Machine Learning (79)
Find narratives by ethical themes or by technologies.
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- 5 min
- MIT Technology Review
- 2019
Humans take the blame for failures of AI automated systems, protecting the integrity of the technological system and becoming a “liability sponge.” It is necessary to redefine the role of humans in sociotechnical systems.
- MIT Technology Review
- 2019
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- 5 min
- MIT Technology Review
- 2019
When algorithms mess up, the nearest human gets the blame
Humans take the blame for failures of AI automated systems, protecting the integrity of the technological system and becoming a “liability sponge.” It is necessary to redefine the role of humans in sociotechnical systems.
Should humans take the blame for algorithm-created harm? At what level (development, corporate, or personal) should this liability occur?
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- 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
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- 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?
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- 5 min
- MIT Tech Review
- 2020
With the surge of the coronavirus pandemic, the year 2020 became an important one in terms of new applications for deepfake technology. Although a primary concern of deepfakes is their ability to create convincing misinformation, this article describes other uses of deepfake which center more on entertaining, harmless creations.
- MIT Tech Review
- 2020
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- 5 min
- MIT Tech Review
- 2020
The Year Deepfakes Went Mainstream
With the surge of the coronavirus pandemic, the year 2020 became an important one in terms of new applications for deepfake technology. Although a primary concern of deepfakes is their ability to create convincing misinformation, this article describes other uses of deepfake which center more on entertaining, harmless creations.
Should deepfake technology be allowed to proliferate enough that users have to question the reality of everything they consume on digital platforms? Should users already approach digital media with such scrutiny? What is defined as a “harmless” use for deepfake technology? What is the danger posed to real people in the acting industry with the rise of convincing synthetic media?
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- 12 min
- Wired
- 2018
This video offers a basic introduction to the use of machine learning in predictive policing, and how this disproportionately affects low income communities and communities of color.
- Wired
- 2018
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- 12 min
- Wired
- 2018
How Cops Are Using Algorithms to Predict Crimes
This video offers a basic introduction to the use of machine learning in predictive policing, and how this disproportionately affects low income communities and communities of color.
Should algorithms ever be used in a context where human bias is already rampant, such as in police departments? Why is it that the use of digital technologies to accomplish tasks in this age makes a process seem more “efficient” or “objective”? What are the problems with police using algorithms of which they do not fully understand the inner workings? Is the use of predictive policing algorithms ever justifiable?
How Cops Are Using Algorithms to Predict Crimes: How Cops Are Using Algorithms to Predict Crimes-
- 6 min
- TED
- 2020
Jamila Gordon, an AI activist and the CEO and founder of Lumachain, tells her story as a refugee from Ethiopia to illuminate the great strokes of luck that eventually brought her to her important position in the global tech industry. This makes the strong case for introducing AI into the workplace, as approaches using computer vision can lead to greater safety and machine learning can be applied to help those who may speak a language not dominant in that workplace or culture train and acclimate more effectively.
- TED
- 2020
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- 6 min
- TED
- 2020
How AI can help shatter barriers to equality
Jamila Gordon, an AI activist and the CEO and founder of Lumachain, tells her story as a refugee from Ethiopia to illuminate the great strokes of luck that eventually brought her to her important position in the global tech industry. This makes the strong case for introducing AI into the workplace, as approaches using computer vision can lead to greater safety and machine learning can be applied to help those who may speak a language not dominant in that workplace or culture train and acclimate more effectively.
Would constant computer vision surveillance of a workplace be ultimately positive or negative or both? How could it be ensured that machine learning algorithms were only used for positive forces in a workplace? What responsibility to large companies have to help those in less privileged countries access digital fluency?
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- 7 min
- The New Republic
- 2020
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
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- 7 min
- The New Republic
- 2020
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.
Should machines be trusted to handle and process the incredibly nuanced meaning of human language? How do different understandings of what languages and words mean and represent become harmful when a minority of people are deciding how to train NLP algorithms? How do tech monopolies prevent more diverse voices from entering this conversation?