Recognizing the Standard Discrimination in Datasets
Much of the public discourse about tech originally focused on how the outcomes of tech tools, e.g., software, affected people. Then, the conversation turned to algorithms and how they are designed to be exclusionary. Now, we’re having more conscious discussions about the data, both input and output, of these algorithms and how data is processed in digital systems. We’ve grappled, however, with how to handle the multifaceted influences of data outcomes. As society has leaned into globalization, the pressure to scale algorithmic approaches and be computational efficient has made all of us irrevocably bonded to collected, digitized data. It’s like all truths are contained within the digital walls around collected data.
Data makes up the raw ingredients of the tech infrastructure landscape. Data, as a topic, isn’t as tantalizing as proposing AI as the everything-solution or as attention grabbing as a data visualization dashboard. Showing your geekiness by talking about algorithms and data is seen as too serious and heavy to be relatable in mainstream conversations. But data makes those other part of the tech ecosystem possible. Our digitized society has unfortunately acquiesced critical decisions to faulty computational approaches. And data becomes this unbearably large and complicated tangled web that can’t be understood by the general public. But to be…
