my attempt to "TLDR" for folks / personal interpretation:
Commercialized AI is often powered by low-paid workers in foreign countries who perform tasks such as labeling images and annotating objects in videos.
These tasks, which are outsourced to gig workers and data training companies, are important for training AI systems.
Many tech companies imagine a future where AI will replace human labor, but the reality is that much of what is considered "AI" is actually powered by human labor.
The people performing these tasks often do not have insight into what their work will ultimately be used for.
The use of low-paid labor in the development of AI raises ethical questions about the treatment of workers and the accuracy of the data they provide.
Amazon's Mechanical Turk platform allows businesses to hire workers to perform tasks for compensation, including training AI projects.
Many of these tasks are outsourced to gig workers and data training companies, often in developing countries.
Turkopticon, a platform created by workers, aims to address the power imbalance between workers and requesters on Mechanical Turk.
Poor working conditions and low pay on gig work platforms like Mechanical Turk can lead to lower quality work for clients.
​
Pros of using low-paid labor to develop AI:
Cost savings: Outsourcing tasks to workers in developing countries can be less expensive for companies than hiring in-house workers to perform the same tasks.
Access to a large labor pool: Crowdsourcing platforms like Mechanical Turk allow companies to access a large pool of workers from around the world.
Efficiency: Using large numbers of workers to label data or perform other tasks can be a quick and efficient way to train AI systems.
Cons of using low-paid labor to develop AI:
Ethical concerns: There are concerns about the treatment of workers on crowdsourcing platforms, including low pay, poor working conditions, and lack of job security.
Quality of work: Poor working conditions and low pay can lead to lower quality work, which can impact the accuracy of the data being used to train AI systems.
Lack of transparency: Workers may not have insight into what their work is being used for, raising questions about accountability and transparency in the development of AI.
There are a few alternatives to using low-paid labor to perform tasks such as data labeling for machine learning operations (industry term for this arduous process): Automation: One possibility is to develop automated tools that can perform tasks such as data labeling without human intervention. This can be more efficient and cost-effective, but it also has its own limitations and may not be suitable for all types of tasks.
In-house teams: Companies can also choose to hire in-house teams to perform tasks such as data labeling. This can help ensure better working conditions and higher pay for workers, but it may also be more expensive and may require more resources to manage.
Volunteer efforts: Some companies have also turned to volunteer efforts to gather data or perform other tasks. For example, the Zooniverse projectrelies on volunteers to classify and label images and other data for use in scientific research. This can be a cost-effective option, but it may also be less reliable and may not be suitable for all types of tasks.
aqeelmeetsworld t1_j21pc6h wrote
Reply to How AI innovation is powered by underpaid workers in foreign countries. by eddytony96
my attempt to "TLDR" for folks / personal interpretation:
​
Pros of using low-paid labor to develop AI:
Cost savings: Outsourcing tasks to workers in developing countries can be less expensive for companies than hiring in-house workers to perform the same tasks.
Access to a large labor pool: Crowdsourcing platforms like Mechanical Turk allow companies to access a large pool of workers from around the world.
Efficiency: Using large numbers of workers to label data or perform other tasks can be a quick and efficient way to train AI systems.
Cons of using low-paid labor to develop AI:
Ethical concerns: There are concerns about the treatment of workers on crowdsourcing platforms, including low pay, poor working conditions, and lack of job security.
Quality of work: Poor working conditions and low pay can lead to lower quality work, which can impact the accuracy of the data being used to train AI systems.
Lack of transparency: Workers may not have insight into what their work is being used for, raising questions about accountability and transparency in the development of AI.
There are a few alternatives to using low-paid labor to perform tasks such as data labeling for machine learning operations (industry term for this arduous process):
Automation: One possibility is to develop automated tools that can perform tasks such as data labeling without human intervention. This can be more efficient and cost-effective, but it also has its own limitations and may not be suitable for all types of tasks.
In-house teams: Companies can also choose to hire in-house teams to perform tasks such as data labeling. This can help ensure better working conditions and higher pay for workers, but it may also be more expensive and may require more resources to manage.
Volunteer efforts: Some companies have also turned to volunteer efforts to gather data or perform other tasks. For example, the Zooniverse projectrelies on volunteers to classify and label images and other data for use in scientific research. This can be a cost-effective option, but it may also be less reliable and may not be suitable for all types of tasks.