GenAI Fundamentals for UX Designers + Researchers By Joe Natoli
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GenAI Fundamentals for UX Designers + Researchers · Program
About this program
GenAI Fundamentals for UX Designers + Researchers
Online Course
Learning format
Figma
Subcategory
English
Course language
$49.99
Price
Price may change · updated within 1–2 weeks
Official program page
What you'll learn in GenAI Fundamentals for UX Designers + Researchers
- Learn core principles of responsible GenAI design that ensure our products serve all users equally + equitably.
- Success factors unique to Machine Learning (ML) products that UX + Product Designers and their teams should adopt
- Guidance on research to determine what kinds of problems are best solved by AI, and where human control should remain central.
- Determining when AI features are appropriate for users — and when they aren't
- Identifying when Automation (AI does the task for users) or Augmentation (help them do it better) is more appropriate.
- Designing the reward function and appropriately considering the balance between false positives and false negatives.
- Essential factors to consider when evaluating the reward function.
- How to weigh necessary, unavoidable tradeoffs between precision and recall, which is key to shaping an AI user experience.
- Designing for fairness and inclusion, from objectives to datasets to guidance on bias testing.
- Designing for generative variability: how do we present multiple, varied outputs to users — and how do we guide them in selecting the best one?
- Designing for multiple outputs: how do we help users filter and highlight differences between outputs?
- Designing for imperfections: how do we empower users to manage + mitigate imperfections and designing with contextual sensitivity?
- Designing for confidence: how do we design confidence scoring to properly evaluate output quality and increase user trust?
- Rules and examples for applying confidence scores.
- Designing for co-creation: how to we design co-creation processes where both the user and the AI can make adjustments?
- Designing for generic controls: using "temperature" to control the number of outputs and the degree of variability in those outputs.
- Designing for domain-specific controls, such as encoder-decoder models, semantic sliders and prompt engineering.
- Designing for prompt engineering: enabling and guiding users to effectively use multiple types of conversational prompts.
- Designing for exploration: incorporating flexibility, feedback, transparency and error handling/expectation management.
- Designing for choice, feedback, transparency + safety: centering users as active, empowered participants in the creation process.
- Designing for mental models: orienting users to generative variability, teaching effective use and teaching the AI about the user.
- Designing for explanation, understanding + trust: providing clear rationales for outputs, using friction to curb over-reliance and showing imperfections.
- Designing against harm: understanding the critical ways irresponsible AI design can harm people.
- Designing against hazardous outputs: discrimination, exclusion, toxicity, misinformation, deep fakes, IP theft and more.
- Mitigating harm with a Value-Sensitive Design (VSD) process, integrated with an Agile or Lean development process.
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