NIST and Facial Recognition: Spanish C1
A C1 Spanish reading on NIST's 2019 evaluation of demographic effects in facial-recognition algorithms.
Lo que midió NIST sobre el reconocimiento facial
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About this article
What you'll learn
C1 vocabulary for algorithm evaluation, evidence, error types, and conditional questions in Spanish
Daniel reads NISTIR 8280 and learns to distinguish measured results, false-positive and false-negative errors, and unproven causes.
After reading
Article learning block
Phrases, words, grammar, and quick practice from this text.
Phrases for training
Add useful phrases to SRS or mark them as already known.
un resultado medido
newa measured result
It is a result obtained in an evaluation, not a guess about its cause.
Useful for separating study data from interpretation.
una explicación posible
newa possible explanation
It is an explanation that may be true but has not yet been proved by the study.
Useful for discussing causes cautiously.
la verificación uno a uno
newone-to-one verification
It compares two photographs to decide whether they show the same person.
Useful for distinguishing verification from database search.
una relación de causa y efecto
newa cause-and-effect relationship
It is a relationship in which one event causes another.
Useful for not confusing a possible cause with a proven link.
Key words
Vocabulary that carries the school-day topic.
Word: sesgo
sesgo
bias
Sesgo means a systematic tendency that can affect a result.
It is a key word in discussions of algorithmic fairness.
Words: falso positivo
falso positivo
false positive
It is an error in which a system associates photographs of two different people.
Useful for naming one error type in the text precisely.
Words: falso negativo
falso negativo
false negative
It is an error in which a system fails to associate two photographs of the same person.
Useful for distinguishing it from a false positive.
Word: diferenciales
diferenciales
measured differences between groups
In the report, this refers to measured differences between groups, for example in score distributions and error rates.
Useful for understanding the report's main finding.
Word: precisos
precisos
more accurate
Here, preciso describes an algorithm that makes fewer errors.
Useful for comparing results from different algorithms.
Word: entrenamiento
entrenamiento
training
Entrenamiento is the process in which an algorithm learns from data.
Useful for discussing data used to train an algorithm.
Grammar in context
Patterns from the text worth noticing after reading.
Grammar: antes de + infinitive
Antes de debatir sobre "sesgo"
Before debating bias
After antes de, the infinitive debatir appears. This construction shows that one action happens before another.
Useful for linking two actions in time.
Grammar: que sí corresponden
fotografías que sí corresponden a la misma persona
that really correspond to the same person
The clause que sí corresponden a la misma persona describes the noun fotografías. Sí emphasises that the photographs really belong to the same person.
Useful for understanding descriptive relative clauses.
Grammar: mostraron
mostraron tasas de falsos positivos reducidas
showed reduced false-positive rates
Mostraron is the preterite form for ellos or ellas. Here it agrees with algunos algoritmos.
Useful for reporting a completed research result.
Grammar: no intentó
no intentó explicar
did not try to explain
Intentó is the preterite form for él, ella, or usted. Here NIST is the subject, and no negates the action.
Useful for reporting precisely what a study did not do.
Grammar: te preocuparía
¿Para qué uso te preocuparía más un falso positivo?
would concern you more
Preocuparía is a Spanish conditional form. It asks a hypothetical question about a possible use.
Useful for asking cautious questions about possible consequences.
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