Digital Literacy and Digital Labor: Exploring the Relationship Between Data-Centric AI and Human-Centric Work
The digital labor market at a glance
How much is the digital labor market growing?
Digital labor is transforming the way we work. As of last year, the online workforce contained over 19 million active users. This year, digital labor platforms are expected to generate $52 billion globally.
The U.S. technology sector is closely linked to this rapid increase. Just within the U.S., there are expected to be over 500,000 new computer and information technology jobs in the next decade; about an 11% growth.
Why does digital literacy development matter?
As COVID-19 revealed the historically well-hidden problems facing workers, it became clear that something needed to change. Specifically, labor and education need to be treated as two sides of the same coin. We need to change how we train in order to improve how we work.
Companies that use digital labor must bring awareness and intention as they engage workforces abroad. Furthermore, there is the opportunity to create mutually beneficial improvements to labor and technology markets, local and abroad.
“AI is the new electricity.”
-Dr. Andrew Ng
“Literacy is a resource in the way that electricity is a resource: it keeps the lights on.”
-Dr. Deborah Brandt
What is Literacy?
Most people only think about literacy when discussing what someone lacks. A person is “illiterate” when they can’t read or write. Rarely do we define or nuance the opposite; being literate. However, being literate is not an accident. It is the result of myriad social and economic factors throughout an entire human life. Literacy is both learned and lived in. Though it’s taken for granted by most, it is an essential tool for navigating the world.
Annotation as digital literacy
It is not easy to define what makes a competent annotator; it is more than just making a selection. Rather, effective annotators decipher rigid, sometimes vague, instructions and interpret them across massive datasets. More than simply drawing a box, effective annotators must intuit what is or isn’t important to a model they may never see. From this perspective, effective annotation becomes a digital literacy; a mix of competencies that let annotators discover “how?” and “why?” independently.
Sponsoring Digital Literacy
Designers and users of annotation platforms have the potential to revolutionize the labor ML/AI is built on by becoming active sponsors of digital literacy. In her words, rhetorical scholar Dr. Deborah Brandt says sponsors of literacy are, “any agents, local or distant, concrete or abstract, who enable, support, teach, and model, as well as recruit, regulate, suppress, or withhold literacy—and gain advantage by it in some way.” The advantages of sponsorship are clear; making the digital workforce more competent makes the results of its labor higher quality.
Treating every step of the annotation process as literacy development—the accumulation of skill over time—brings a data-centric approach from start to finish. Enhancing the ML/AI process is not a singular person’s task. Rather, the work begins even before the data is annotated; the future of AI depends on what competencies we offer our workforce.
We are a group of scientists, engineers, and entrepreneurs with a vision for better AI. With backgrounds primarily in Machine Learning and Computer Vision, the Innotescus team understands the importance of having full control over and insight into data used to train Machine Learning models.
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