A principle stating that a system should be liberal in what it accepts and conservative in what it sends, meaning it should handle user input flexibly while providing clear, consistent output, similar to the principle of fault tolerance. Essential for designing robust and user-friendly interfaces that accommodate a wide range of user inputs and behaviors while maintaining reliability and clarity in responses.
The quality of being uniform and coherent across different elements and touchpoints in design. Crucial for creating predictable and reliable user experiences.
A design principle that ensures a system continues to function at a reduced level rather than completely failing when some part of it goes wrong. Crucial for enhancing system reliability and user experience in adverse conditions.
Voice User Interface (VUI) is a system that allows users to interact with a device or software using voice commands. Essential for creating hands-free, intuitive user experiences.
Human in the Loop (HITL) integrates human judgment into the decision-making process of AI systems. Crucial for ensuring AI reliability and alignment with human values.
Redundant, outdated, or unnecessary code or design elements that accumulate over time in a system. Important for identifying and removing to maintain clean, efficient, and maintainable systems and interfaces.
A cognitive bias where people place too much importance on one aspect of an event, causing errors in judgment. Important for understanding decision-making and designing interfaces that provide balanced information.
Perceivable, Operable, Understandable, and Robust (POUR) are the four main principles of web accessibility. These principles are essential for creating inclusive digital experiences that can be accessed and used by people with a wide range of abilities and disabilities.
Design strategies aimed at preventing user errors before they occur. Crucial for enhancing usability and ensuring a smooth user experience.
The phenomenon where people have a reduced ability to recall the last items in a list when additional, unrelated information is added at the end. Crucial for designing information presentation to optimize memory retention.
A dark pattern where additional costs are only revealed at the last step of the checkout process. It's essential to avoid this tactic and promote transparent pricing to build user trust.
A cognitive bias where people ignore the relevance of sample size in making judgments, often leading to erroneous conclusions. Crucial for designers to account for appropriate sample sizes in research and analysis.
A cognitive bias where individuals' expectations influence their perceptions and judgments. Relevant for understanding how expectations skew perceptions and decisions among users.
A logical fallacy that occurs when one assumes that what is true for a part is also true for the whole. Important for avoiding incorrect assumptions in design and decision-making.
A schedule of reinforcement where a desired behavior is reinforced every time it occurs, promoting quick learning and behavior maintenance. Important for designing systems that encourage consistent user behavior.
The persistence of misinformation in memory and influence on reasoning, even after it has been corrected. Crucial for understanding and mitigating the impact of misinformation in design and communication.
A cognitive bias where the total probability assigned to a set of events is less than the sum of the probabilities assigned to each event individually. Important for understanding how users estimate probabilities and make decisions under uncertainty.
Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that uses human input to guide the training of AI models. Essential for improving the alignment and performance of AI systems in real-world applications.