Responsible use
Deployment of algorithms that meets legal, public-value and technical standards.
How the laws define this
Both instruments establish that using AI responsibly requires transparency and adherence to legal standards, such as privacy protections, alongside proactive controls to prevent harm. However, they diverge significantly in scope and operational domain. The Implementation Framework for Responsible Use of Algorithms approaches the concept through a broad, public-sector lens, structuring responsible use around three formal pillars—legal compliance, alignment with public values like non-discrimination and accountability, and technical robustness such as security and resilience—to govern decision-making across the entire system lifecycle. Conversely, the Guidelines for the Responsible Use of AI in Advertising and Marketing anchors its definition in consumer-facing contexts, focusing specifically on deploying AI with consumer protection safeguards, privacy preservation, and meaningful human oversight to prevent consumer manipulation. Thus, while the former frames responsible use as a systemic governance framework for public-sector algorithms, the latter concentrates on preventing direct harm during targeted marketing deployments.
Synthesised from the 2 statutory definitions below. Each one is quoted in full, with its source.
Definitions (2)
A three‑pillar concept defined by the framework: (1) legal compliance (e.g., GDPR and sectoral law), (2) alignment with public values (transparency, accountability, non‑discrimination), and (3) technical robustness (security, reliability and resilience). It frames decision‑making and required measures across the lifecycle to ensure public‑sector algorithmic systems are used responsibly.
Deploying AI technologies with appropriate consumer protection safeguards, privacy preservation, transparency mechanisms, and meaningful human oversight to prevent manipulation or harm to consumers.
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