The Role of Digital Gig Workers in Machine Learning and Artificial Intelligence

The Role of Digital Gig Workers in Machine Learning and Artificial Intelligence

By: Mark Koh, CEO at Supahands

The Role of Digital Gig Workers in Machine Learning and Artificial Intelligence

The introduction of the gig economy has given workers an unprecedented amount of control, flexibility, and opportunity. Made possible through a variety of digital apps and platforms, the gig economy allows freelance workers to carry out a wide variety of tasks and jobs throughout the world. Businesses and job-seekers alike are able to benefit from such an arrangement, allowing organizations to scale and support operations while providing workers with flexible methods of attaining income.

Real World Gigs and Digital Gigs

Many associate gig workers have in-person service gigs, such as couriers and ride-share drivers. Delivering packages, escorting Grab passengers, and fetching food orders are among the most identifiable gig economy jobs held today.

The gig economy also makes it possible for workers to carry out tasks online, providing an array of services at a distance. From freelance writers to voice over artists, digital gig workers enjoy the same type of flexibility that physical gig workers enjoy, allowing them to adjust workloads and hours to accommodate their own needs and schedules.

Digital Gig Workers for Machine Learning

One key component of artificial intelligence (AI) is clean and accurate training data, which allows models to learn to see the world as humans. Recognizing patterns in audio, video, photos, and text is essential for AI to make intelligent decisions. Without high-quality information, AI and ML solutions produce less-than-ideal results.

Here are some common AI and ML microtasks carried out by digital gig workers:

  • Image annotation, which defines an area of an image for classification. For instance, drawing bounding boxes around a particular area of an image allows AI applications for retailers to recognize particular items on store shelves.
  • Data transcription, which produces text transcripts from audio, video, and image files. For instance, image-to-text transcriptions can improve the performance of optical character recognition systems.
  • Tagging and categorization, which cleans up raw datasets through the addition of relevant tags, keywords, and categories. For instance, adding relevant keywords or specific data attributes improves the accuracy of recommendation engines.

Taken together, these three types of microtasks help improve the overall intelligence of AI applications. While automated solutions exist to help execute many of these tasks, they often lack the accuracy and the human touch required to determine whether data is of high quality.

Mutually Beneficial Flexibility

The gig economy has made it possible for businesses and gig workers to collaborate on positive solutions. Digital gig workers are also afforded the opportunity to work anytime, anywhere, through the continence of their own personal computer. As the world becomes more connected and the traditional idea of work further decentralizes, gig workers will continue to play an important and mutually beneficial role.

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