This is part 12 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
Here’s a principle every genAI booster should take note of - the Collingridge Dilemma.
What is it
The Collingridge Dilemma describes how technologies in their early stages are easy to control, but nearly impossible to predict, while in later stages, as their consequences become clearer, the opportunity to control them has passed.
Why It Matters
The Collingridge Dilemma has become incredibly relevant in recent decades, as the internet and computers have allowed the proliferation and scaling of technologies at previously impossible rates. At the extreme, this dilemma is embodied by the fear of AGI take-off. However, long before this may happen, the self-replicating and self-modifying features of future AI deployments will continue to shorten the Window of Opportunity where technologies can be influenced in their formative phases.
Real-World Examples
An example of the Collingridge Dilemma that evolved from both our digital and physical spaces is the proliferation of ride-sharing platforms such as Uber and Lyft in urban settings.
The disruption of traditional taxi services was initially seen as a net positive by the public because of the combination of smart-phone based hailing and lower prices. Advantages were also seen on the supply side, as drivers could use their existing vehicles on their own schedule outside the regulatory oversight of traditional taxis.
As Uber and Left proliferated several serious issues started to become apparent:
- Exploitation of gig workers.
- Increased traffic congestion due to vehicles circling around waiting for a fare.
- Predatory pricing at peak times.
- Erosion of public transit.
- Increase of prices after traditional taxis were no longer around to compete and from venture capital was removed.
- Abuse of the platform by both drivers and riders.
The long term impact of ride-sharing services is still playing out now, through legal and regulatory battles, traditional taxis evolving with their own technology, local legislative changes, and labour disputes.
Key Dimensions
The Anticipatory Gap - We must recognize that the technology is nascent and that we are blind to the eventual outcomes.
Legislation - Legislation must recognize the above and anchor itself into fundamental human principles. It must allow for adaptive governance and domain-specific regulation, like in medicine.
Trial and error - The reach and stakes must be incremented through trial and error. This is what will allow us to learn from our mistakes before they are too widespread to fix.
Take-away
The Collingridge Dilemma explains why we need to build with humility, not hype.