Machine Learning (82)
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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- 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
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- 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?
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- 5 min
- Venture Beat
- 2021
Relates the story of Google’s inspection of Margaret Mitchell’s account in the wake of Timnit Gebru’s firing from Google’s AI ethics division. With authorities in AI ethics clearly under fire, the Alphabet Worker’s Union aims to ensure that workers who can ensure ethical perspectives of AI development and deployment.
- Venture Beat
- 2021
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- 5 min
- Venture Beat
- 2021
Google targets AI ethics lead Margaret Mitchell after firing Timnit Gebru
Relates the story of Google’s inspection of Margaret Mitchell’s account in the wake of Timnit Gebru’s firing from Google’s AI ethics division. With authorities in AI ethics clearly under fire, the Alphabet Worker’s Union aims to ensure that workers who can ensure ethical perspectives of AI development and deployment.
How can bias in tech monopolies be mitigated? How can authorities on AI ethics be positioned in such a way that they cannot be fired when developers do not want to listen to them?
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- 7 min
- VentureBeat
- 2021
New research and code was released in early 2021 to demonstrate that the training data for Natural Language Processing algorithms is not as robust as it could be. The project, Robustness Gym, allows researchers and computer scientists to approach training data with more scrutiny, organizing this data and testing the results of preliminary runs through the algorithm to see what can be improved upon and how.
- VentureBeat
- 2021
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- 7 min
- VentureBeat
- 2021
Salesforce researchers release framework to test NLP model robustness
New research and code was released in early 2021 to demonstrate that the training data for Natural Language Processing algorithms is not as robust as it could be. The project, Robustness Gym, allows researchers and computer scientists to approach training data with more scrutiny, organizing this data and testing the results of preliminary runs through the algorithm to see what can be improved upon and how.
What does “robustness” in a natural language processing algorithm mean to you? Should machines always be taught to automatically associate certain words or terms? What are the consequences of large corporations not using the most robust training data for their NLP algorithms?
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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