Why Take a Unidimensional Approach to Tools That Exist in High Dimensional Space?

There is a problematic and yet ubiquitous phenomenon in the field of AI research and product development: we’re too late to ask ourselves whether the algorithms we’ve been developing are built for purpose. Why? Because we’ve been misinterpreting “fit for purpose” to mean technically feasible. This definition obscures considerations of equal importance including the cultural, social, political and legal landscape that permeate the tool’s design and determine the tool’s utility.

When building AI products, researchers are only confronted with questions of multidisciplinary reflection as they’re about to submit a paper or launch their tool. However, at this point in the project, a host of relevant decisions have already been made, whether consciously or not, that influence the algorithms’ utility. These decisions begin before the algorithm has ever been trained or the data has ever been collected.

In this talk, I will discuss important points of inflection that are too often missed in the product development lifecycle. These inflection points present opportunities for AI researchers and product developers to ensure that the technology they’re building is fit for purpose.