Promotion of Human Values (138)
Find narratives by ethical themes or by technologies.
FILTERreset filters-
- 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
-
- 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?
-
- 5 min
- Kinolab
- 1993
Jurassic Park is an under-review theme park where innovator John Hammond has managed to use computational genomics to revive the dinosaurs. The park is managed by a complex security system, involving an internet of things which connects security cameras, other monitors, and defense systems to the computers in the control room. Computer programmer Dennis Nedry, under command of a briber, uses malware to hack the computer systems and steal dinosaur DNA, turning the park into a very hostile environment for the scientists once the safety mechanisms fail.
- Kinolab
- 1993
-
- 5 min
- Kinolab
- 1993
Systems Errors in Entertainment Areas
Jurassic Park is an under-review theme park where innovator John Hammond has managed to use computational genomics to revive the dinosaurs. The park is managed by a complex security system, involving an internet of things which connects security cameras, other monitors, and defense systems to the computers in the control room. Computer programmer Dennis Nedry, under command of a briber, uses malware to hack the computer systems and steal dinosaur DNA, turning the park into a very hostile environment for the scientists once the safety mechanisms fail.
How can workplaces be protected against hacks from someone who works within said workplace? Should safety systems be under the control of a small number of people or computers? Should volatile environments, such as nuclear power plants or dinosaur parks, be trusted with a security system involving a hackable internet of things? What are the alternatives? Is convenience worth the cost in this case?
Jurassic Park: Systems Offline-
- 5 min
- ZDNet
- 2020
In recent municipal elections in Brazil, the software and hardware of a machine learning technology provided by Oracle failed to properly do its job in counting the votes. This ultimately led to a delay in the results, as the AI had not been properly calibrated beforehand.
- ZDNet
- 2020
-
- 5 min
- ZDNet
- 2020
AI Failure in Elections
In recent municipal elections in Brazil, the software and hardware of a machine learning technology provided by Oracle failed to properly do its job in counting the votes. This ultimately led to a delay in the results, as the AI had not been properly calibrated beforehand.
Who had responsibility to fully test and calibrate this AI before it was used for an election? What sorts of more dire consequences could result from a failure of AI to properly count votes? What are the implications of an American tech monopoly providing this faulty technology to another country’s elections?
-
- 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
-
- 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?
-
- 5 min
- Vice
- 2020
Robot researches in Japan have recently begun to use robotic “monster wolves” to help control wildlife populations by keeping them out of human civilizations or agricultural areas. These robots are of interest to robot engineers who work in environmentalism because although the process of engineering a robot does not help the environment, the ultimate good accomplished by robots which help control wildlife populations may outweigh this cost.
- Vice
- 2020
-
- 5 min
- Vice
- 2020
Robotic Beasts, Wildlife Control, and Environmental Impact
Robot researches in Japan have recently begun to use robotic “monster wolves” to help control wildlife populations by keeping them out of human civilizations or agricultural areas. These robots are of interest to robot engineers who work in environmentalism because although the process of engineering a robot does not help the environment, the ultimate good accomplished by robots which help control wildlife populations may outweigh this cost.
What are all the ways, aside from those mentioned in the article, in which robots and robotics could be utilised in environmentalist and conservationist causes? How could robots meant to tell wildlife where not to travel be misused?
-
- 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
-
- 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?