Glossary Key Terms and Concepts in AI Ethics

Artificial Intelligence (AI): A field of computer science focused on creating systems capable of performing tasks that require human intelligence, such as recognizing speech, making decisions, and translating languages.

Autonomy: In AI ethics, refers to the capacity of AI systems to operate independently, making decisions without human intervention. It also relates to human autonomy, emphasizing the importance of ensuring that AI enhances human decision-making rather than undermines it.

Bias: Prejudice or unfairness in AI systems, often arising from biased data sets or algorithms, which can lead to discriminatory outcomes against certain groups or individuals.

Data Privacy: Concerns related to the collection, processing, and storage of personal information by AI systems, emphasizing the need to protect individuals’ data from unauthorized access and use.

Ethical AI: The practice of designing, developing, and deploying AI technologies in a manner that adheres to ethical principles and values, such as fairness, accountability, and transparency.

Explainability (or Transparency): The ability of an AI system to provide understandable explanations for its decisions, actions, or recommendations, making it possible for users to interpret and trust AI outputs.

Fairness: The principle that AI systems should make decisions without bias or discrimination, ensuring equitable treatment and outcomes for all individuals and groups.

Governance: The frameworks, policies, and regulations that guide the development, deployment, and use of AI technologies, ensuring they adhere to ethical standards and societal values.

Machine Learning: A subset of AI that involves training algorithms on data sets to perform tasks by identifying patterns and making decisions based on the data they have processed.

Neural Networks: Computing systems inspired by the biological neural networks of human brains, used in machine learning to model complex patterns and perform tasks like image and speech recognition.

Non-maleficence: A principle in AI ethics emphasizing that AI systems should not harm humans or the environment, either intentionally or unintentionally.

Privacy: The right of individuals to control the collection, use, and dissemination of their personal information. In the context of AI, it involves ensuring that personal data is used ethically and protected from misuse.

Regulatory Frameworks: Legal and institutional structures established to oversee the development and application of AI technologies, ensuring compliance with ethical norms and societal standards.

Sentience: The capacity to have subjective experiences, feelings, or sensations. In the context of AI, it refers to the hypothetical ability of AI systems to experience states akin to human consciousness or emotions.

Transparency: The principle that AI systems should operate in a manner that is open and understandable to users and stakeholders, enabling insight into AI processes and decision-making.

Trust: Confidence in the reliability, integrity, and fairness of AI systems, essential for their acceptance and integration into society.