Key insights

• Human-Centric AI Experience

  • 🤖 AI automates routine tasks, but some jobs require human involvement.
  • 🔄 Focus on augmented AI and human-centric AI experience rather than full automation.
  • 🤲 Building trust and co-creating with users for a human-centered AI experience.

• Building Trust for System Optimization

  • ⚙️ Building trust between user and AI leads to system optimization.
  • 🗂️ Trust calibration in enterprise applications is crucial and involves balancing user trust and product trust.
  • 🤝 Co-creation balances control and automation in AI products.

• Trust in Business and AI

  • 🛡️ Trust impacts adoption, usage, and decision-making, necessitating balanced confidence and skepticism.
  • ⚖️ Core components of trust include understanding, utility, and impact, calibrated based on user personas and external factors.

• User Understanding and Trust

  • 🔍 Explaining limitations, accuracy, data sources, model workings, and actions on outputs crucial for user satisfaction.
  • 🤝 Building trust in AI model is essential despite some system aspects remaining obscure.

• Building Mental Models

  • 🧠 Importance of addressing wrong mental models in AI and building mental models by explaining and educating users.
  • 📊 Starting with simple data visualizations and gradually increasing complexity is essential.
  • 🗣️ Challenges of explaining AI due to model complexity, favoring partial explanation.

• AI Interface and Education

  • 🔄 AI interface is freestyle, allows natural language interactions, and hides system capabilities in the model.
  • 👩‍🏫 Educating users about AI and avoiding misconceptions is crucial in customer-engineer interactions.

• Human AI Partnership

  • 📈 Developed analytical solution to monitor market trends and provide actionable recommendations.
  • 💡 Focused on shaping the human AI partnership and building trust in AI by addressing concerns and transparency requirements.

• UX of AI Products

  • 🖥️ Discusses the UX of AI products and the speaker's journey to AI from Chinese studies to a PhD in AI.
  • 🔬 Case study in B2B AI development focused on customization, product functionality after launch, and high transparency and accuracy requirements.

Q&A

• In what way can AI be used in the workforce?

  AI is used for automating routine tasks such as customer service and fraud detection, but some jobs require human touch. The focus in the field is primarily on augmented AI rather than full automation, emphasizing the need for trust, responsibility, and co-creation with users.

• How can trust between the user and AI be crucial for system optimization?

  Building trust between the user and AI is crucial for system optimization, and calibrating trust is important, balancing user trust in the product and product trust in user usage. Co-creation involves balancing control and automation in AI products.

• Why is explaining the limitations, accuracy, sources, and workings of an AI system crucial?

  Explaining the system's limitations, accuracy, data sources, and model workings is essential for user understanding and satisfaction. It helps build trust in the model, despite some parts remaining obscure, and highlights the value of the system. Trust is vital in business and AI, impacting adoption, usage, and decision-making.

• What is the importance of understanding and addressing wrong mental models in AI?

  Understanding and addressing wrong mental models in AI is crucial. Building mental models by explaining and educating users is emphasized, starting with simple data visualizations and gradually increasing complexity due to the challenge of explaining the complexity of AI models.

• How is the AI interface different from graphical interfaces?

  The AI interface is freestyle, allowing natural language interactions. The system's capabilities are concealed in the model, necessitating user education to avoid misconceptions in customer-engineer interactions.

• What did the company develop for the customer?

  The company developed an analytical solution to monitor market trends, offering actionable recommendations. Additionally, they concentrated on shaping the human-AI partnership, addressing people's concerns, and building trust in AI.

• What is the talk about?

  The talk focuses on the UX of AI products, the speaker's journey to AI, and a case study in B2B AI development. It includes insights on customization, the importance of product functionality after launch, and high standards for transparency and accuracy. The customer, a global premium travel company, emphasized its history, past achievements, internal politics, and the need for market situational awareness.

• 00:00 The talk is about the UX of AI products, the speaker's journey to AI, and a case study in B2B AI development. The project involved customization, the importance of the product working after launch, and high requirements for transparency and accuracy. The customer was a global premium travel company with a focus on their own history, past achievements, internal politics, and the need for market situational awareness.
• 05:45 The company developed an analytical solution to monitor market trends and provide actionable recommendations. They also focused on shaping the human AI partnership by addressing people's concerns and building trust in AI.
• 11:45 The interface for AI is different from graphical interfaces, as AI interfaces are freestyle and the capabilities are hidden in the model. It's important to educate users about AI and avoid misconceptions in customer-engineer interactions.
• 17:34 The video discusses the importance of understanding and addressing wrong mental models in AI and how to build mental models by explaining and educating users. It also emphasizes the need for starting with simple data visualizations and gradually increasing complexity. Explaining AI to users is challenging due to the complexity of the models, so a partial explanation is preferred.
• 23:35 Explaining the limitations, accuracy, data sources, model workings, and actions on outputs of an AI system is crucial for user understanding and satisfaction. Building trust in the model is key, even though some parts of the system will always remain obscure.
• 29:20 Trust is crucial in business and AI. It impacts adoption, usage, and decision-making. Calibrated trust is important to balance confidence and skepticism. Core components of trust include understanding, utility, and impact. Trust calibration depends on user personas and other external factors.
• 35:17 Building trust between the user and AI is crucial for system optimization and calibrating trust in enterprise applications. Trust is a two-way street, balancing user trust in the product and product trust in user usage. Co-creation involves balancing control and automation in AI products.
• 40:47 AI can be used for automating routine tasks, but some jobs require human touch. Most work in the field is focused on augmented AI rather than full automation. Building AI requires trust, responsibility, and co-creation with users.