It refers to the dual functional capacities of algorithms within AI systems or machine-learning frameworks, as understood within a procedural and legal context.

Activities encompass the routine, foundational, and general-purpose tasks that algorithms perform, such as data processing, pattern recognition, or automated responses, which are essential for the day-to-day functioning of digital systems across diverse applications.

Operations, in contrast, denote specialised, context-driven, or technology-specific tasks that are tailored to particular domains, objectives, or technical environments, such as predictive modelling for financial markets, real-time decision-making in autonomous systems, or adaptive learning in personalised healthcare solutions, for instance.

This distinction highlights the layered complexity of algorithmic behaviour, recognising that algorithms operate at varying levels of abstraction and intent, necessitating nuanced governance approaches in a globalised digital ecosystem.

This idea was originally proposed in Deciphering Artificial Intelligence Hype and its Legal-Economic Risks, VLiGTA-TR-001 (2022).

Original Definition in line with technical report "Deciphering Artificial Intelligence Hype and its Legal-Economic Risks, VLiGTA-TR-001 (2022)":

It means the algorithms of any AI system or machine-learning-based system are capable to perform two kinds of tasks, in a procedural sense of law, i.e., performing normal and ordinary tasks - which could be referred to as 'activities' and methodical and context-specific or technology-specific tasks, called 'operations'.