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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

Concerning one particular thing or kind of thing. 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. Allowing you to pass between, or to.

reviewed

/rəˈvyo͞o/

verb

To formally examine something in the military.

produce
noun verb

agricultural products. To make something appear.

previous

/ˈprēvēəs/

adjective

Existing or happening before the present time.

machine

/məˈSHēn/

noun verb

mechanical apparatus. To shape, work on something, using a device.

learning

/ˈlərniNG/

noun verb

Act of getting knowledge. To get knowledge or skills by study or experience.

algorithm

/ˈalɡəˌriT͟Həm/

noun

A step-by-step problem-solving feature.

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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