Social Networks (42)
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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- 4 min
- TechCrunch
- 2021
On the day of the January 6th insurrection at the U.S Capitol, social media proved to be a valuable tool for telling the narrative of the horrors taking place within the Capitol building. At the same time, social media plays a large role in political polarization, as users can end up on fringe sites where content is tailored to their beliefs and not always true.
- TechCrunch
- 2021
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- 4 min
- TechCrunch
- 2021
Social media allowed a shocked nation to watch a coup attempt in real time
On the day of the January 6th insurrection at the U.S Capitol, social media proved to be a valuable tool for telling the narrative of the horrors taking place within the Capitol building. At the same time, social media plays a large role in political polarization, as users can end up on fringe sites where content is tailored to their beliefs and not always true.
How can social media platforms be redesigned or regulated to crack down more harshly on misinformation and extremism? How much can social media be valued as a set of platforms that “help tell the true story of an event” when they also allow mass denial of objective fact? Who should be responsible for shutting down fringe sites, and how should this happen?
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- 3 min
- Kinolab
- 2014
Donald and Helen, a married couple, are both dissatisfied with their marriage, particularly in their sexual relationship, and so unwilling to communicate with each other that they both want to cheat on each other. The technology in this clip are the websites on which they both succumb to the temptation of an affair.
- Kinolab
- 2014
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- 3 min
- Kinolab
- 2014
Infidelity and Social Networks
Donald and Helen, a married couple, are both dissatisfied with their marriage, particularly in their sexual relationship, and so unwilling to communicate with each other that they both want to cheat on each other. The technology in this clip are the websites on which they both succumb to the temptation of an affair.
Are websites like the ones shown in this narrative a justifiable affordance of social networks and digital technologies? Does the facility of making connections with other people make infidelity overall easier to accomplish? Does having these more private, secluded channels make communication between dissatisfied partners harder? In thinking particularly about the “Escort Edition” website, what is problematic about its quantification of women and “shopping” user interface?
Men, Women and Children: Extramarital Affairs-
- 41 min
- The New York Times
- 2021
In this podcast episode, Ellen Pao, an early whistleblower on gender bias and racial discrimination in the tech industy, tells the story of her experience suing the venture capital firm Kleiner Perkins for gender discrimination. The episode then moves into a discussion of how Silicon Valley, and the tech industry more broadly, is dominated by white men who do not try to deeply understand or move toward racial or gender equity; instead, they focus on PR moves. Specifically, she reveals that social media companies and CEOs can be particularly performative when it comes to addressing racial or gender inequality, focusing on case studies rather than breeding a new, more fair culture.
- The New York Times
- 2021
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- 41 min
- The New York Times
- 2021
Sexism and Racism in Silicon Valley
In this podcast episode, Ellen Pao, an early whistleblower on gender bias and racial discrimination in the tech industy, tells the story of her experience suing the venture capital firm Kleiner Perkins for gender discrimination. The episode then moves into a discussion of how Silicon Valley, and the tech industry more broadly, is dominated by white men who do not try to deeply understand or move toward racial or gender equity; instead, they focus on PR moves. Specifically, she reveals that social media companies and CEOs can be particularly performative when it comes to addressing racial or gender inequality, focusing on case studies rather than breeding a new, more fair culture.
How did Silicon Valley and the technology industry come to be dominated by white men? How can this be addressed, and how can the culture change? How can social networks in particular be re-imagined to open up doors to more diverse leadership and workplace cultures?
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- 35 min
- Wired
- 2021
In this podcast, interviewees share several narratives which discuss how certain technologies, especially digital photo albums, social media sites, and dating apps, can change the nature of relationships and memories. Once algorithms for certain sites have an idea of what a certain user may want to see, it can be hard for the user to change that idea, as the Pinterest wedding example demonstrates. When it comes to photos, emotional reactions can be hard or nearly impossible for a machine to predict. While dating apps do not necessarily make a profit by mining data, the Match monopoly of creating different types of dating niches through a variety of apps is cause for some concern.
- Wired
- 2021
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- 35 min
- Wired
- 2021
How Tech Transformed How We Hook Up—and Break Up
In this podcast, interviewees share several narratives which discuss how certain technologies, especially digital photo albums, social media sites, and dating apps, can change the nature of relationships and memories. Once algorithms for certain sites have an idea of what a certain user may want to see, it can be hard for the user to change that idea, as the Pinterest wedding example demonstrates. When it comes to photos, emotional reactions can be hard or nearly impossible for a machine to predict. While dating apps do not necessarily make a profit by mining data, the Match monopoly of creating different types of dating niches through a variety of apps is cause for some concern.
How should algorithms determine what photos a specific user may want to see or be reminded of? Should machines be trusted with this task at all? Should users be able to take a more active role in curating their content in certain albums or sites, and would most users even want to do this? Does the existence of dating apps drastically change the nature of dating? How could creating a new application which introduces a new dating “niche” ultimately serve a tech monopoly?
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- 4 min
- Reuters
- 2020
Facebook has a new independent Oversight Board to help moderate content on the site, picking individual cases from the many presented to them where it is alright to remove content. The cases usually deal in hate speech, “inappropriate visuals,” or misinformation.
- Reuters
- 2020
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- 4 min
- Reuters
- 2020
From hate speech to nudity, Facebook’s oversight board picks its first cases
Facebook has a new independent Oversight Board to help moderate content on the site, picking individual cases from the many presented to them where it is alright to remove content. The cases usually deal in hate speech, “inappropriate visuals,” or misinformation.
How much oversight do algorithms or networks with a broad impact need? Who all needs to be in a room when deciding what an algorithm or site should or should not allow? Can algorithms be designed to detect and remove hate speech? Should such an algorithm exist?