Ethical AI Design
The practice of developing artificial intelligence systems that are fair, transparent, and respect user privacy and rights.
The practice of developing artificial intelligence systems that are fair, transparent, and respect user privacy and rights.
A symmetrical, bell-shaped distribution of data where most observations cluster around the mean.
A type of testing conducted to determine if the requirements of a specification are met, often the final step before delivery to the customer.
A cognitive bias where individuals underestimate their own abilities and performance relative to others, believing they are worse than average.
In AI, the generation of incorrect or nonsensical information by a model, particularly in natural language processing.
Impact, Confidence, and Ease of implementation (ICE) is a prioritization framework used in product management to evaluate features.
A prioritization framework used in product management to evaluate features based on Reach, Impact, Confidence, and Effort.
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.
ModelOps (Model Operations) is a set of practices for deploying, monitoring, and maintaining machine learning models in production environments.