47. Designing AI Experiences: What to Consider (feat. Caleb Sponheim PhD, NN/g) Designing AI Experiences: A Conversation with Caleb Sponheim Summary: This document summarizes a podcast interview with Caleb Sponheim, a UX specialist, focusing on the role of designers in the age of AI. The conversation covers the current state of AI tools, best practices for designing AI experiences, the importance of AI safety , and advice for designers looking to break into the field. Chapter 1: Caleb's Background and Research Focus - Summary: This chapter introduces Caleb Sponheim and his background in computational neuroscience, explaining how his expertise led him to research AI's impact on UX design. Sections: ** Caleb's Journey into AI Research:** - Caleb explains his transition from a decade-long career as a neuroscientist to researching AI's role in UX design. His PhD in computational neuroscience provided a strong foundation for understanding AI's underlying mechanisms. Computational Neuroscience Explained: - Caleb defines computational neuroscience as the application of mathematical approaches to understanding brain function. He highlights the strong connection between neuroscience and AI, emphasizing that many AI advancements stem from insights gained in neuroscience. This includes the development of artificial neural networks, which emulate aspects of the brain's structure and function. Focus on AI and UX: - Caleb describes his current research at the Nielsen Norman Group (NNG), focusing on quantitative UX research related to AI. He notes the scarcity of established best practices for designing quality user experiences involving AI. Chapter 2: The Current State of AI Tools and Design Challenges - Summary: This chapter explores the current landscape of AI tools, highlighting both successful and unsuccessful implementations, and the challenges faced by designers in integrating AI effectively. Sections: Current AI Research at NNG: - Caleb details NNG's research on AI, including studies on AI chatbots and user expectations. He emphasizes that AI's potential to improve UX extends beyond the front-end, impacting back-end processes as well. He also stresses the importance of determining whether AI is necessary for a given application. The "Amazon Effect" and Misaligned Incentives: - Caleb discusses the tendency to blindly adopt AI features based on the practices of large tech companies, even when those features might not be appropriate for smaller companies or different contexts. He points out that many companies integrate AI due to factors other than genuine user experience improvement, such as investor pressure or marketing appeal. Examples of Good and Bad AI Implementations: - Caleb provides examples of successful AI integrations (like recommendation algorithms) that are often invisible to the user but provide significant value. He contrasts this with poorly implemented AI features, such as generic chatbots that fail to meet user expectations. Chapter 3: Agentic AI and its Limitations - Summary: This chapter dives into the concept of "agentic" AI, highlighting its promise and current limitations. Sections: Defining Agentic AI: - Caleb explains agentic AI as autonomous systems designed to automate complex tasks. He uses the example of travel planning to illustrate the current limitations of these systems, emphasizing their unreliability and high failure rate. Limitations of Agentic AI: - Caleb critiques the common use of travel planning as an example of successful agentic AI, arguing that the complexity of user preferences makes it highly unlikely that these systems will perform reliably. He encourages listeners to critically evaluate their own experiences with agentic AI tools. Chapter 4: The Role of UX Designers in AI Development - Summary: This chapter emphasizes the crucial role of UX designers in the entire AI development lifecycle, from data curation to final design. Sections: ** UX's Broader Role in AI Development:** - Caleb argues that UX designers should be involved throughout the entire AI development process, not just at the validation stage. This includes participating in data curation, algorithm design, and the overall user experience design. The Importance of Training Data: - Caleb highlights the significant impact of training data on the performance and usability of AI models. He stresses the importance of UX professionals' involvement in gathering and curating training data that accurately reflects user needs. Challenges and Opportunities: - Caleb discusses the challenges of working with pre-built AI models and the need for UX designers to collaborate effectively with engineers and product managers. He emphasizes that the probabilistic nature of AI models necessitates a more proactive role for UX designers in the development process. Chapter 5: AI Safety and its Multifaceted Nature - Summary: This chapter defines AI safety from multiple perspectives, including user safety, company safety, and the long-term implications of AI. Sections: Defining AI Safety: - Caleb distinguishes between two perspectives on AI