Models to predict success

Prompt: Imagine that you are running the University Admissions Office. A statistics professor has suggested that you could do a better job of picking students for the school if the admissions office used models to predict success and then just admit the students that score highest. Note: Humans can still be used to numerically code subjective judgments.

1. What do you think of this plan? What might models do better than expert admissions officers?

2. What problems do you anticipate? How would you solve them?

3. How can we ensure that the model maintains what makes the university unique?

Hint 1: Specify what measures of success the model is trying to predict for applicants. That is, what metric is the model trying to maximize.

Hint 2: Even if you are against using models, you still must base your write-up on this week’s readings (and not just discuss your own opinions)!

_the_utter_uselessness_of_job_interviews dawes_1979_ap_the_robust_beauty_of_improper_linear_models_in_decision_making dawes_corrigan_1974_pb_linear_models_in_decision_making hbr_2014_the_problem_with_using_personality_tests_for_hiring _kleinberg_ludwig_mullainathan_2016_hbr_a_guide_to_solving_social_problems_with_machine_learning

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