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Fairness Criteria, Exploring Fairness in Machine Learning

5,137 views

MIT OpenCourseWare

Video Language:

  • English

Caption Language:

  • {{captionLanguage | language_name}} ({{captionLanguage}})

Accent:

  • English (US)

Speech Time:

94%
  • 6:42 / 7:07

Speech Rate:

  • 172 wpm - Fast

Category:

  • Education

Tags :

Intro:

Video Vocabulary

scholar

/ˈskälər/

noun

An academic in a certain field.

specific

/spəˈsifik/

adjective noun

clearly defined or identified. medicine or remedy effective in treating particular disease.

assistant

/əˈsistənt/

adjective noun

(Person) helping another, usually as a job. person who ranks below senior person.

through

/THro͞o/

adjective adverb preposition

From one end or side of something to the other. From the beginning of something until the end. moving in one side and out of other side of.

reviewed

/rəˈvyo͞o/

verb

To study again to remind yourself about something.

produce
noun verb

agricultural products. To control the making a movie or television show.

previous

/ˈprēvēəs/

adjective

Existing or happening before the present time.

machine

/məˈSHēn/

noun verb

Piece of equipment used to do work. make or operate on with machine.

learning

/ˈlərniNG/

noun verb

acquisition of knowledge or skills through study. To get knowledge or skills by study or experience.

algorithm

/ˈalɡəˌriT͟Həm/

noun

process or set of rules to be followed in calculations or other problem-solving operations.

equalized

/ˈēkwəˌlīz/

verb

make same in quantity, size, or degree throughout place or group.

discussed

/dəˈskəs/

verb

To talk about seriously or in great detail.

protected

/prəˈtektəd/

adjective verb

preserved from harm. To defend someone or something from harm or danger.

outcomes

/ˈoutˌkəm/

noun other

way thing turns out. Some things that happen as a result, consequences.

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