Machine Learning (84)
Find narratives by ethical themes or by technologies.
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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
- 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
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- 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?
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- 7 min
- Venture Beat
- 2021
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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- 7 min
- Venture Beat
- 2021
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.
Whose job is it to ameliorate the “privilege hazard”, and how should this be done? How should large data sets be analyzed to avoid bias and ensure fairness? How can large data aggregators such as Google be held accountable to new standards of scrutinizing data and introducing humanities perspectives in applications?
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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?