safety. The first is the "Silicon Valley" perspective, focused on apocalyptic scenarios like AI taking over the world. The second is a more practical perspective, concerned with real-world risks to users (e.g., privacy violations, mental health issues) and the financial sustainability of AI projects. Practical Considerations for AI Safety: - Caleb advises designers to consider potential negative outcomes early in the product development process. He also highlights the importance of understanding the financial implications of using third-party AI models and implementing appropriate safeguards. Chapter 6: Advice for Designers and a Final Message - Summary: This chapter offers practical advice for designers working with AI, and concludes with a concise, memorable message for designers. Sections: Advice for Designers: - Caleb advises designers to understand the underlying technology of AI, actively participate in development discussions, and build strong working relationships with engineers and other stakeholders. Final Message: - Caleb's final message is "Maybe not," encouraging designers to question the necessity of AI in every application and explore alternative approaches to delivering value. He also suggests that smaller, incremental improvements can be just as meaningful as large-scale AI integrations. He promotes his courses on designing AI experiences and practical AI for UX professionals. Final Summary The conversation with Caleb Sponheim provided valuable insights into the evolving landscape of AI and its impact on UX design. He emphasized the importance of a nuanced understanding of AI technology, the need for designers to be involved throughout the entire development process, and the critical role of AI safety. His final message, "Maybe not," serves as a powerful reminder for designers to prioritize user needs and carefully consider the appropriateness and effectiveness of AI integrations. 47. Designing AI Experiences: What to Consider (feat. Caleb Sponheim PhD, NN/g) Interactive Q&A Sheet: Designing AI Experiences This Q&A sheet is based on an interview with Caleb Sponheim, a UX specialist, about the role of designers in the age of AI. Basic Questions: 1. How did Caleb Sponheim get into researching AI? - Caleb's background is in science, specifically computational neuroscience. His PhD focused on understanding how the brain works using mathematical approaches. He recognizes the strong connection between advancements in neuroscience and artificial intelligence, with many AI innovations stemming from our understanding of the brain's neural networks. This background naturally led him to research how AI integrates into user experiences. 2. What kind of research is Caleb currently conducting? - Caleb's research at the NN/g group focuses on understanding how users interact with AI, particularly in the context of chatbots. His studies investigate user expectations, mental models, and trust levels when interacting with AI. He emphasizes the importance of determining whether AI is truly necessary for a product and highlights the abundance of poorly designed AI implementations. 3. What are some examples of good and bad AI implementations? - A good example is recommendation algorithms in e-commerce. These systems learn user behavior and offer relevant suggestions, delivering value without explicitly advertising their AI capabilities. A bad example is the overuse of chatbots, particularly those that are unreliable and fail to meet user expectations. Many companies integrate AI without a clear business case or user benefit, leading to poor user experiences. Advanced Questions: 4. What are "agents" in the context of AI, and why are they sometimes considered a bad implementation? - AI agents are autonomous systems designed to automate tasks. The promise is that they can independently interact with other systems to complete complex tasks, like arranging travel. However, Caleb argues that this is often a bad implementation because the complexity of user preferences and the unreliability of current AI agents make them fail frequently. The high failure rate and the low probability of success make the current application of agents a poor user experience. 5. How can UX designers improve the safety of AI products? - AI safety has two key aspects: 1. User safety: This involves considering privacy, mental health, and the potential for negative impacts on users. For example, the risks of harmful interactions with AI chatbots. 2. Business safety: This includes managing costs associated with AI implementation and avoiding financial risks, such as unexpectedly high bills from third-party AI services. Designers can contribute to safety by being involved in the early stages of AI development, considering potential negative outcomes, and carefully selecting and implementing AI tools. 