This is part 22 of “101 Ways AI Can Go Wrong” - a series exploring the interaction of AI and human endeavor through the lens of the Crossfactors framework.
View all posts in this series or explore the Crossfactors Framework
The use of Model Context Protocol (MCP) with Agentic AI is a method to mitigate this fundamental problem.
Context Collapse is factor #22 in my series on 101 Ways To Mess Things Up With AI.
What is it?
Context collapse occurs when information or opinions are shared but the required context to understand the intended meaning is unavailable or ignored. It’s been highlighted mainly in the case of social media and with a negative connotation. But it can be as simple as being puzzled by an inside joke or a home assistant misinterpreting your meaning.
Why It Matters
Context matters a great deal and more meaning is inferred into many aspects of our lives than we realize. But digital communications, both private and public, limit opportunities to attach context and tone to content. In social media, content can go viral, often hopping from one audience to another, each with a distinct perceptual profile.
Social media curation, content moderation and chatbots can all suffer from context collapse in ways that can negatively affect humans or degrade the intended utility. Context collapse can also occur the other way around. It is difficult and even sometimes impossible to guarantee that all necessary context is shared with the human interacting with a system.
Real-World Example
As humans, we’re aware of context collapse on some level and we try to guard against it on social media. Or at least we should. On LinkedIn, when you see a post that grabs your attention, you may check the poster’s profile to get more information about where they are coming from. You may check past posts and comments. You will also check the date of the post. A discussion on tariffs today is much different from one from two weeks ago!
Here’s another harmless example. You’ve hopefully had a good laugh earlier this week on April Fools as folks posted various announcements or fake products as jokes. But what happens as LinkedIn recycles this content in the coming weeks, as the lifetime of a post stretches longer than a single day? These jokes will be seen out of context and may leave some puzzled or misled, especially since the best jokes are the ones that might be outrageous yet believable - until you remembered what day it was.
Key Dimensions
Contextual depth - obtain and curate context-rich datasets.
Data blindspots - data can omit specific cultural or situational dimensions.
Correction mechanisms - detect and correct collapse in real-time.
Take-away
What we build must recognize that context is central to human understanding.