The phrase "are vulnerable to error" is correct and usable in written English.
It can be used when discussing the susceptibility of a system, process, or individual to making mistakes or experiencing inaccuracies.
Example: "Despite the advanced technology, the algorithms are vulnerable to error, leading to occasional miscalculations."
Alternatives: "prone to mistakes" or "susceptible to inaccuracies."
Instead of relying on election judges who are vulnerable to error or political pressures, the results are transmitted on a secure line to the state capital and then to a national center in Brasília.
Estimates of items such as depreciation, provisions and goodwill are vulnerable to error.
Studies of Tourette syndrome are vulnerable to error because tics vary in intensity and expression, are often intermittent, and are not always recognized by clinicians, patients, family members, friends or teachers; approximately 20% of persons with Tourette syndrome do not recognize that they have tics.
We have a prevailing disposition to use heuristics, and while they work well most of the time, they are vulnerable to error.
Many methods are available for the assessment of T1, but the faster methods (progressive nutation) are vulnerable to error due to radio frequency field inhomogeneity and mis-set pulses (Gowland and Stevenson, 2003; Parker and Padhani, 2003).
There are relatively few studies validating anthropometric measures in older and obese people; these and other confounders make anthropometric measures vulnerable to error and questionable for individual use [ 14]. > Anthropometric measures are vulnerable to error and not recommended for routine use in the diagnosis of sarcopenia.
Additionally, many in vitro studies in this field use ultrasound configurations that are vulnerable to errors during calibration and use.
The genetic "hotspots" where recombination is most likely to take place are also linked to inherited diseases, since these regions are vulnerable to errors in the genetic code.
While standard machine learning algorithms are robust against input data with errors from random distributions, it turns out that they are vulnerable to errors that are strategically chosen by an adversary.
In the processes of imprint erasure, establishment and maintenance are vulnerable to errors.
Since only a few points are used in the non-linear regression and the location of core pith was estimated rather than observed, the age scenarios are vulnerable to errors and variations in input data (midpoint vs. other estimates; 1 vs. 2 sigma ranges).
