Issues relating to the unequal access to or usage of digital technologies or networks.
Digital Divides and Inequality (12)
Find narratives by ethical themes or by technologies.
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- 14 min
- Kinolab
- 2008
After his family home is destroyed and his father is killed, Memo must become a part of the global economy. He is expected to do this at the Sleep Dealer Factory, where citizens of Mexico who are implanted with “nodes” connect to a brain-computer interface which they use to remotely control robots in the United States. This was meant to be a solution to the “migrant problem” to the United States in this imagined future, allowing the United States to contract labor from immigrants without actually having people cross the border. However, the wages payed by the Sleep Dealers for the exhaustive labor are incredibly low, thus most laborers there live in unlivable conditions. The technology is shown to not only be exhausting due to the menial labor, but also dangerous if someone is connected during a short-circuit.
- Kinolab
- 2008
Networked Laborers and Remote Workforces
After his family home is destroyed and his father is killed, Memo must become a part of the global economy. He is expected to do this at the Sleep Dealer Factory, where citizens of Mexico who are implanted with “nodes” connect to a brain-computer interface which they use to remotely control robots in the United States. This was meant to be a solution to the “migrant problem” to the United States in this imagined future, allowing the United States to contract labor from immigrants without actually having people cross the border. However, the wages payed by the Sleep Dealers for the exhaustive labor are incredibly low, thus most laborers there live in unlivable conditions. The technology is shown to not only be exhausting due to the menial labor, but also dangerous if someone is connected during a short-circuit.
How could technology theoretically exacerbate the gross xenophobia displayed toward Mexican immigrants in the United States? How does automation lower the value of labor, causing harm to those communities who need jobs? How can automation and robots be used to avoid putting workers in dangerous scenarios? Could a system using the technologies displayed in this narrative ever be designed to be truly fair?
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- 16 min
- Kinolab
- 2003
In a distant future, the majority of humanity has been wiped out, and most of the planet is flooded. ECOBAN is a city which runs on technological power, avoiding destruction and pollution by using a machine which converts pollutants into power. However, Marrians, who live on the exterior of the city in the destroyed world, are responsible for performing the labor to harvest these pollutants, without any of the benefits. Essentially, Ecoban keeps its technology to itself, not sharing it with the “contaminated” underclasses. Shua, a renegade Marrian hacker, attempts to shut down the DELOS system, the technology which powers Ecoban and has destroyed the surrounding environment entirely. He ultimately succeeds in his mission, breaking the DELOS system which gave Ecobans a privileged life and at last bringing back blue skies.
- Kinolab
- 2003
Technological Regulation of the Environment and Division
In a distant future, the majority of humanity has been wiped out, and most of the planet is flooded. ECOBAN is a city which runs on technological power, avoiding destruction and pollution by using a machine which converts pollutants into power. However, Marrians, who live on the exterior of the city in the destroyed world, are responsible for performing the labor to harvest these pollutants, without any of the benefits. Essentially, Ecoban keeps its technology to itself, not sharing it with the “contaminated” underclasses. Shua, a renegade Marrian hacker, attempts to shut down the DELOS system, the technology which powers Ecoban and has destroyed the surrounding environment entirely. He ultimately succeeds in his mission, breaking the DELOS system which gave Ecobans a privileged life and at last bringing back blue skies.
How can it be ensured that technology built with the aim to reverse climate change or otherwise aid the environment helps all people, and not just certain higher classes? How can governments or leaders support “Robin Hood” hackers who disrupt technology for a greater good? Who is responsible for bridging digital divides and bringing technological equality to disadvantaged communities, and how should this be approached? How should technology be created to be accessible to all communities?
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- 14 min
- Kinolab
- 1973
On a faraway planet, kidnapped humans under the name of Oms live as an inferior race to the Draggs, giant blue aliens that either keep the Oms as pets or banish them to the wilds to be consumed by extraterrestrial monsters. One of these Oms, Terr, is the pet of Tiwa, and begins to acquire an education through a malfunction of Tiwa’s brain-computer interface, which beams knowledge directly into her head. Terr eventually uses this cutting edge technology to which Oms do not usually have access to spread knowledge to other Oms and begin a revolt.
