensive approach, and transparency, are key factors. The utmost importance must be given to vendors' track records, their technical expertise, ethical relevancy, risk identification/mitigation ability, and their auditability/liability over AI models, incidents, and privacy compliance... Do remember, AI risk management is much more than a matter of IT.

Enfrentei propostas de IA de alto risco ao adotar uma abordagem meticulosa e estratégica. Primeiramente, conduzi uma análise devida rigorosa dos fornecedores para avaliar sua reputação e credibilidade. Consultei meu network para saber algo desses fornecedores. Revisei minha politica de dados e a dos fornecedores. Exigi total transparência no uso de dados e na lógica de algoritmos para garantir conformidade regulatória. Além disso, defini métricas de desempenho claras e adicionei cláusulas contratuais para contingências, como auditorias regulares e revisões de segurança. Finalmente, mantive uma comunicação aberta com stakeholders para adaptar estratégias conforme necessário, minimizando riscos e assegurando o sucesso do projeto.

In my experience, the best way to safeguard your project is to treat vendor selection like you're choosing a long-term relationship partner. Trust me, it's not that different! - Look for shared values: Does their ethical stance align with yours? - Test compatibility: Run a small pilot project before committing - Meet the family: Talk to their other clients, especially the ones they don't showcase I once dodged a bullet by calling a vendor's former client who wasn't on their reference list. The stories I heard... let's just say it saved us from a very expensive mistake!

To safeguard my project when considering high-risk AI proposals from external vendors: 1. Conduct thorough due diligence to assess vendor credibility and solution viability. 2. Implement pilot tests to evaluate the technology's impact before full integration. 3. Establish clear contractual agreements with defined performance metrics and exit strategies.

High-risk AI proposals can be minefields, but with the right strategies, you can protect your project and ensure success. Start by conducting thorough due diligence on vendors, scrutinizing their track record, financial stability, and client feedback. Insist on transparency in the AI’s decision-making processes and data handling to ensure compliance with regulations. Set clear performance benchmarks in your contracts and include contingency plans to address potential failures. Engage cross-functional teams in evaluation and decision-making, ensuring that all aspects of the project are covered. Begin with a pilot project to validate the AI's effectiveness before full-scale implementation. These steps will help mitigate risks.