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Accountability algoritmica
Algorithmic accountability is the principle that organizations developing, deploying or using AI systems must be able to answer for the decisions and effects those systems produce, maintaining traceability, documentation and clear responsibility throughout the system's lifecycle. It concerns not just formal compliance with a rule but the substantive ability to explain, justify and correct an algorithm's behavior.
It is realized through concrete practices: assigning precise roles and responsibilities within the organization (who decides to put a model into production, who monitors it, who answers in case of harm), keeping logs and documentation of design decisions, independent verification mechanisms, and channels through which affected people can contest an automated decision. It is a principle referenced in both the EU AI Act and the NIST AI RMF, though with different implementation tools.
For companies, building algorithmic accountability means investing in internal governance: review committees for high-impact systems, policies on who can approve a model's deployment, internal audit procedures and reporting channels for users and employees. It is an increasingly requested requirement from investors and business partners as a signal of organizational maturity.
The concept emerged from academic debate on algorithmic fairness in the 2010s, when researchers and legal scholars began documenting cases of discriminatory or opaque automated decisions in areas such as credit, hiring and criminal justice, highlighting the absence of identifiable accountable parties when an algorithm caused harm.
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