Algorithmic accountability is the principle that any organization deploying an automated decision-making system must be able to explain, test, and answer for its outcomes, with mechanisms in place to verify this before harm occurs rather than only after.
Automated systems are frequently treated as neutral because their logic sits inside proprietary code, unreadable to the people they affect. That opacity removes the ordinary friction that lets people contest an unfair decision. Accountability restores that friction by requiring the system, and the organization behind it, to be answerable in ways a black box cannot be by default.
The A+ Alliance approach: The A+ Declaration calls for public and private sector uptake of Algorithmic Impact Assessments, a self-assessment framework built to respect the public’s right to know how a system that affects their lives actually functions. It further calls for rigorous testing across the full lifecycle of an AI system, covering training data, test data, models, and APIs, through pre-release trials, independent auditing, certification, and ongoing monitoring.
Related projects
A+ Declaration, AI & Equality Initiative, Our Ecosystem
Related resources
Feminist AI Network Papers, A+ Advisory Board, Global Directory