6. What advice does Caleb have for designers wanting to break into AI design? - Caleb advises designers to understand the underlying AI technology, including how different models work and the importance of training data. It's crucial to be involved in the decision-making process, which often requires building relationships with engineers and developers. This involves learning about AI technology to understand its potential and limitations in improving user experience. Real-World Examples: Good AI: Netflix's recommendation system, which learns user viewing habits to suggest relevant shows and movies. Bad AI: A chatbot on a company website that provides inaccurate or unhelpful responses, frustrating users. Additional Thought-Provoking Questions: How can we better measure the success of AI implementations beyond immediate conversions? What ethical considerations should guide the design and development of AI systems? How can we ensure that AI is used responsibly and ethically, avoiding biases and potential harms? What are the long-term implications of increasing reliance on AI-powered systems? How can we design AI systems that foster trust and transparency with users? 47. Designing AI Experiences: What to Consider (feat. Caleb Sponheim PhD, NN/g) Exam Focused Notes: Designing AI Experiences Introduction - : The increasing integration of AI in products necessitates designers' understanding of AI's role in UX. This podcast features Caleb Sponheim, a UX specialist researching AI trends and their impact on design. Caleb's Background and AI Research - : Caleb's background in computational neuroscience ( applying mathematical approaches to understanding the brain ) informs his AI research. Many AI advancements stem from neuroscience, emulating brain functions to create artificial neural networks. His current research focuses on: Quantitative UX research in AI. User expectations and trust in AI chatbots. The role of AI in both front-end and back-end user experiences. Determining when AI is appropriate for a product. The Need for Critical Evaluation of AI Tools - : Not all AI tools are beneficial; many are poorly implemented. The "Amazon effect" highlights the danger of blindly adopting features from large tech companies without considering context and user needs. Good vs. Bad AI Implementations - : Good: Recommendation algorithms seamlessly integrate into e-commerce, enhancing user experience without overt AI branding. They deliver value invisibly, improving conversions and user loyalty. Bad: Agentic AI ( autonomous AI systems designed to complete tasks ) often fails to meet user expectations due to complex real-world preferences. Examples like travel planning highlight the unreliability of current agentic AI. OpenAI's Operator is cited as a poorly performing example. The Role of UX Designers in AI Development - : UX designers should be involved throughout the AI development process, not just for final validation. Key areas of involvement include: Curating training data: Ensuring the data used to train AI models reflects user needs. Early-stage design: Influencing the design of algorithms and models to ensure usability. Addressing limitations: Working within the constraints of the underlying technology to create the best possible user experience. The example of a poorly implemented chatbot on a car dealership website illustrates the importance of this. AI Safety - : AI safety encompasses multiple aspects: End-user safety: Protecting users from potential harm, including privacy violations and mental health risks. Examples include the impact of AI chatbots on teenagers' mental health and the misuse of personal data. Company safety: Protecting the company from financial risks associated with AI implementation, such as unexpectedly high costs. Advice for Designers Entering AI Design - : Understand the technology: Learn how AI models work and their potential impact on UX. Be involved in decision-making: Participate in the development process from the beginning. Build relationships: Collaborate effectively with engineers and other stakeholders. Key Message for Designers - : Don't force AI into every product. Prioritize user needs and explore alternative solutions. Consider smaller, targeted AI implementations instead of large-scale integrations. Resources: - : Caleb's course "Designing AI Experiences" and "Practical AI for UX Professionals" are available on the NNGroup website. Likely Exam Questions: Discuss the challenges and opportunities for UX designers in the age of AI. Compare and contrast good and bad examples of AI implementation in product design. Explain the importance of UX designers' involvement in all stages of AI development. Define AI safety and discuss its various dimensions. What advice would you give to a UX designer looking to transition into AI design? Final Recap: The integration of AI into products presents both challenges and opportunities for UX designers. Effective AI implementation requires a critical evaluation of user needs, careful consideration of safety implications (for both users and the company), and proactive involvement in the entire development process. Blindly adopting AI features without understanding their implications can lead to poor user experiences and potential risks. A human-centered approach remains crucial, even in the context of AI.