Algorithmic Bias

Test

Jugadas 1

Crear copia

Sobre esta actividad

Examples of types of algorithmic biases

Creada por

stellen

Estados Unidos

Descarga la versión para jugar en papel

Top 10 resultados

Todavía no hay resultados para este juego. ¡Sé el primero en aparecer en el ranking! Inicia sesión para identificarte.

Crea tu propio juego gratis desde nuestro creador de juegos

Compite contra tus amigos para ver quien consigue la mejor puntuación en esta actividad

Crear reto

Cultura general española

Ernest Serret

España

(100)

Responde las siguientes preguntas
Tipos de sintagmas (II)

idoia rubio

España

(945)

Actividades interactivas para reconocer los distintos tipos de sintagmas
Verbos irregulares en Inglés

Hélmer González Serrano

Colombia

(311)

Se presentan 10 preguntas, cada una con 4 opciones de respuesta. Seleccione la que considere correcta.
cuanto sabes de Futbol

hugo estay

Chile

(318)

este test esta basado en las diapositivas que pudimos ver con anterioridad, consta de 10 preguntas las cuales tienen alternativas para poder resolverlas.
Brawl stars preguntas

Carlos Andrés Prieto Rodríguez

Colombia

(164)

Completa todas las preguntas


Algorithmic BiasVersión en línea Examples of types of algorithmic biases por stellen 1 A hiring algorithm consistently ranks resumes from candidates who live in affluent neighborhoods higher than those from economically disadvantaged areas, regardless of skills or experience. a Training data bias b Feature selection bias c Measurement bias d 2 An AI hiring tool favors candidates from NYU over candidates from UNC-Chapel Hill for finance roles because it was trained on past hiring data where students were predominantly hired. a Training data bias b Feature selection bias c Measurement bias d 3 A video interview analysis tool misinterprets the facial expressions of darker-skinned candidates as showing lower enthusiasm compared to lighter-skinned candidates. a Training data bias b Feature selection bias c Measurement bias d Feedback 1 Correct answer: feature selection bias. The use of zip code as a feature introduces socioeconomic and potentially racial bias. 2 Correct answer: training data bias. The algorithm learned from historical data that reflect biased hiring practices. 3 Correct answer: measurement bias. The input (facial expression data) is not accurately measured across all races.

Algorithmic BiasVersión en línea

Examples of types of algorithmic biases

por stellen

A hiring algorithm consistently ranks resumes from candidates who live in affluent neighborhoods higher than those from economically disadvantaged areas, regardless of skills or experience.

Training data bias

Feature selection bias

Measurement bias

An AI hiring tool favors candidates from NYU over candidates from UNC-Chapel Hill for finance roles because it was trained on past hiring data where students were predominantly hired.

Training data bias

Feature selection bias

Measurement bias

A video interview analysis tool misinterprets the facial expressions of darker-skinned candidates as showing lower enthusiasm compared to lighter-skinned candidates.

Training data bias

Feature selection bias

Measurement bias

Feedback

Correct answer: feature selection bias. The use of zip code as a feature introduces socioeconomic and potentially racial bias.

Correct answer: training data bias. The algorithm learned from historical data that reflect biased hiring practices.

Correct answer: measurement bias. The input (facial expression data) is not accurately measured across all races.

Puedes integrar el juego en un LMS compatible con LTI 1.1 o LTI 1.3 como Canvas, Moodle, o Blackboard. De esta manera podrás guardar las puntuaciones automáticamente en el libro de calificaciones de esa plataforma.

Algorithmic Bias

Test

Sobre esta actividad

Creada por

Descarga la versión para jugar en papel

Top juegos

Test

Cultura general española

Test

Tipos de sintagmas (II)

Test

Verbos irregulares en Inglés

Test

cuanto sabes de Futbol

Test

Brawl stars preguntas

Algorithmic BiasVersión en línea

por stellen

A hiring algorithm consistently ranks resumes from candidates who live in affluent neighborhoods higher than those from economically disadvantaged areas, regardless of skills or experience.

An AI hiring tool favors candidates from NYU over candidates from UNC-Chapel Hill for finance roles because it was trained on past hiring data where students were predominantly hired.

A video interview analysis tool misinterprets the facial expressions of darker-skinned candidates as showing lower enthusiasm compared to lighter-skinned candidates.

Feedback

		%
	Anónimo	%
		%
		%