Towards Data-Driven Affirmative Action Policies under Uncertainty

• URL: http://arxiv.org/abs/2007.01202v1
• Date: Thu, 2 Jul 2020 15:37:16 GMT
• Title: Towards Data-Driven Affirmative Action Policies under Uncertainty
• Authors: Corinna Hertweck, Carlos Castillo, Michael Mathioudakis
• Abstract summary: We consider affirmative action policies that seek to increase the number of admitted applicants from underrepresented groups. Since such a policy has to be announced before the start of the application period, there is uncertainty about the score distribution of the students applying to each program. We explore the possibility of using a predictive model trained on historical data to help optimize the parameters of such policies.
• Score: 3.9293125023197595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study university admissions under a centralized system that uses grades and standardized test scores to match applicants to university programs. We consider affirmative action policies that seek to increase the number of admitted applicants from underrepresented groups. Since such a policy has to be announced before the start of the application period, there is uncertainty about the score distribution of the students applying to each program. This poses a difficult challenge for policy-makers. We explore the possibility of using a predictive model trained on historical data to help optimize the parameters of such policies.

Related papers

• Algorithms for College Admissions Decision Support: Impacts of Policy Change and Inherent Variability [18.289154814012996]
  We show that removing race data from a developed applicant ranking algorithm reduces the diversity of the top-ranked pool without meaningfully increasing the academic merit of that pool. We measure the impact of policy change on individuals by comparing the arbitrariness in applicant rank attributable to policy change to the arbitrariness attributable to randomness.
  arXiv  Detail & Related papers  (2024-06-24T14:59:30Z)
• Conformal Off-Policy Evaluation in Markov Decision Processes [53.786439742572995]
  Reinforcement Learning aims at identifying and evaluating efficient control policies from data. Most methods for this learning task, referred to as Off-Policy Evaluation (OPE), do not come with accuracy and certainty guarantees. We present a novel OPE method based on Conformal Prediction that outputs an interval containing the true reward of the target policy with a prescribed level of certainty.
  arXiv  Detail & Related papers  (2023-04-05T16:45:11Z)
• Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions [21.70450099249114]
  College admissions offices have historically relied on standardized test scores to organize large applicant pools into viable subsets for review. We explore a machine learning-based approach to replace the role of standardized tests in subset generation. We find that a prediction model trained on past admission data outperforms an SAT-based model and matches the demographic composition of the last admitted class.
  arXiv  Detail & Related papers  (2023-02-07T17:14:26Z)
• Counterfactual Learning with General Data-generating Policies [3.441021278275805]
  We develop an OPE method for a class of full support and deficient support logging policies in contextual-bandit settings. We prove that our method's prediction converges in probability to the true performance of a counterfactual policy as the sample size increases.
  arXiv  Detail & Related papers  (2022-12-04T21:07:46Z)
• Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation [60.71312668265873]
  We develop a method to balance the need for personalization with confident predictions. We show that our method can be used to form accurate predictions of heterogeneous treatment effects.
  arXiv  Detail & Related papers  (2021-11-28T23:19:12Z)
• Sayer: Using Implicit Feedback to Optimize System Policies [63.992191765269396]
  We develop a methodology that leverages implicit feedback to evaluate and train new system policies. Sayer builds on two ideas from reinforcement learning to leverage data collected by an existing policy. We show that Sayer can evaluate arbitrary policies accurately, and train new policies that outperform the production policies.
  arXiv  Detail & Related papers  (2021-10-28T04:16:56Z)
• Supervised Off-Policy Ranking [145.3039527243585]
  Off-policy evaluation (OPE) leverages data generated by other policies to evaluate a target policy. We propose supervised off-policy ranking that learns a policy scoring model by correctly ranking training policies with known performance. Our method outperforms strong baseline OPE methods in terms of both rank correlation and performance gap between the truly best and the best of the ranked top three policies.
  arXiv  Detail & Related papers  (2021-07-03T07:01:23Z)
• Offline Policy Selection under Uncertainty [113.57441913299868]
  We consider offline policy selection as learning preferences over a set of policy prospects given a fixed experience dataset. Access to the full distribution over one's belief of the policy value enables more flexible selection algorithms under a wider range of downstream evaluation metrics. We show how BayesDICE may be used to rank policies with respect to any arbitrary downstream policy selection metric.
  arXiv  Detail & Related papers  (2020-12-12T23:09:21Z)
• Intersectional Affirmative Action Policies for Top-k Candidates Selection [3.4961413413444817]
  We study the problem of selecting the top-k candidates from a pool of applicants, where each candidate is associated with a score indicating his/her aptitude. We consider a situation in which some groups of candidates experience historical and present disadvantage that makes their chances of being accepted much lower than other groups. We propose two algorithms to solve this problem, analyze them, and evaluate them experimentally using a dataset of university application scores and admissions to bachelor degrees in an OECD country.
  arXiv  Detail & Related papers  (2020-07-29T12:27:18Z)
• Distributionally Robust Batch Contextual Bandits [20.667213458836734]
  Policy learning using historical observational data is an important problem that has found widespread applications. Existing literature rests on the crucial assumption that the future environment where the learned policy will be deployed is the same as the past environment. In this paper, we lift this assumption and aim to learn a distributionally robust policy with incomplete observational data.
  arXiv  Detail & Related papers  (2020-06-10T03:11:40Z)
• Policy Evaluation Networks [50.53250641051648]
  We introduce a scalable, differentiable fingerprinting mechanism that retains essential policy information in a concise embedding. Our empirical results demonstrate that combining these three elements can produce policies that outperform those that generated the training data.
  arXiv  Detail & Related papers  (2020-02-26T23:00:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.