- Kinolab
- 1973
Technology and Educational Inequalities
On a faraway planet, kidnapped humans under the name of Oms live as an inferior race to the Draggs, giant blue aliens that either keep the Oms as pets or banish them to the wilds to be consumed by extraterrestrial monsters. One of these Oms, Terr, is the pet of Tiwa, and begins to acquire an education through a malfunction of Tiwa’s brain-computer interface, which beams knowledge directly into her head. Terr eventually uses this cutting edge technology to which Oms do not usually have access to spread knowledge to other Oms and begin a revolt.
How can access to technology determine the quality of education that a certain person or group receives? How are people with less technological access or fluency somewhat at the mercy of those with more? How can educational technologies be made more equitable?
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- 51 min
- TechCrunch
- 2020
In this podcast, several disability experts discuss the evolving relationship between disabled people, society, and technology. The main point of discussion is the difference between the medical and societal models of disability, and how the medical lens tends to spur technologies with an individual focus on remedying disability, whereas the societal lens could spur technologies that lead to a more accessible world. Artificial Intelligence and machine learning is labelled as inherently “normative” since it is trained on data that comes from a biased society, and therefore is less likely to work in favor of a social group as varied as disabled people. There is a clear need for institutional change in the technology industry to address these problems.
- TechCrunch
- 2020
Artificial Intelligence and Disability
In this podcast, several disability experts discuss the evolving relationship between disabled people, society, and technology. The main point of discussion is the difference between the medical and societal models of disability, and how the medical lens tends to spur technologies with an individual focus on remedying disability, whereas the societal lens could spur technologies that lead to a more accessible world. Artificial Intelligence and machine learning is labelled as inherently “normative” since it is trained on data that comes from a biased society, and therefore is less likely to work in favor of a social group as varied as disabled people. There is a clear need for institutional change in the technology industry to address these problems.
What are some problems with injecting even the most unbiased of technologies into a system biased against certain groups, including disabled people? How can developers aim to create technology which can actually put accessibility before profit? How can it be ensured that AI algorithms take into account more than just normative considerations? How can developers be forced to consider the myriad impacts that one technology may have on large heterogeneous communities such as the disabled community?
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- 5 min
- TechCrunch
- 2020
At the end of 2020, Twitch, a social network predicated on streaming video content and commenting, expanded and clarified its definitions of hateful content in order to moderate comments or posts which harassed other users or otherwise had a negative effect on other people. However, as a workplace, the Twitch company has much to prove before validating this updated policy as something more than a PR move.
- TechCrunch
- 2020
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- 5 min
- TechCrunch
- 2020
Twitch updates its hateful content and harassment policy after company called out for its own abuses
At the end of 2020, Twitch, a social network predicated on streaming video content and commenting, expanded and clarified its definitions of hateful content in order to moderate comments or posts which harassed other users or otherwise had a negative effect on other people. However, as a workplace, the Twitch company has much to prove before validating this updated policy as something more than a PR move.
How can content moderation algorithms be used for a greater good, in terms of recognizing hate speech and symbols? What nuances might be missed by this approach? What does the human part of content moderation look like? What responsibilities does such a position come with? How might content moderation on digital platforms moderate harassment behaviors in real life, and vice versa?
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- 6 min
- TED
- 2020
Jamila Gordon, an AI activist and the CEO and founder of Lumachain, tells her story as a refugee from Ethiopia to illuminate the great strokes of luck that eventually brought her to her important position in the global tech industry. This makes the strong case for introducing AI into the workplace, as approaches using computer vision can lead to greater safety and machine learning can be applied to help those who may speak a language not dominant in that workplace or culture train and acclimate more effectively.
- TED
- 2020
How AI can help shatter barriers to equality
Jamila Gordon, an AI activist and the CEO and founder of Lumachain, tells her story as a refugee from Ethiopia to illuminate the great strokes of luck that eventually brought her to her important position in the global tech industry. This makes the strong case for introducing AI into the workplace, as approaches using computer vision can lead to greater safety and machine learning can be applied to help those who may speak a language not dominant in that workplace or culture train and acclimate more effectively.
Would constant computer vision surveillance of a workplace be ultimately positive or negative or both? How could it be ensured that machine learning algorithms were only used for positive forces in a workplace? What responsibility to large companies have to help those in less privileged countries access digital fluency?