When developing an AI product, start by clearly defining the problem it's meant to solve and the value it will bring to users. Choose the most appropriate AI technologies and methods that match your data and objectives. Design your AI solution with clear metrics for success and integrate it into a user-friendly product. Finally, rigorously evaluate the impact of your AI, using feedback to make iterative improvements to ensure it meets user needs and performs reliably in real-world scenarios.
At each stage, an AI product developer should think about the range of issues likely to be encountered with the product. This could include the initial licensing of any technology and the potential patentability of any novel technology; IP, copyright and fair use that may be relevant to any data relied upon by the technology; and privacy and data security issues, particularly ensuring that data used in AI systems is collected, processed and stored in compliance with applicable privacy laws and regulations. AI systems should be designed to uphold principles of data minimization, consent, and individual rights to access and delete personal information. Depending on the type of data involved, consider appropriate protections and consents that would need to be sought, particularly where it may be protected data (e.g., health) or customer data.
These concerns and risks should then be factored into the relevant contracts and other third-party agreements, ensuring that customers or parties using the product are put on appropriate notice and are appropriately responsible for their own data. And when marketing the product, the organization should take care that its representations about the product and data use are fair and not misleading.