Accountability
The principle that there must be clear responsibility and liability for AI system decisions and their outcomes. Someone must be answerable when AI causes harm.
Why It Matters
Without clear accountability, AI failures lead to finger-pointing. Accountability frameworks ensure that someone is responsible for oversight, maintenance, and consequences.
Example
When an autonomous vehicle causes an accident, accountability frameworks determine whether the manufacturer, the software developer, or the operator is responsible.
Think of it like...
Like a chain of command in an organization — when something goes wrong, there must be clear responsibility, not everyone pointing at someone else.
Related Terms
AI Governance
The frameworks, policies, processes, and organizational structures that guide the responsible development, deployment, and monitoring of AI systems within organizations and across society.
Responsible AI
An approach to developing and deploying AI that prioritizes ethical considerations, fairness, transparency, accountability, and societal benefit throughout the entire AI lifecycle.
AI Ethics
The study of moral principles and values that should guide the development and deployment of AI systems. It addresses questions of fairness, accountability, transparency, privacy, and the societal impact of AI.
Transparency
The principle that AI systems should operate in a way that allows stakeholders to understand how they work, what data they use, and how decisions are made.