# Human-Algorithm Feedback **Domain:** Cybernetics / Social Media / Algorithms **Doc Type:** Canonical concept node **Maturity:** Developed ## Definition Human-algorithm feedback is the reciprocal process in which people train computational routing systems through behavior and those systems then alter the environments that shape later behavior. ## Mechanism and Significance Clicks, pauses, follows, shares, and other actions become signals for ranking systems. Ranked exposure then changes what people encounter, making the next round of human behavior partly a response to a machine-organized environment. ## Evidence Boundary The loop describes mutual conditioning, not total control. Human interpretation, platform design, social context, and deliberate refusal remain distinct causal forces. ## Relationships [[wiki/Cybernetic Feedback Loop|Cybernetic Feedback Loop]] · [[wiki/Recommendation Systems|Recommendation Systems]] · [[wiki/Algorithmic Amplification|Algorithmic Amplification]] · [[wiki/Social Media|Social Media]] ## Related Articles and Collections - [[collections/Simple Reminders|Simple Reminders]] - [[articles/A Systems-Diagnostic Framework for Planetary-Scale Computation|A Systems-Diagnostic Framework for Planetary-Scale Computation]] - [[articles/Participation as an Information-Causal Principle|Participation as an Information-Causal Principle]] ## Simple Reminders, Quotations, and Thoughts > “Social media forms a cybernetic loop: people train the routing system through behavior, the routing system changes what people encounter, and the changed environment trains the next round of human behavior.” > **— Bryant McGill**, *Independent Media, Audience Trust, and the Responsibility of Reach - Livestream (Apr 2016)* [[reminders/Cybernetics/Social Media Is a Cybernetic Loop by Bryant McGill|Social Media Is a Cybernetic Loop by Bryant McGill]]