arXiv subjects

Rene F. Kizilcec

Publications and source records attributed to Rene F. Kizilcec.

24 records · Page 2Linked to original sources

arXiv2024

Reflection on Purpose Changes Students' Academic Interests: A Scalable Intervention in an Online Course Catalog

College students routinely use online course catalogs to explore a variety of academic offerings. Course catalogs may therefore be an effective place to encourage reflection on academic choices and interests. To test this, we embedded a psychological intervention in an online course catalog to encourage students to reflect on their purpose during course exploration. Results of a randomized field experiment with over 4,000 students at a large U.S. university show that a purpose intervention increased students' cognitive engagement in describing their interests, but reduced search activities. Students became more interested in courses related to creative arts and social change, but less in computer and data science. The findings demonstrate the malleability of students' interests during course exploration and suggest practical strategies to support purpose reflection and guide students toward deliberate exploration of their interests in higher education.

cs.CY↗

arXiv2024

Cultural Bias and Cultural Alignment of Large Language Models

Culture fundamentally shapes people's reasoning, behavior, and communication. As people increasingly use generative artificial intelligence (AI) to expedite and automate personal and professional tasks, cultural values embedded in AI models may bias people's authentic expression and contribute to the dominance of certain cultures. We conduct a disaggregated evaluation of cultural bias for five widely used large language models (OpenAI's GPT-4o/4-turbo/4/3.5-turbo/3) by comparing the models' responses to nationally representative survey data. All models exhibit cultural values resembling English-speaking and Protestant European countries. We test cultural prompting as a control strategy to increase cultural alignment for each country/territory. For recent models (GPT-4, 4-turbo, 4o), this improves the cultural alignment of the models' output for 71-81% of countries and territories. We suggest using cultural prompting and ongoing evaluation to reduce cultural bias in the output of generative AI.

cs.CL↗

arXiv2024

Algorithms for College Admissions Decision Support: Impacts of Policy Change and Inherent Variability

Each year, selective American colleges sort through tens of thousands of applications to identify a first-year class that displays both academic merit and diversity. In the 2023-2024 admissions cycle, these colleges faced unprecedented challenges. First, the number of applications has been steadily growing. Second, test-optional policies that have remained in place since the COVID-19 pandemic limit access to key information historically predictive of academic success. Most recently, longstanding debates over affirmative action culminated in the Supreme Court banning race-conscious admissions. Colleges have explored machine learning (ML) models to address the issues of scale and missing test scores, often via ranking algorithms intended to focus on 'top' applicants. However, the Court's ruling will force changes to these models, which were able to consider race as a factor in ranking. There is currently a poor understanding of how these mandated changes will shape applicant ranking algorithms, and, by extension, admitted classes. We seek to address this by quantifying the impact of different admission policies on the applications prioritized for review. 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 contextualize this impact by showing that excluding data on applicant race has a greater impact than excluding other potentially informative variables like intended majors. Finally, 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. We find that any given policy has a high degree of arbitrariness and that removing race data from the ranking algorithm increases arbitrariness in outcomes for most applicants.

cs.CY↗

arXiv2023

Augmenting Holistic Review in University Admission using Natural Language Processing for Essays and Recommendation Letters

University admission at many highly selective institutions uses a holistic review process, where all aspects of the application, including protected attributes (e.g., race, gender), grades, essays, and recommendation letters are considered, to compose an excellent and diverse class. In this study, we empirically evaluate how influential protected attributes are for predicting admission decisions using a machine learning (ML) model, and in how far textual information (e.g., personal essay, teacher recommendation) may substitute for the loss of protected attributes in the model. Using data from 14,915 applicants to an undergraduate admission office at a selective U.S. institution in the 2022-2023 cycle, we find that the exclusion of protected attributes from the ML model leads to substantially reduced admission-prediction performance. The inclusion of textual information via both a TF-IDF representation and a Latent Dirichlet allocation (LDA) model partially restores model performance, but does not appear to provide a full substitute for admitting a similarly diverse class. In particular, while the text helps with gender diversity, the proportion of URM applicants is severely impacted by the exclusion of protected attributes, and the inclusion of new attributes generated from the textual information does not recover this performance loss.

cs.CL↗

arXiv2021

On the limits of algorithmic prediction across the globe

The impact of predictive algorithms on people's lives and livelihoods has been noted in medicine, criminal justice, finance, hiring and admissions. Most of these algorithms are developed using data and human capital from highly developed nations. We tested how well predictive models of human behavior trained in a developed country generalize to people in less developed countries by modeling global variation in 200 predictors of academic achievement on nationally representative student data for 65 countries. Here we show that state-of-the-art machine learning models trained on data from the United States can predict achievement with high accuracy and generalize to other developed countries with comparable accuracy. However, accuracy drops linearly with national development due to global variation in the importance of different achievement predictors, providing a useful heuristic for policymakers. Training the same model on national data yields high accuracy in every country, which highlights the value of local data collection.

cs.LG↗

arXiv2021

Artificial intelligence in communication impacts language and social relationships

Artificial intelligence (AI) is now widely used to facilitate social interaction, but its impact on social relationships and communication is not well understood. We study the social consequences of one of the most pervasive AI applications: algorithmic response suggestions ("smart replies"). Two randomized experiments (n = 1036) provide evidence that a commercially-deployed AI changes how people interact with and perceive one another in pro-social and anti-social ways. We find that using algorithmic responses increases communication efficiency, use of positive emotional language, and positive evaluations by communication partners. However, consistent with common assumptions about the negative implications of AI, people are evaluated more negatively if they are suspected to be using algorithmic responses. Thus, even though AI can increase communication efficiency and improve interpersonal perceptions, it risks changing users' language production and continues to be viewed negatively.

cs.HC↗