1. Understand your data
DO: Start by gaining a deep understanding of your AI training data and where it comes from. Be confident it was collected or created in an ethical and secure manner.
DO: Invest time in AI data preprocessing and cleaning. This will ensure that your model doesn’t inadvertently learn and propagate harmful or incorrect information.
2. Spend time developing thorough content guidelines
DO: Develop and adhere to ethical guidelines for content generation. Clearly define what is acceptable and unacceptable content for your specific generative AI project.
DO: Regularly review and update your content guidelines in line with today’s ever-evolving AI environment as well as the latest data- and AI-related regulations such as the EU AI Act and GDPR (General Data Protection Regulation).
3. Continuously evaluate model outputs
DO: Implement a feedback loop to evaluate the quality and appropriateness of generated content. Regularly involve human reviewers to provide your generative AI model with guidance and feedback that it can learn from.
DO: Use metrics that are specific to your generative AI project goals, such as relevance and coherence.
4. Mitigate bias
DO: Prioritize fairness and inclusivity in content generation, and strive to minimize any potential type of discrimination or harm.
5. Maintain documentation and transparency
DO: Maintain up-to-date documentation about your generative AI model and its fine-tuning processes, including its model architecture and training data sources.
DO: Be transparent about your AI system's limitations, and clearly communicate that its outputs are machine-generated.