All Narratives (328)
Find narratives by ethical themes or by technologies.
FILTERreset filters-
- 5 min
- ABC News
- 2020
The United States government is pushing its interest in breaking up the tech monopoly that is Facebook, hoping to restore some competition in the social networking and data selling market which the company dominates. Facebook, of course, is resistant to these efforts.
- ABC News
- 2020
-
- 5 min
- ABC News
- 2020
Facebook hit with antitrust lawsuit from FTC and 48 state attorneys general
The United States government is pushing its interest in breaking up the tech monopoly that is Facebook, hoping to restore some competition in the social networking and data selling market which the company dominates. Facebook, of course, is resistant to these efforts.
What role did data collection and use play in Facebook’s rise as a monopoly power? What would breaking up this monopoly accomplish? Will users achieve more data privacy if one large company does not own several platforms on which users communicate?
-
- 7 min
- MIT Technology Review
- 2020
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
-
- 7 min
- MIT Technology Review
- 2020
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.
Does more efficiency mean more data would be wanted or needed? Would that be a good thing, a bad thing, or potentially both?
-
- 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
-
- 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?
-
- 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
-
- 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?
-
- 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
-
- 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?
-
- 5 min
- Business Insider
- 2020
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
-
- 5 min
- Business Insider
- 2020
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.
How can tech monopolies dismiss recommendations to make their technologies more ethical? How do bias ethicists such as Gebru get onto a more unshakeable platform? Who is going to hold tech monopolies more accountable? Should these monopolies even by trying to fix their current algorithms, or might it be better to just start fresh?