Design Process for building AI Products
In the traditional design process, you define user problems, ideate with your team, prototype, and test solutions. However, in AI design, additional steps are crucial, such as assessing whether AI is necessary, selecting the right AI models, and considering AI-specific flows. You also need to account for AI's limitations during prototyping and iterate based on data feedback. Throughout the process, you'll collaborate with new team members like AI researchers, ML engineers, and data scientists. The AI design process demands deeper considerations beyond traditional design approaches.
Summary
The lesson covers the following key topics:
Responsible AI Practices: AI should be safe, secure, humane, and environmentally friendly. Designers must ensure AI systems are reliable, trustworthy, and aligned with human values by addressing issues like bias, transparency, and fairness.
System and Model-Level Alignment: Responsible AI requires testing for biases, fine-tuning models, and building reporting mechanisms to mitigate harm and improve systems through user feedback.
Ethical Case Study - Dove's Keep Beauty Real: The Dove campaign addresses bias in AI, promoting the responsible use of beauty prompts to ensure more realistic portrayals of beauty.
AI Laws and Regulations: We will discuss important AI laws, such as the U.S. Executive Order (October 2023) promoting safe and trustworthy AI, SB 1047 in California, and the EU AI Act, which sets strict standards based on risk levels.
Content Ownership and Copyright: The U.S. copyright office considers purely AI-generated content as public domain, but there are ongoing questions about intellectual property and the balance between AI-generated content and human involvement.
Given how rapidly AI is evolving, we as designers need to play an active role in these conversations. We have an ethical responsibility as designers working with AI, from addressing bias to navigating global regulations and ensuring transparency for users.