Hypothesis
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Introduction
The Hypothesis Vision
Building a platform based on openness & transparency
Conclusion
Defining "Trust" in a "Post-Truth" World
Hypothesis is searching for a sustainable future where it serves as a useful tool for building a better, more honest way to produce and interact with information online. Its goal is to create a new set of equitable and actionable data points that accurately represent a person’s understanding of the world and hold accountable the creators and disseminators of the information that, upon consumption, forms such an understanding. Implemented correctly, these data points can lead to a new way of interacting with media and invigorate the valuation of objectivity as a cornerstone of inclusive, humanistic discourse that feels accessible to the broadest possible cross section of society.
From its methods of formulating Foresight and Usefulness to those dictating the financial compensation of content creators, Hypothesis maintains a commitment to the transparency required of a platform with a framework attempting to incentivize such a monumental shift in our relationship with truth.
Our company is founded on the notion that—when structured correctly—a simple yes-no prediction contains all of the qualities required to represent an objective description of reality. The question “Will x happen? Yes or no?” wonders whether a future outcome will align with a present estimation of its eventual possibility. Upon resolution, the questioned outcome either happened (yes) or didn’t (no). This simplicity creates a much-needed space for meaningful, vibrant discourse about the nuances of a given outcome.
Accordingly, Hypothesis defines trust as a function of effective, transparent mechanisms that lead to a meaningful relationship with verifiable information, and by extension, truth. These two concepts: trust and truth, are foundational to Hypothesis’ success. A Hypothesis community member must first trust that the system offers an honest, equitable means by which they can evaluate the world. They must also trust that our algorithms' quantification of the work they do to build a greater understanding of the world is a truly accurate representation of their knowledge.
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