Review Algorithmic Accountability

Algorithmic accountability is the principle and practice that organizations remain answerable for the design, use, effects, and remediation of decisions made or supported by algorithmic systems.

Responsibility does not disappear because software recommended or made a decision. People and institutions must be able to explain authority, examine evidence, correct harm, and change the system.

Automated systems can spread decisions at scale while making ownership harder to see. Accountability preserves a path for review, challenge, correction, and consequences.

A company using an automated hiring screen maintains a named owner, audit evidence, applicant appeal process, and authority to suspend the system.

Accountability is not satisfied by saying a vendor or model made the decision. Deploying organizations retain responsibilities that depend on law and context.

Automation can move the decision, not erase accountability.

* [[human-ownership|Human Ownership]] * [[ownership-gap|Ownership Gap]] * [[automated-decision-making|Automated Decision-Making]] * [[interpretability-explainability|Interpretability and Explainability]] * [[ai-governance|AI Governance]]

* [NIST AI RMF — Accountable and Transparent](https://airc.nist.gov/airmf-resources/airmf/3-sec-characteristics/) — official guidance connecting accountability with transparency across AI design, data, training, deployment, and decision-making. * [NIST SP 1270, *Towards a Standard for Identifying and Managing Bias in Artificial Intelligence*](https://doi.org/10.6028/NIST.SP.1270) — official guidance on governance, assurance, documentation, and practices for identifying and managing harmful bias.

* Use [[human-ownership|Human Ownership]] to make accountability operational.