Algorithmic bias
A useful lens for examining how this topic shapes meaning, action, and public life.
Algorithms, language & human judgment
Artificial Intelligence
Artificial intelligence increasingly participates in writing, recommending, ranking, translating, and interpreting communication.
Communication overview
Why artificial intelligence matters.
AI systems do not communicate from nowhere. They are trained on human-produced data, shaped by design choices, and deployed within institutions that define what counts as useful, accurate, or safe. Large language models can reproduce patterns in language while also carrying forward the limitations and inequalities found in their training environments (Bender et al., 2021).
The central communication question is not only whether AI is intelligent, but how it influences attention, authority, and decision-making. Automated systems can make support more accessible, yet they can also hide bias behind technical language or statistical confidence. Critical algorithm studies show that classification and ranking systems may reproduce social inequalities while appearing neutral (Noble, 2018).
Communication scholarship
Four lenses for deeper analysis.
These concepts offer starting points for studying the topic without reducing it to a single explanation.
Human-computer communication
A useful lens for examining how this topic shapes meaning, action, and public life.
Media ecology
A useful lens for examining how this topic shapes meaning, action, and public life.
Communication ethics
A useful lens for examining how this topic shapes meaning, action, and public life.
Questions worth exploring
Connect analysis to conflict and common ground.
- When does assistance become persuasion?
- What assumptions are built into an AI system’s outputs?
- Who is accountable when automated communication causes harm?
- How can users distinguish fluency from truth?
- Could AI be designed to surface uncertainty and common ground more clearly?
Look beyond the surface. Ask how meaning, identity, power, and relationship are being constructed.
Selected references
Scholarship supporting this overview.
- Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623.
- Noble, S. U. (2018). Algorithms of oppression. New York University Press.
More information coming soon
This topic hub will continue to grow.
Future additions may include original research, longer essays, visual analyses, teaching resources, case studies, and links to related commentary on Symbol Wizards and visual storytelling on Humanity’s Common Ground.