Lowmanipulation

Lowmanipulation is a term used in discussions of interaction design, ethics, and behavioral science to describe practices that minimize manipulative influence over people, systems, or outcomes. The concept emphasizes autonomy, transparency and user agency, with the goal of reducing coercive or deceptive techniques while preserving effective communication and decision support.

Contexts include user experience design, artificial intelligence and algorithmic decision-making, marketing ethics, and organizational behavior. In UX, lowmanipulation involves clear privacy practices, straightforward pricing, and interfaces that avoid dark patterns. In AI and automation, it advocates explainable models and user control over automated decisions. In marketing and politics, it aligns with ethical standards that avoid manipulation and deceptive persuasion.

Key principles include informed consent, meaningful choice, predictable outcomes, and default settings that favor the user’s welfare. Methods often rely on participatory design, transparency about data use, opt-in rather than opt-out data collection, and ongoing auditing for bias and manipulation potential.

Critiques note that what counts as manipulation can be context-dependent, and that strong non-manipulation norms can limit the effectiveness of beneficial persuasive health communications or safety prompts. Proponents argue that trust, long-term engagement, and safety are enhanced by reducing manipulative practices. The term remains a subject of debate and is not uniformly standardized across disciplines.

See also: manipulation, ethics in technology, dark patterns, user empowerment.