The process of enabling users to take control of their interactions with a product or system, enhancing their confidence and satisfaction. Crucial for designing systems that provide users with the tools and information they need to make informed decisions.
A cognitive bias where people overestimate the probability of success for difficult tasks and underestimate it for easy tasks. Useful for designers to understand user confidence and design
The belief in one's ability to succeed in specific situations or accomplish a task, influencing motivation and behavior. Crucial for designing systems that enhance user confidence and encourage goal achievement.
The ability of users to influence the behavior and outcomes of a system or product, allowing them to interact with it according to their preferences. Essential for creating user-friendly interfaces that allow for flexibility and customization.
Principle of Least Astonishment (POLA) is a design guideline stating that interfaces should behave in a way that users expect to avoid confusion. Crucial for enhancing user experience and reducing the learning curve in digital products.
User Acceptance Testing (UAT) is the final phase of the software testing process where actual users test the software to ensure it meets their requirements. Crucial for validating that the software functions correctly in real-world scenarios before its release.
A cognitive bias where a person's subjective confidence in their judgments is greater than their objective accuracy. Crucial for understanding user decision-making and designing systems that account for overconfidence.
Explainable AI (XAI) are AI systems that provide clear and understandable explanations for their decisions and actions. This transparency is crucial for building trust and confidence in AI applications across various domains.
A state of overthinking and indecision that prevents making a choice, often due to too many options or uncertainty. Important for designing interfaces that simplify decision-making processes for users.
The phenomenon where having too many options leads to decision-making paralysis and decreased satisfaction. Crucial for understanding and designing user interfaces that avoid overwhelming users with choices.
A psychological phenomenon where people follow the actions of others in an attempt to reflect correct behavior for a given situation. Essential for designing interfaces and experiences that leverage social influence to guide user behavior and increase trust and engagement.
A cognitive bias where individuals overestimate their own abilities, qualities, or performance relative to others. Important for understanding user self-perception and designing systems that account for inflated self-assessments.
The tendency for people to believe that others are telling the truth, leading to a general assumption of honesty in communication. Important for understanding communication dynamics and designing systems that account for this bias.
A cognitive bias where people's decisions are influenced by how information is presented rather than just the information itself. Crucial for designers to minimize bias in how information is presented to users.
The quality of being uniform and coherent across different elements and touchpoints in design. Crucial for creating predictable and reliable user experiences.
A cognitive bias where decision-making is affected by the lack of information or uncertainty. Important for understanding and mitigating user decision-making biases due to uncertainty or lack of information.
The degree to which the operations and decisions of an AI system are understandable and explainable to users. Crucial for building trust and ensuring ethical AI use.
A tendency to avoid making decisions that might lead to regret, influencing risk-taking and decision-making behaviors. Crucial for understanding decision-making processes and designing systems that minimize regret.
The practice of developing artificial intelligence systems that are fair, transparent, and respect user privacy and rights. Crucial for ensuring that AI technologies are developed responsibly and ethically.
A type of testing conducted to determine if the requirements of a specification are met, often the final step before delivery to the customer. Important for ensuring that a product meets the defined criteria and is ready for release.
A symmetrical, bell-shaped distribution of data where most observations cluster around the mean. Fundamental in statistics and crucial for many analytical techniques used in digital product design and data-driven decision making.
In AI, the generation of incorrect or nonsensical information by a model, particularly in natural language processing. Important for understanding and mitigating errors in AI systems.
A cognitive bias where individuals underestimate their own abilities and performance relative to others, believing they are worse than average. Important for understanding self-perception biases among designers and designing systems that support accurate self-assessment.
Impact, Confidence, and Ease of implementation (ICE) is a prioritization framework used in product management to evaluate features. Essential for making informed and strategic decisions about feature development and prioritization.
A prioritization framework used in product management to evaluate features based on Reach, Impact, Confidence, and Effort. Crucial for making informed decisions about which product features to prioritize and develop.
Reasons to Believe (RTB) is a marketing concept that refers to the evidence or arguments that support a product's claims and persuade consumers of its benefits. Essential for building trust and credibility with customers.
ModelOps (Model Operations) is a set of practices for deploying, monitoring, and maintaining machine learning models in production environments. Crucial for ensuring the reliability, scalability, and performance of AI systems throughout their lifecycle, bridging the gap between model development and operational implementation.