Algorithmic accountability

The principle that organizations and individuals responsible for creating and deploying algorithmic systems should be held responsible for the outcomes and impacts of those systems. In accessibility contexts, algorithmic accountability addresses who is responsible when AI-powered assistive technologies make errors that harm users or those observed by such systems—for example, when facial recognition misidentifies someone or when emotion detection provides inaccurate information that leads to awkward social interactions. Accountability frameworks typically include requirements for transparency about how algorithms work, mechanisms for users to provide feedback and seek redress, and organizational processes for auditing systems for bias and errors.