Themes (353)
Find narratives by ethical themes or by technologies.
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- 6 min
- Wired
- 2019
Spreading of harmful content through Youtube’s AI recommendation engine algorithm. AI helps create filter bubbles and echo chambers. Limited user agency to be exposed to certain content.
- Wired
- 2019
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- 6 min
- Wired
- 2019
The Toxic Potential of YouTube’s Feedback Loop
Spreading of harmful content through Youtube’s AI recommendation engine algorithm. AI helps create filter bubbles and echo chambers. Limited user agency to be exposed to certain content.
How much agency do we have over the content we are shown in our digital artifacts? Who decides this? How skeptical should we be of recommender systems?
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- 7 min
- ZDNet
- 2020
Dr. Gary Marcus explains that deep machine learning as it currently exists is not maximizing the potential of AI to collect and process knowledge. He essentially argues that these machine “brains” should have more innate knowledge than they do, similar to how animal brains function in processing an environment. Ideally, this sort of baseline knowledge would be used to collect and process information from “Knowledge graphs,” a semantic web of information available on the internet which can sometimes be hard for an AI to process without translation to machine vocabularies such as RDF.
- ZDNet
- 2020
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- 7 min
- ZDNet
- 2020
Rebooting AI: Deep learning, meet knowledge graphs
Dr. Gary Marcus explains that deep machine learning as it currently exists is not maximizing the potential of AI to collect and process knowledge. He essentially argues that these machine “brains” should have more innate knowledge than they do, similar to how animal brains function in processing an environment. Ideally, this sort of baseline knowledge would be used to collect and process information from “Knowledge graphs,” a semantic web of information available on the internet which can sometimes be hard for an AI to process without translation to machine vocabularies such as RDF.
Does giving a machine similar learning capabilities to humans and animals bring artificial intelligence closer to singularity? Should humans ultimately be in control of what a machine learns? What is problematic about leaving AI less capable of understanding semantic webs?
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- 7 min
- Wired
- 2020
In discussing the history of the singular Internet that many global users experience every day, this article reveals some dangers of digital technologies becoming transparent through repeated use and reliance. Namely, it becomes more difficult to imagine a world where there could be alternatives to the current digital way of doing things.
- Wired
- 2020
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- 7 min
- Wired
- 2020
Hello, World! It is ‘I’, the Internet
In discussing the history of the singular Internet that many global users experience every day, this article reveals some dangers of digital technologies becoming transparent through repeated use and reliance. Namely, it becomes more difficult to imagine a world where there could be alternatives to the current digital way of doing things.
Is it too late to imagine alternatives to the Internet? How could people be convinced to get on board with a radical redo of the internet as we know it? Do alternatives need to be imagined before forming a certain digital product or service, especially if they end up being as revolutionary as the internet? Are the most popular and powerful digital technologies and services “tools”, or have they reached the status of cultural norms and conduits?
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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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- 4 min
- VentureBeat
- 2020
A study on the engine of TaskRabbit, an app which uses an algorithm to recommend the best workers for a specific task, demonstrates that even algorithms which attempt to account for fairness and parity in representation can fail to provide what they promise depending on different contexts.
- VentureBeat
- 2020
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- 4 min
- VentureBeat
- 2020
Researchers Find that Even Fair Hiring Algorithms Can Be Biased
A study on the engine of TaskRabbit, an app which uses an algorithm to recommend the best workers for a specific task, demonstrates that even algorithms which attempt to account for fairness and parity in representation can fail to provide what they promise depending on different contexts.
Can machine learning ever be enacted in a way that fully gets rid of human bias? Is bias encoded into every trained machine learning program? What does the ideal circumstance look like when using digital technologies and machine learning to reach a point of equitable representation in hiring?
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- 4 min
- OneZero
- 2020
A group of “Face Queens” (Dr. Timnit Gebru, Joy Buolamwini, and Deborah Raji) have joined forces to do important racial justice and equity work in the field of computer vision, struggling against racism in the industry to whistleblow against biased machine learning and computer vision technologies still deployed by companies like Amazon.
- OneZero
- 2020
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- 4 min
- OneZero
- 2020
Dr. Timnit Gebru, Joy Buolamwini, Deborah Raji — an Enduring Sisterhood of Face Queens
A group of “Face Queens” (Dr. Timnit Gebru, Joy Buolamwini, and Deborah Raji) have joined forces to do important racial justice and equity work in the field of computer vision, struggling against racism in the industry to whistleblow against biased machine learning and computer vision technologies still deployed by companies like Amazon.
How can the charge led by these women for more equitable computer vision technologies be made even more visible? Should people need high degrees to have a voice in fighting against technologies which are biased against them? How can corporations be made to listen to voices such as those of the Face Queens?