AI (95)
Find narratives by ethical themes or by technologies.
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- 6 min
- Wired
- 2019
Spreading of harmful content through Youtube’s AI recommendation engine algorithm. AI helps create filter bubbles and echo chambers. Limited user agency to be exposed to certain content.
- Wired
- 2019
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- 6 min
- Wired
- 2019
The Toxic Potential of YouTube’s Feedback Loop
Spreading of harmful content through Youtube’s AI recommendation engine algorithm. AI helps create filter bubbles and echo chambers. Limited user agency to be exposed to certain content.
How much agency do we have over the content we are shown in our digital artifacts? Who decides this? How skeptical should we be of recommender systems?
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- 13 min
- Kinolab
- 2020
George Almore is an engineer working with a company which hopes to achieve singularity with robots, making their artificial intelligence one step above real humans. In doing this, he works with three prototypes: J1, J2, and J3, each one more advanced than the last. Simultaneously, he plans to upload his dead wife’s consciousness into the J3 robot in order to extend her life. The narrative begins with him explaining his goal to J3 as he has this robot go through taste and emotion tests. Eventually, J3 has evolved into a humanoid robot who takes on the traits of George’s wife, leaving the earlier two versions, who all have a sibling-like bond with each other, feeling neglected.
- Kinolab
- 2020
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- 13 min
- Kinolab
- 2020
Prototypes, Evolution, and Replacement with Robots
George Almore is an engineer working with a company which hopes to achieve singularity with robots, making their artificial intelligence one step above real humans. In doing this, he works with three prototypes: J1, J2, and J3, each one more advanced than the last. Simultaneously, he plans to upload his dead wife’s consciousness into the J3 robot in order to extend her life. The narrative begins with him explaining his goal to J3 as he has this robot go through taste and emotion tests. Eventually, J3 has evolved into a humanoid robot who takes on the traits of George’s wife, leaving the earlier two versions, who all have a sibling-like bond with each other, feeling neglected.
Are taste and emotion examples of necessary elements of creating advanced AI? If so, why? What good does having these abilities serve in terms of the AI’s relationship to the human world? Is it right to transfer consciousness or elements of consciousness from a deceased person into one or several AI? In the AI, how much is too much similarity to a pre-existing person? Can total similarity ever be achieved, and how? Can advanced AI feel negative human emotions and face mental health problems such as depression? Is it ethical to program AI to feel such emotions, knowing the risks associated with them, including bonding with former or flawed prototypes of itself? If an AI kills itself, does the onus fall on the machine or the human creator?
Archive: Test Runs-
- 5 min
- MIT Tech Review
- 2020
The Semantic Scholar is a new AI program which has been trained to read through scientific papers and provide a unique one sentence summary of the paper’s content. The AI has been trained with a large data set focused on learning how to process natural language and summarise it. The ultimate idea is to use technology to help learning and synthesis happen more quickly, especially for figure such as politicians.
- MIT Tech Review
- 2020
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- 5 min
- MIT Tech Review
- 2020
AI Summarisation
The Semantic Scholar is a new AI program which has been trained to read through scientific papers and provide a unique one sentence summary of the paper’s content. The AI has been trained with a large data set focused on learning how to process natural language and summarise it. The ultimate idea is to use technology to help learning and synthesis happen more quickly, especially for figure such as politicians.
How might this technology cause people to become lazy readers? How does this technology, like many other digital technologies, shorten attention spans? How can it be ensured that algorithms like this do not leave out critical information?
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- 7 min
- Wired
- 2020
After student members of the University of Miami Employee Student Alliance held a protest on campus, the University of Miami Police Department likely used facial recognition technology in conjunction with video surveillance cameras to track down nine students from the protest and summon them to a meeting with the dean. This incident provided a gateway into the discussion of fairness of facial recognition programs, and how students believe that they should not be deployed on college campuses.
- Wired
- 2020
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- 7 min
- Wired
- 2020
Facial Recognition Applications on College Campuses
After student members of the University of Miami Employee Student Alliance held a protest on campus, the University of Miami Police Department likely used facial recognition technology in conjunction with video surveillance cameras to track down nine students from the protest and summon them to a meeting with the dean. This incident provided a gateway into the discussion of fairness of facial recognition programs, and how students believe that they should not be deployed on college campuses.
How can facial recognition algorithms interfere with the right of people to protest? When it comes to facial recognition databases, are larger photo repositories better or worse? Does facial recognition and video surveillance have a place on college campuses? How does facial recognition and video surveillance embolden people in power in general?
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- 5 min
- Wired
- 2021
This narrative describes the recent AI Incident Database launched at the end of 2020, where companies report case studies in which applied machine learning algorithms did not function as intended or caused real-world harm. The goal is to operate in a sense similar to air travel safety report programs; with this database, technological developers can get a sense of how to make algorithms which are more safe and fair while having the incentive to take precautions to stay off the list.
- Wired
- 2021
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- 5 min
- Wired
- 2021
Don’t End Up on This Artificial Intelligence Hall of Shame
This narrative describes the recent AI Incident Database launched at the end of 2020, where companies report case studies in which applied machine learning algorithms did not function as intended or caused real-world harm. The goal is to operate in a sense similar to air travel safety report programs; with this database, technological developers can get a sense of how to make algorithms which are more safe and fair while having the incentive to take precautions to stay off the list.
What is your opinion on this method of accountability? Is there anything it does not take into account? Is it possible that some machine learning algorithms make mistakes that cannot even be detected by humans? How can this be avoided? How can the inner workings of machine learning algorithms be made more understandable and digestible by the general public?
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- 3 min
- Kinolab
- 2009
In a distant future after the “Water War” in which much of the natural environment was destroyed and water has become scarce, Asha works as a curator at a museum which displays the former splendor of nature on Earth. She receives a mysterious soil sample which, after digital analysis using a object recognition to take data from the soil, surprisingly contains water.
- Kinolab
- 2009
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- 3 min
- Kinolab
- 2009
Digital Environment Analysis
In a distant future after the “Water War” in which much of the natural environment was destroyed and water has become scarce, Asha works as a curator at a museum which displays the former splendor of nature on Earth. She receives a mysterious soil sample which, after digital analysis using a object recognition to take data from the soil, surprisingly contains water.
How can technology be used to gather data on certain environments and aspects of an ecosystem to help them reach their full potential? How should this technology be made accessible to communities all across the world?