Public Attitudes Toward Computer Algorithms
"...despite the growing presence of algorithms in many aspects of daily
life, a Pew Research Center survey of U.S. adults finds that the public
is frequently skeptical of these tools when used in various real-life
situations.
This skepticism spans several dimensions. At a broad level, 58% of
Americans feel that computer programs will always reflect some level of
human bias – although 40% think these programs can be designed in a way
that is bias-free. And in various contexts, the public worries that
these tools might violate privacy, fail to capture the nuance of complex
situations, or simply put the people they are evaluating in an unfair
situation. Public perceptions of algorithmic decision-making are also
often highly contextual. The survey shows that otherwise similar
technologies can be viewed with support or suspicion depending on the
circumstances or on the tasks they are assigned to do.
To gauge the opinions of everyday Americans on this relatively
complex and technical subject, the survey presented respondents with
four different scenarios in which computers make decisions by collecting
and analyzing large quantities of public and private data. Each of
these scenarios were based on real-world examples of algorithmic
decision-making... and included: a personal
finance score used to offer consumers deals or discounts; a criminal
risk assessment of people up for parole; an automated resume screening
program for job applicants; and a computer-based analysis of job
interviews. The survey also included questions about the content that
users are exposed to on social media platforms as a way to gauge
opinions of more consumer-facing algorithms."
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Showing posts with label algorithms. Show all posts
Showing posts with label algorithms. Show all posts
Busted by Big Data: Algorithms Could Make Cities Safer - But They Can't Protect Us From Policing's Worst Instincts
"By combining huge tranches of data and highly sophisticated algorithms, predictive policing appears to hold out the science-fiction promise that technology could, one day, spit out 100 percent accurate prophecies concerning the location of future crimes. The latest iteration of these analytics can’t ID a killer-to-be, but it can offer insight into what areas are potential sites for crime by drawing on information in everything from historical records to live social-media posts.
The technology, however, has raised tough questions about whether hidden biases in these systems will lead to even more over-policing of racialized and lower-income communities. In such cases, the result can turn into a feedback loop: the algorithms recommend a heightened police presence in response to elevated arrest rates that can be attributed to a heightened police presence.
Andrew Ferguson, who teaches law at the University of the District of Columbia and is the author of The Rise of Big Data Policing, goes further. He says that current predictive systems use social media and other deep wells of personal information to predict whether certain offenders may commit future crimes—an Orwellian scenario. Canadian governments and civilian oversight bodies, however, have done little to establish clear policies differentiating appropriate and inappropriate uses for these technologies. It is little wonder that critics are becoming increasingly concerned that police departments fitted out with big-data systems could use them to pre-emptively target members of the public. Can we really trust crime fighting to an algorithm?"
Related:
"By combining huge tranches of data and highly sophisticated algorithms, predictive policing appears to hold out the science-fiction promise that technology could, one day, spit out 100 percent accurate prophecies concerning the location of future crimes. The latest iteration of these analytics can’t ID a killer-to-be, but it can offer insight into what areas are potential sites for crime by drawing on information in everything from historical records to live social-media posts.
The technology, however, has raised tough questions about whether hidden biases in these systems will lead to even more over-policing of racialized and lower-income communities. In such cases, the result can turn into a feedback loop: the algorithms recommend a heightened police presence in response to elevated arrest rates that can be attributed to a heightened police presence.
Andrew Ferguson, who teaches law at the University of the District of Columbia and is the author of The Rise of Big Data Policing, goes further. He says that current predictive systems use social media and other deep wells of personal information to predict whether certain offenders may commit future crimes—an Orwellian scenario. Canadian governments and civilian oversight bodies, however, have done little to establish clear policies differentiating appropriate and inappropriate uses for these technologies. It is little wonder that critics are becoming increasingly concerned that police departments fitted out with big-data systems could use them to pre-emptively target members of the public. Can we really trust crime fighting to an algorithm?"
Related:
Code-Dependent: Pros and Cons of the Algorithmic Age
"Algorithms are aimed at optimizing everything. They can save lives, make things easier and conquer chaos. Still, experts worry they can also put too much control in the hands of corporations and governments, perpetuate bias, create filter bubbles, cut choices, creativity and serendipity, and could result in greater unemployment."
"Algorithms are aimed at optimizing everything. They can save lives, make things easier and conquer chaos. Still, experts worry they can also put too much control in the hands of corporations and governments, perpetuate bias, create filter bubbles, cut choices, creativity and serendipity, and could result in greater unemployment."
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