Examples of using Algorithmic confounding in English and their translations into Indonesian
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Colloquial
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Ecclesiastic
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Computer
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Ecclesiastic
How does this compare to algorithmic confounding?
Algorithmic confounding is relatively unknown to social scientists, but it is a major concern among careful data scientists.
The dynamic nature of algorithmic confounding is one form of system drift.
The ways that the goals of system designers can introduce patterns into data is called algorithmic confounding.
System drift is closely related to a problem called algorithmic confounding, which I will cover in section 2.3.8.
However, the magnitude of transitivity in the Facebooksocial graph is partially driven by algorithmic confounding.
Algorithmic confounding is relatively unknown to social scientists, but it is a major concern among careful data scientists.
System drift is closely related to problem called algorithmic confounding to which we now turn.
In this previous example, algorithmic confounding produced a quirky result that a careful researcher might detect and investigate further.
Evaluate these systems in terms of issues of scientific value, algorithmic confounding(see Chapter 2), and ethics.
Algorithmic confounding means that we should be cautious about any claim for human behavior that comes from a single digital system, no matter how big.
Evaluate these systems in terms of issues of scientific value, algorithmic confounding(see Chapter 2), and ethics.
Unfortunately, dealing with algorithmic confounding is particularly difficult because many features of online systems are proprietary, poorly documented, and constantly changing.
And, unlike some of the other problems with digital traces, algorithmic confounding is largely invisible.
Unfortunately, dealing with algorithmic confounding is particularly difficult because many features of online systems are proprietary, poorly documented, and constantly changing.
And, unlike some of the other problems with digital traces, algorithmic confounding is largely invisible.
Unfortunately, dealing with algorithmic confounding is particularly difficult because many features of online systems are proprietary, poorly documented, and constantly changing.
The ways that the goals of system designers canintroduce patterns into data is called algorithmic confounding.
However, there is an even trickier version of algorithmic confounding that occurs when designers of online systems are aware of social theories and then bake these theories into the working of their systems.
And, unlike some of the other problems with digital traces, algorithmic confounding is largely invisible.
Further, as I will describe later in the chapter, the behavior captured in big data sources is sometimes impacted by the goals of platform owners,an issue I will call algorithmic confounding.
A relatively simple example of algorithmic confounding is the fact that on Facebook there are an anomalously high number of users with approximately 20 friends, as was discovered by Johan Ugander and colleagues(2011).
However, the magnitude of transitivity in the Facebook social graph is partially driven by algorithmic confounding.
More pernicious than this previous example where algorithmic confounding produced a quirky result that a careful researchers might investigate further, there is an even trickier version of algorithmic confounding that occurs when designers of online systems are aware of social theories and then bake these theories into the working of their systems.
However, the magnitude of transitivity in the Facebook social graph is partially driven by algorithmic confounding.
Further, as I will describe more below, these data sources are sometimes impacted by the goals of platform owners,a problem called algorithmic confounding(described more below).
The second important caveat about Google Flu Trends is that its ability to predict the CDC flu data was prone to short-term failure andlong-term decay because of drift and algorithmic confounding.
The second important caveat about Google Flu Trends is that its ability to predict the CDC flu data was prone to short-term failure andlong-term decay because of drift and algorithmic confounding.
The second important caveat about Google Flu Trends is that its ability to predict the CDC flu data was prone to short-term failure andlong-term decay because of drift and algorithmic confounding.