Machine Learning (83)
Find narratives by ethical themes or by technologies.
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- 9 min
- Kinolab
- 2002
In the year 2054, the PreCrime police program is about to go national. At PreCrime, three clairvoyant humans known as “PreCogs” are able to forecast future murders by streaming audiovisual data which provides the surrounding details of the crime, including the names of the victims and perpetrators. Although there are no cameras, the implication is that anyone can be under constant surveillance by this program. Once the “algorithm” has gleaned enough data about the future crime, officers move out to stop the murder before it happens. In this narrative, the PreCrime program is audited, and the officers must explain the ethics and philosophies at play behind their systems. After captain John Anderton is accused of a future crime, he flees, and learns of “minority reports,” or instances of disagreement between the Precogs covered up by the department to make the justice system seem infallible.
- Kinolab
- 2002
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- 9 min
- Kinolab
- 2002
Trusting Machines and Variable Outcomes
In the year 2054, the PreCrime police program is about to go national. At PreCrime, three clairvoyant humans known as “PreCogs” are able to forecast future murders by streaming audiovisual data which provides the surrounding details of the crime, including the names of the victims and perpetrators. Although there are no cameras, the implication is that anyone can be under constant surveillance by this program. Once the “algorithm” has gleaned enough data about the future crime, officers move out to stop the murder before it happens. In this narrative, the PreCrime program is audited, and the officers must explain the ethics and philosophies at play behind their systems. After captain John Anderton is accused of a future crime, he flees, and learns of “minority reports,” or instances of disagreement between the Precogs covered up by the department to make the justice system seem infallible.
What are the problems with taking the results of computer algorithms as infallible or entirely objective? How are such systems prone to bias, especially when two different algorithms might make two different predictions? Is there any way that algorithms could possibly make the justice system more fair? How might humans inflect the results of a predictive crime algorithm in order to serve themselves? Does technology, especially an algorithm such as a crime predictor, need to be made more transparent to its users and the general public so that people do not trust it with a religious sort of fervor?
Minority Report: Predetermination Happens All The Time-
- 13 min
- Kinolab
- 2002
In the year 2054, the PreCrime police program is about to go national. At PreCrime, three clairvoyant humans known as “PreCogs” are able to forecast future murders by streaming audiovisual data which provides the surrounding details of the crime, including the names of the victims and perpetrators. Although there are no cameras, the implication is that anyone can be under constant surveillance by this program. Once the “algorithm” has gleaned enough data about the future crime, officers move out to stop the murder before it happens.
- Kinolab
- 2002
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- 13 min
- Kinolab
- 2002
Preventative Policing and Surveillance Information
In the year 2054, the PreCrime police program is about to go national. At PreCrime, three clairvoyant humans known as “PreCogs” are able to forecast future murders by streaming audiovisual data which provides the surrounding details of the crime, including the names of the victims and perpetrators. Although there are no cameras, the implication is that anyone can be under constant surveillance by this program. Once the “algorithm” has gleaned enough data about the future crime, officers move out to stop the murder before it happens.
How will predicted crime be prosecuted? Should predicted crime be prosecuted? How could technologies such as the ones shown here be affected for the worse by human bias? How would these devices make racist policing practices even worse? Would certain communities be targeted? Is there ever any justification for constant civil surveillance?
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- 14 min
- Kinolab
- 2014
In the midst of World War II, mathematics prodigy Alan Turing is hired by the British government to help decode Enigma, the code used by Germans in their encrypted messages. Turing builds an expensive machine meant to help decipher the code in a mathematical manner, but the lack of speedy results incites the anger of his fellow coders and the British government. After later being arrested for public indecency, Turing discusses with the officer the basis for the modern “Turing Test,” or how to tell if one is interacting with a human or a machine. Turing argues that although machines think differently than humans, it should still be considered a form of thinking. His work displayed in this film became a basis of the modern computer.
- Kinolab
- 2014
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- 14 min
- Kinolab
- 2014
Decryption and Machine Thinking
In the midst of World War II, mathematics prodigy Alan Turing is hired by the British government to help decode Enigma, the code used by Germans in their encrypted messages. Turing builds an expensive machine meant to help decipher the code in a mathematical manner, but the lack of speedy results incites the anger of his fellow coders and the British government. After later being arrested for public indecency, Turing discusses with the officer the basis for the modern “Turing Test,” or how to tell if one is interacting with a human or a machine. Turing argues that although machines think differently than humans, it should still be considered a form of thinking. His work displayed in this film became a basis of the modern computer.
How did codebreaking help launch computers? What was Alan Turing’s impact on computing, and on the outcome of WW2? How can digital technologies be used to turn the tides for the better in a war? Are computers in our age too advanced for codes to be secret for long, and is this a positive or a negative? How do machines think? Should a machines intelligence be judged by the same standards as human intelligence?
