DESCRIPTION

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QUESTION

According to one of our readings, how do some social network companies predict user age, and what reason was cited for this being a potential problem for older people?

QUESTION

On two or three occasions in class, we've noted that one should be cautious about claims from company X that n% of its workforce is women and minorities. There is an example relating specifically to this concerning Apple in one of our readings covered by a student presentation in discussion.  What was involved in this example?

QUESTION

Consider Table 1 of the "Good for Men, Good for Women, Bad for People" article. The 0.60 = 3/5 figure is a prospective probability. Find the numerical value of the corresponding retrospective version. Show your work.

QUESTION

We discussed employers using the university that an applicant had attended as a hiring factor. Say employer X tends to screen out applicants who are not from the Ivy League or similar schools. This may be disparate impact on underrepresented minority applicants. How might this be illegal under case law?

QUESTION

A point was made in class about a tweet on Twitter where somebody suggested that even if men and women of the same traits are treated equally badly, it still is unequal treatment. Explain.

QUESTION

What application of ML did we discuss in class in which most people would actually WANT their race, gender etc. to be used?