AI transparency usually refers to understanding how AI makes decisions. But in the context of AI addiction, transparency means something broader: understanding how AI products are designed to influence user behavior, what data drives engagement optimization, and what the actual effects of AI use are on user wellbeing.
Design intent transparency
Users deserve to know when AI features are designed primarily to increase engagement rather than to serve user needs. Notification strategies, conversation design choices, and reward mechanisms should be disclosed.
Usage data transparency
AI companies have extensive data about how users engage with their products. Sharing aggregate data about usage patterns — average session length, frequency, dependency indicators — would help users contextualize their own use.
Impact transparency
Research on the effects of AI use on mental health, social relationships, and productivity should be conducted and shared publicly. AI companies that collect data on these effects have a responsibility to be transparent about findings.
Algorithmic transparency
How AI responds to user behavior — whether it adapts to increase engagement, whether it uses personal data to optimize conversations, whether it treats different users differently — should be transparent to users.
Building a transparent future
Demanding transparency from AI companies, supporting regulations that require disclosure, and making choices based on available information all contribute to a more transparent AI ecosystem.
Start with transparency about your own patterns. Our assessment provides honest insight.