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- 12 min
- Kinolab
- 2016
“Hidden Figures” chronicles the journeys of Katherine Johnson (Taraji P. Henson), Dorothy Vaughan (Octavia Spencer), and Mary Jackson (Janelle Monáe), three black women who worked on the space missions at the Langley Research Center in Hampton, Virginia in 1961. All three women persist against segregation and abject racism as they climb the ladder and make important contributions to the space mission. While Katherine becomes the first black woman on Al Harrison’s Space Task Group, Mary Jackson pursues her dream of becoming an engineer at NASA by petitioning to take courses at an all white school, and Dorothy Vaughan attempts to learn the programming language Fortran in order to ensure that herself and fellow human computers are not replaced by the newest IBM 7090 computer.
- Kinolab
- 2016
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- 12 min
- Kinolab
- 2016
Hidden Figures Part II: Goals of Equity and Women of Color in the Workplace
“Hidden Figures” chronicles the journeys of Katherine Johnson (Taraji P. Henson), Dorothy Vaughan (Octavia Spencer), and Mary Jackson (Janelle Monáe), three black women who worked on the space missions at the Langley Research Center in Hampton, Virginia in 1961. All three women persist against segregation and abject racism as they climb the ladder and make important contributions to the space mission. While Katherine becomes the first black woman on Al Harrison’s Space Task Group, Mary Jackson pursues her dream of becoming an engineer at NASA by petitioning to take courses at an all white school, and Dorothy Vaughan attempts to learn the programming language Fortran in order to ensure that herself and fellow human computers are not replaced by the newest IBM 7090 computer.
How is the history of the oppression of Black people in America responsible for a lack of diversity in workplaces, including those involving science and technology in the present? What do technology companies in the current day need to consider in order to ensure that their workforce is diverse and equitable? What does the specific case of Dorothy being initially denied access to the Fortran book reveal about the past and present accessibility of minority groups to fluency in digital technologies? What needs to happen inside of and outside of the technology industry to ensure better opportunities for women of color in technology-focused workplaces? What role does implicit bias play in all of these considerations?
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- 13 min
- Kinolab
- 2016
“Hidden Figures” chronicles the journeys of Katherine Johnson (Taraji P. Henson), Dorothy Vaughan (Octavia Spencer), and Mary Jackson (Janelle Monáe), three black women who worked on the space missions at the Langley Research Center in Hampton, Virginia in 1961. All three women persist against segregation and abject racism as they climb the ladder and make important contributions to the space mission. While Katherine becomes the first black woman on Al Harrison’s Space Task Group, Mary Jackson pursues her dream of becoming an engineer at NASA by petitioning to take courses at an all white school, and Dorothy Vaughan attempts to learn the programming language Fortran in order to ensure that herself and fellow human computers are not replaced by the newest IBM 7090 computer.
- Kinolab
- 2016
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- 13 min
- Kinolab
- 2016
Hidden Figures Part I: Goals of Equity and Women of Color in the Workplace
“Hidden Figures” chronicles the journeys of Katherine Johnson (Taraji P. Henson), Dorothy Vaughan (Octavia Spencer), and Mary Jackson (Janelle Monáe), three black women who worked on the space missions at the Langley Research Center in Hampton, Virginia in 1961. All three women persist against segregation and abject racism as they climb the ladder and make important contributions to the space mission. While Katherine becomes the first black woman on Al Harrison’s Space Task Group, Mary Jackson pursues her dream of becoming an engineer at NASA by petitioning to take courses at an all white school, and Dorothy Vaughan attempts to learn the programming language Fortran in order to ensure that herself and fellow human computers are not replaced by the newest IBM 7090 computer.
How is the history of the oppression of Black people in America responsible for a lack of diversity in workplaces, including those involving science and technology in the present? What do technology companies in the current day need to consider in order to ensure that their workforce is diverse and equitable? What does the specific case of Dorothy being initially denied access to the Fortran book reveal about the past and present accessibility of minority groups to fluency in digital technologies? What needs to happen inside of and outside of the technology industry to ensure better opportunities for women of color in technology-focused workplaces?
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- 15 min
- Kinolab
- 2017
In a world in which the program Coach determines the pairing and duration of romantic matches, Frank and Amy managed to be matched more than once and eventually fall in love after failed matches with other people. After Frank breaks a promise to Amy by checking the expiry date that is automatically assigned to all relationships, they temporarily break up. After a reunion, they set out to discover the truth of their reality and the meaning of their match.
- Kinolab
- 2017
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- 15 min
- Kinolab
- 2017
Online Dating Algorithms
In a world in which the program Coach determines the pairing and duration of romantic matches, Frank and Amy managed to be matched more than once and eventually fall in love after failed matches with other people. After Frank breaks a promise to Amy by checking the expiry date that is automatically assigned to all relationships, they temporarily break up. After a reunion, they set out to discover the truth of their reality and the meaning of their match.
Should machine learning algorithms, even the most sophisticated ones, be trusted when it comes to deeply emotional matters like love? Can simulations and algorithms account for everything when it comes to a person’s experience of love? How could algorithmic bias which is present in real-life matching programs enter the virtual reality system shown here? How can advanced simulations be distinguished from reality? Has the digital age moved the dating experience firmly past the “old days” of falling in love, and should this be embraced?