Institutional Scholarship

Browsing by Author "Friedler, Sorelle"

Browsing by Author "Friedler, Sorelle"

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  • Roth, Derek (2018)
    Fairness, as it applies to algorithms, implies that the decisions made by an algorithm are being made such that there is no discrimination against individuals or groups in labeled data sets. In this paper, I give a summary ...
  • Tionney, Nix (2017)
    In fairness-aware data mining, discrimination discovery refers to determining if social discrimination against certain individuals or groups of individuals exists in labeled data sets or in learned models. In this thesis, ...
  • Nicholas, Gareth (2020)
    This thesis outlines the methods used in machine learning to generate models which are effective on a variety of tasks. We begin with a quick overview of the field of machine learning, covering the topics necessary to ...
  • Susai, Silvia (2020)
    Recent advances in deep learning have led to the development of state-of-the-art models with remarkable accuracy; however, previous work has shown that these results incur a high environmental cost due to their significant ...
  • Ayad, Yasmine (2019)
    For my thesis, I analyzed the COMPAS recidivism prediction tool made by Equivant which aims to see how likely a defendant charged with a crime will re-offending given a score from 1-10 where 1 indicating lowest risk and ...
  • Falk, Casey (2016)
    In this era of self-driving cars, smart watches, and voice-commanded speakers, machine learning is ubiquitous. Recently, deep learning has shown impressive success in solving many machine learning problems related to image ...
  • Smith, Brandon (2016)
    In recent years, deep neural network models have proven to be incredibly accurate on many classification benchmarks. Due to this high accuracy, many non-technical fields are interested in using these models to assist in ...
  • Hamilton, Evan (2017)
    Benchmarking fairness aware machine learning.
  • Moll, Karl (2014)
    Information about interactions between human actors, and the attributes about the actors in the networks, has become increasingly abundant in computer systems over the last decade. Multidimensional social networks are an ...
  • Feldman, Michael (2015)
    Machine learning algorithms called classifiers make discrete predictions about new data by training on old data. These predictions may be hiring or not hiring, good or bad credit, and so on. The training data may contain ...
  • Levin, Harry (2014)
    Every ten years, when states are forced to redraw their congressional districts, the process is intensely partisan, and the outcome is rarely fair and democratic. In the last few decades, the growing capabilities of computers ...
  • Raccuglia, Paul (2014)
    We present an exploration of data mining and machine learning techniques applied to a materials science dataset, with the goal of improving a lab's efficiency when running experiments. The primary product of our work is ...
  • Slack, Dylan (2019)
    Reinforcement Learning is concerned with developing machine learning approaches to answer the question: "What should I do?" Transfer Learning attempts to use previously trained Reinforcement Learning models to achieve ...
  • Chang, Kyu Hyun (2017)
    In contrast to traditional supervised machine learning that takes a set of labeled data and builds a model that best fits the given data, active learning selects instances from which it will learn. In a typical setting, ...
  • Beilinson, Hannah (2020)
    My thesis focuses on strategies to analyze fairness in information spread in social networks. Building off the field of influence maximization, I examine how the spread of information in a social network advantages some ...
  • Cueto, Paulina (2014)
    Cis-Regulatory Modules (CRMs) are the portion of DNA that initiates gene expression. Gene expression is the process through which the body turns DNA into functions and cells within an organism. In this paper I build upon ...
  • Lee, Steve (2021)
    At the bachelor's level, female students, Black students, and Indigenous students pursue computer science degrees at disproportionately lower rates. For example, in recent years, approximately 20% of bachelors degrees in ...
  • Byars, Monique (2021)
    The goal of this literature review is to highlight the importance of inclusivity in auditing algorithms. Machine Learning (ML) models affect many aspects of our lives such as providing us with relevant ads or predicting ...
  • Rybeck, Gabriel (2016)
    Rapid advancements in the use of big data to make automated decisions may result in indirect discrimination. For example, Larson et al. (2015) find that the Princeton Review charges different prices by zip code resulting ...
  • Marx, Charles (2020)
    Methods for measuring fairness in machine learning often operate by quantifying the relationship between some protected feature (e.g., race or gender) and the predictions of a model. When we are interested in understanding ...

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