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Artificial intelligence has been hailed as a powerful tool, capable of propelling startups to unprecedented levels of growth and speed. However, responsible implementation of innovative technologies can be complex. To address this, Responsible Innovation Labs (RIL), in collaboration with the United States Department of Commerce, has devised a five-step protocol for the responsible use of AI by startups. Additionally, RIL has partnered with 35 venture capital firms to encourage their portfolio companies to adopt this protocol. Gaurab Bansal, Executive Director of Responsible Innovation Labs, emphasizes the importance of considering risk when discussing funding for AI startups.
To safeguard your business while leveraging AI, here are the five steps, along with insights from Bansal:
1. Obtain organizational buy-in: It is crucial to align all key stakeholders within your business. RIL recommends holding inclusive "Responsible AI Key Stakeholder Forums" to gather perspectives from various departments. According to Bansal, this inclusive approach minimizes risk and enables swift crisis response.
2. Forecast risks and benefits: Upon securing buy-in, conduct a thorough risk/benefit assessment, considering reliability, security, bias, data sets, and alignment with company values. Bansal emphasizes the importance of forecasting and addressing potential risks to prepare for investor and regulatory inquiries.
3. Audit and test your products: After assessing risks and benefits, build and test your AI product. Regular testing and auditing are crucial to understanding and mitigating potential risks. According to RIL, monitoring for errors, disclosing model information, involving humans in decision-making, and limiting usage are essential. Bansal stresses the baseline responsibility and preparedness required before releasing products.
4. Foster trust through transparency: Clear communication regarding the alignment of AI usage with your company's mission is essential. RIL recommends publishing a value statement and a Model Card for each new AI model version to convey usage, risks, and safety evaluations. Bansal highlights the critical nature of transparency, particularly when selling AI-powered solutions to enterprise-level businesses.
5. Make regular and ongoing improvements: Responsibility doesn't end after initial implementation. Maintaining vigilance involves transparently communicating progress, forecasting new risks, and continually testing AI products for bias and efficiency. According to Bansal, transparency in communicating improvements is key to nurturing customer relationships and fostering trust.
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