Most history struggles are fights to decide who takes control over certain resources. Slaves fought because they wanted to be decision makers of their jobs and bodies. Wars are fought to decide who is the decision maker of certain territory. Although rarely seen that way, debates about Artificial Intelligence share a common factor: decision-making power.
Some artists want decision-making power on whether their art trains AI models or not. Software engineers fear AI will overrule their instructions—their decision making. Others are concerned about AI's amplification capabilities for surveillance and control.
Whether you're an artist, a programmer, or an internet user, chances are your main worry is all about who controls Artificial Intelligence.
Even the most anti-AI person could agree that there are useful cases for large language models that could be worth exploring—if servers are hosted locally (with little to no damage to the environment). That is, of course, if they had the power to decide how and to what extent Artificial Intelligence will be used in our daily life (and consequently their effects). In other words, as long as they feel they have influence over AI's decision-making capabilities, use cases and outcomes.
What this means is that, as uncomfortable as it sounds, most Artificial Intelligence debates are not about the technology itself but who controls it. This could be a positive thing if we understand what is behind "decision-making power": the need for autonomy and certainty. Perhaps we all would understand each other better if we knew we all are looking to fulfill the same human needs. And perhaps it'd be easier to find solutions that satisfy everyone's need for autonomy and certainty if we focus on strategies that make them possible.
Universal Language Framework Applied
Needs
- Autonomy: People want meaningful influence over decisions that affect them.
- Certainty: People want predictable rules and consequences.
- Clarity: People want to understand what is happening and why.
Strategies
- Opt-in or opt-out systems for training data.
- Disclosure or labeling systems for AI-generated content.
- Clear public policies about how AI is developed and used.
- Community governance models that include affected stakeholders.
When we shift from arguing about positions to understanding the underlying needs—autonomy, certainty, clarity—the conversation changes. Instead of "You're wrong about AI," it becomes "How can we design systems that give everyone meaningful influence over outcomes that affect them?" That is the power of a framework: it turns fights into design problems.