Algorithmic accountability refers to the obligation of organizations that develop or deploy AI systems to take responsibility for the outcomes those systems produce — and to be able to demonstrate that responsibility to regulators, affected individuals, and the public.

Algorithmic accountability refers to the obligation of organizations that develop or deploy AI systems to take responsibility for the outcomes those systems produce — and to be able to demonstrate that responsibility to regulators, affected individuals, and the public.

Fairness testing evaluates whether an AI system produces systematically different outcomes for different groups of people in ways that are unjustified, harmful, or legally impermissible.

AI explainability is the degree to which an AI system's outputs can be understood by the people responsible for them and by those affected by them. In practice, this means being able to account for why a system produced a given output, which inputs influenced the result, and how confident the system was in its decision.

Post-deployment monitoring is the continuous tracking of an AI system's behavior after it has been deployed into production. AI systems are not static: they encounter new data distributions, edge cases, and usage patterns that pre-deployment testing could not anticipate. Without systematic monitoring, organizations discover drift, emerging bias, or degrading accuracy only when the consequences are already visible. 

AI risk assessment is the process of identifying, measuring, and prioritizing the risks associated with an AI system before and during its deployment in production.

The EU AI Act is the European Union’s framework for regulating artificial intelligence systems, with obligations that vary based on the risk level of the AI system in question. High-risk AI systems — those used in employment, education, healthcare, law enforcement, and critical infrastructure — are subject to the most stringent requirements, including mandatory conformity assessments, transparency obligations, and ongoing monitoring.

Socio-technical auditing evaluates an AI system as a complete system, not just the AI models that are used. It assesses data flows, model behavior, business rules, user interface, human oversight, organizational processes, and the real-world outcomes the system produces. 

AI governance refers to the frameworks, policies, processes, and oversight structures that determine how AI systems are developed, deployed, and monitored within an organization. It encompasses technical controls, human accountability, regulatory compliance, and the organizational structures that ensure AI systems behave as intended over time.

Algorithmic bias occurs when an AI system produces systematically different outcomes for different groups of people based on protected characteristics like race, gender, age, or socioeconomic status. It can originate in training data, in feature engineering that introduces proxy variables, or in threshold decisions that were never tested across the full population the system serves.