The practice of developing AI systems that adhere to ethical standards, ensuring fairness and transparency.

Techniques used to reduce bias in AI models and datasets, promoting fairness in decision-making.

Quantitative measures used to evaluate the fairness of AI models, ensuring equitable outcomes.

The degree to which AI processes and decisions are clear and understandable to stakeholders.

The process through which AI systems make choices based on data inputs and algorithms.

A subset of AI that enables systems to learn from data and improve over time without explicit programming.

Techniques used to clean and prepare data for analysis, ensuring quality and relevance.

A set of rules or calculations used by AI to process data and make decisions.

Guidelines that help in assessing the ethical implications of AI technologies.

Involving relevant parties in discussions about AI development and its ethical implications.

AI models that produce skewed results due to biased training data or design.

A technique used to assess the generalizability of a model by partitioning data into subsets.

A modeling error that occurs when a model learns noise instead of the underlying pattern in the data.

A modeling issue where a model is too simple to capture the underlying trends in the data.

The graphical representation of data to help stakeholders understand trends and patterns.

Adhering to laws and guidelines governing AI development and deployment.

The effect of AI technologies on society, including ethical and fairness considerations.

An in-depth analysis of a particular instance of AI application, focusing on ethical implications.

A process where colleagues evaluate each other's work to ensure quality and relevance.

Documents designed to help non-technical stakeholders understand AI processes.

An evaluation process to assess the fairness of AI systems and their impact on different groups.

AI systems designed to provide clear explanations for their decisions and outputs.

Individual beliefs about what is right or wrong in the context of AI development.

A mathematical representation of a process that learns from data to make predictions or decisions.

Records that outline the decision-making processes and methodologies of AI systems.

Data that reflects societal biases, leading to unfair outcomes when used in AI models.