Machine Learning (80)

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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
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  • Immersive Technology
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  • Year
    • 1916 - 1966
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    • 2019 - 2069
  • Duration
  • 7 min
  • The New Republic
  • 2020
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Who Gets a Say in Our Dystopian Tech Future?

The narrative of Dr. Timnit Gebru’s termination from Google is inextricably bound with Google’s irresponsible practices with training data for its machine learning algorithms. Using large data sets to train Natural Language Processing algorithms is ultimately a harmful practice because for all the harms to the environment and biases against certain languages it causes, machines still cannot fully comprehend human language.

  • The New Republic
  • 2020
  • 7 min
  • MIT Technology Review
  • 2020
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Tiny four-bit computers are now all you need to train AI

This article details a new approach emerging in AI science; instead of using 16 bits to represent pieces of data which train an algorithm, a logarithmic scale can be used to reduce this number to four, which is more efficient in terms of time and energy. This may allow machine learning algorithms to be trained on smartphones, enhancing user privacy. Otherwise, this may not change much in the AI landscape, especially in terms of helping machine learning reach new horizons.

  • MIT Technology Review
  • 2020
  • 4 min
  • VentureBeat
  • 2020
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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.

  • VentureBeat
  • 2020
  • 5 min
  • Business Insider
  • 2020
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One of Google’s leading AI researchers says she’s been fired in retaliation for an email to other employees

This article tells the story of Timnit Gebru, a Google employee who was fired after Google refused to take her research on machine learning and algorithmic bias into full account. She was terminated hastily after sending an email asking Google to meet certain research-based conditions. Gebru is a leading expert in the field of AI and bias.

  • Business Insider
  • 2020
  • 7 min
  • MIT Tech Review
  • 2020
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Why 2020 was a pivotal, contradictory year for facial recognition

This article examines several case studies from the year of 2020 to discuss the widespread usage, and potential for limitation, of facial recognition technology. The author argues that its potential for training and identification using social media platforms in conjunction with its use by law enforcement is dangerous for minority groups and protestors alike.

  • MIT Tech Review
  • 2020
  • 7 min
  • ZDNet
  • 2020
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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.

  • ZDNet
  • 2020
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