Browsing by Author "Van Busum, Kelly"

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    Interactive Mitigation of Biases in Machine Learning Models
    (2024-08) Van Busum, Kelly; Fang, Shiaofen; Mukhopadhyay, Snehasis; Xia, Yuni; Tuceryan, Mihran
    Bias and fairness issues in artificial intelligence algorithms are major concerns as people do not want to use AI software they cannot trust. This work uses college admissions data as a case study to develop methodology to define and detect bias, and then introduces a new method for interactive bias mitigation. Admissions data spanning six years was used to create machine learning-based predictive models to determine whether a given student would be directly admitted into the School of Science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of a student’s application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed predictive models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. Then, using a variety of bias and fairness metrics, we analyzed these predictive models to detect biases the models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his/her family to attend college. We found that high accuracy rates can mask underlying algorithmic bias towards these sensitive groups. Finally, we describe our method for bias mitigation which uses a combination of machine learning and user interaction. Because bias is intrinsically a subjective and context-dependent matter, it requires human input and feedback. Our approach allows the user to iteratively and incrementally adjust bias and fairness metrics to change the training dataset for an AI model to make the model more fair. This interactive bias mitigation approach was then used to successfully decrease the biases in three AI models in the context of undergraduate student admissions.
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    Project-Based Learning: IUPUI High-Impact Taxonomy
    (2023-04-27) Oesch-Minor, Deborah; Pierce, David; Hayes, Kelly; Mihci, Gurkan; Robertson, Nancy Marie; Stucky, Tom; Van Busum, Kelly; Westerhaus-Renfro, Charlotte
    Project-Based Learning [PBL] infuses content-rich readings, lectures, and instruction to support students as they learn by actively engaging in real-world and personally meaningful projects. PBL is a high-impact practice (HIP) that can be applied simultaneously when using other HIPS or pedagogical approaches (e.g., case study, capstone, research, study abroad, work-integrated learning, community-based learning, writing intensive course, ePortfolio). In PBL courses students identify real-world/authentic problems to explore and participate in sustained inquiry throughout the project. Students do not re-ceive information to memorize it; they learn because they have a real need to know something so they can use it to solve a problem or answer a question that matters to them. Students go through iterative cycles of posing real questions, finding resources, collecting data, interpreting information, and reporting findings. Student progress is supported through scaffolded activities, feedback loops with peers and faculty, and meeting benchmarks for progress. At key moments, students reflect on the process, what they have achieved, and make connections between the work they are completing and relevant course concepts.