Accountability (34)

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Find narratives by ethical themes or by technologies.

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Themes
  • Privacy
  • Accountability
  • Transparency and Explainability
  • Human Control of Technology
  • Professional Responsibility
  • Promotion of Human Values
  • Fairness and Non-discrimination
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Technologies
  • AI
  • Big Data
  • Bioinformatics
  • Blockchain
  • Immersive Technology
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  • Year
    • 1916 - 1966
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    • 2019 - 2069
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  • 5 min
  • GIS Lounge
  • 2019
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When AI Goes Wrong in Spatial Reasoning

GIS, a relatively new form of computational analysis, can often contain algorithms with biases based on biases present in the training data from open data sources, with this case study focusing on the tendency of power-line identification data being centered around the Western world. This problem can be improved by approaching data collection with more intentionality, either broadening the pool of collected geographic data or inputting artificial images to help the tool recognize a greater number of circumstances and thus become more accurate.

  • GIS Lounge
  • 2019
  • 16 min
  • Kinolab
  • 2003
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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.

  • Kinolab
  • 2003
  • 10 min
  • MIT Technology Review
  • 2020
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We read the paper that forced Timnit Gebru out of Google. Here’s what it says.

This article explains the ethical warnings of Timnit Gebru against training Natural Language Processing algorithms on large language models developed on sets of textual data from the internet. Not only does this process have a negative environmental impact, it also still does not allow these machine learning tools to process semantic nuance, especially as it relates to burgeoning social movements or countries with lower internet access. Dr. Gebru’s refusal to retract this paper ultimately lead to her dismissal from Google.

  • MIT Technology Review
  • 2020
  • 5 min
  • Gizmodo
  • 2021
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Bots Reportedly Helped Fuel GameStonks Hype on Facebook, Twitter, and Other Platforms

Thorough investigation led to the conclusion that bots played a role in the economic disruption of GameStop stocks in early 2021. Essentially, the automated accounts aided in the diffusion of materials promoting the purchase and maintenance of GameStop stocks as a ploy to act as a check on wealthy hedge fund managers who bet that the stock would crash. The wholistic effect of these bots in this specific campaign, and thus a measure of how bots may generally be used to cause economic disruption in online markets through interaction with humans, remains hard to read.

  • Gizmodo
  • 2021
  • 30 min
  • UNIVERSITY OF WÜRZBURG GRADUATE SCHOOLS
  • 1982
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Cyberspace and Internet Imaginations: “Burning Chrome” by William Gibson

Hardware specialist Automatic Jack is roped into a dangerous hacking scheme with his partner Bobby Quine while they compete for the affections of Rikki. Their plan is to use deadly malware to infiltrate the protections of Chrome, a mysterious overlord of cyberspace who hoards massive amounts of wealth. They enact this plan by entering cyberspace within a program and visualizing the data held within this digital network which connects people all across the globe.

  • UNIVERSITY OF WÜRZBURG GRADUATE SCHOOLS
  • 1982
  • 7 min
  • Venture Beat
  • 2021
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Center for Applied Data Ethics suggests treating AI like a bureaucracy

As machine learning algorithms become more deeply embedded in all levels of society, including governments, it is critical for developers and users alike to consider how these algorithms may shift or concentrate power, specifically as it relates to biased data. Historical and anthropological lenses are helpful in dissecting AI in terms of how they model the world, and what perspectives might be missing from their construction and operation.

  • Venture Beat
  • 2021
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