Developing and Evaluating Fairness-Aware Machine Learning Methods; Ethical Responsibility and Accountability for Algorithmic Bias in Hiring

Introduction Machine learning systems have increasingly taken control of making high-stake decisions about people’s lives. This can include who advances in a hiring pipeline, who is released before trial, or whose face a security camera flag. The reason behind this mass adoption is the efficiency and apparent objectivity that machine learning systems bring. What gets left out is that this objectivity that is spoken of by organizations is often a fiction. When training a model using historical data, it is often shaped by the discrimination apparent within this data. So instead of correcting them, it encodes and automates these patterns. Amazon’s recruiting algorithm penalized resumes containing the word “women’s” as it had learned from a male-dominated workforce. HireVue’s video interviewing system was responsible for generating disparate outcomes across demographic groups and even faced EEOC charges in 2025. These are the inevitable outcomes that come from machine learning systems optimizing for accuracy with no questions raised about fairness. My thesis addresses this problem with two directions: A technical project proposing a framework for evaluating fairness-aware machine learning methods, and an STS project that uses Actor-Network Theory to analyze how ethical responsibility for discriminatory hiring algorithms is distributed and evaded. Technical Project The technical portion of my thesis proposes a systematic framework for comparing bias mitigation techniques across three pipeline stages, which are preprocessing, in-processing, and postprocessing. It includes the use of three datasets: The Adult Income dataset, a synthetic pretrial risk assessment dataset that will be constructed from public criminal justice statistics, and facial recognition benchmarks. The synthetic dataset is critical to the project as it allows control over what known bias patterns are explicitly implemented into the data and therefore the framework can test whether each method removed them, which is impossible with proprietary tools like COMPAS. The evaluation will use Python, scikit-learn, and IBM’s AI Fairness 360 toolkit to measure not only the predictive performance but also fairness metrics which will include demographic parity, equalized odds, and equalized opportunity. The expected result from the project is a practical and empirically grounded understanding of the tradeoffs that come from each type of method and in which domains each of them work best. STS Research Paper In my STS research, I applied Actor-Network Theory to discover and trace how responsibility for biased hiring algorithms is formed and deflected. ANT treats both human and non-human actors symmetrically, so executives, data scientists, training datasets, and legal frameworks are all actants that actively shape the outcomes; therefore blame cannot rest at any single node. Through case studies of Amazon’s recruiting tool, HireVue’s video system, and the 2024 Mobley v. Workday lawsuit, I have mapped how accountability spreads when no one actor has full control of the system. The employers will blame vendors, vendors claim they only built the tool, and regulators find civil rights laws ill-suited to algorithmic decision-making. Drawing from the works of O’Neil, Noble, and Eubanks, my research argues this diffusion is a structural feature of how these networks are formed and recommends three specific interventions: mandatory algorithmic impact assessments, enforceable third-party bias audits with the inclusion of public disclosure, and vendor liability reforms. Conclusion When put together, these two projects illustrate a core tension as the technical work demonstrates that fairness interventions are feasible, while the STS research shows that this technical feasibility will not matter if there are no accountability structures that require their use. The technical framework strengthens the argument that developers who create the algorithms and employers who use them have an obligation to act. The STS analysis details why these tools remain underused, as organizations have very little reason to adopt these fairness-aware methods when the alternative carries no real legal or reputational risk. Hiring is a primary domain where this often occurs in and automating discrimination at scale behind the façade of objectivity causes harm that is harder to detect and contest than its human equivalent. Addressing this issue requires both the technical capability of building fairer systems and the institutional will to require that organizations use them.