When Algorithms Codify Inequality

Author: Denis Avetisyan

New research reveals how machine learning systems used in higher education can inadvertently reinforce existing inequities throughout their entire lifecycle.

Early warning system success prediction probabilities demonstrate a clear distinction between risk levels, with a threshold of approximately 0.4 delineating high-risk scenarios from those considered medium risk at around 0.8.

Fairness audits of deployed institutional risk models demonstrate that percentile-based post-processing amplifies disparities and functions as institutional policy within the ASP-HEI cycle.

Despite growing reliance on machine learning to inform institutional decision-making, disparities in algorithmic outcomes often remain hidden within deployed systems. This paper, ‘Fairness Audits of Institutional Risk Models in Deployed ML Pipelines’, presents a replica-based audit of an Early Warning System at Centennial College, revealing systematic misallocation of support resources based on gender, age, and residency. Our analysis demonstrates that these inequities aren’t simply statistical artifacts, but are compounded throughout the pipeline-particularly via percentile-based post-processing-effectively formalizing existing institutional biases. How can rigorous, replicable auditing methodologies move beyond statistical fairness to address the underlying construct validity of these increasingly influential systems?

The Illusion of Proactive Support

Across higher education, institutions are increasingly adopting data-driven Early Warning Systems (EWS) as a proactive measure against student dropout. These systems utilize algorithms to analyze student data – encompassing academic performance, engagement metrics, and even financial aid status – with the stated goal of identifying at-risk individuals before they disengage. The premise is that early identification allows for targeted interventions, such as tutoring, counseling, or financial assistance, ultimately boosting student retention rates and fostering academic success. While presented as a supportive tool, the growing reliance on EWS signifies a shift towards data-informed decision-making at an institutional level, influencing not only student support services, but also broader strategic planning and resource allocation. The implementation of these systems reflects a desire to optimize student outcomes, but also raises questions regarding data privacy, algorithmic bias, and the potential for these tools to inadvertently reinforce existing inequalities within the higher education landscape.

Early Warning Systems, intended to bolster student success, frequently operate within a reinforcing cycle-termed the ASP-HEI Cycle-where institutional financial health and power dynamics overshadow genuine student wellbeing. This cycle prioritizes metrics like retention rates not as indicators of student flourishing, but as key performance indicators directly linked to funding and institutional prestige. Consequently, resources are often allocated to interventions targeting students deemed ‘at-risk’ based on easily quantifiable data, while systemic issues contributing to student struggles – such as inadequate access to mental health services or culturally insensitive pedagogy – receive less attention. The effect is a self-perpetuating system where institutions address the symptoms of student hardship to maintain positive metrics, rather than addressing the root causes, ultimately solidifying existing power structures and potentially exacerbating inequalities within the student body.

The predictive algorithms driving student retention systems are not neutral arbiters of risk; rather, the data used to train these models reflects and often amplifies existing institutional biases. A focus on financial stability subtly influences which student characteristics are flagged as indicative of potential dropout, potentially overlooking systemic barriers faced by specific groups. Evidence of this dynamic is revealed in completion rate disparities: statistically significant differences-with international students demonstrating an 85% completion rate compared to 67% for domestic students (p < 0.001)-suggest that the models may be misinterpreting or inadequately accounting for the unique challenges and resources available to these distinct student populations, effectively reinforcing pre-existing inequalities under the guise of objective prediction.

Data’s Echo of Existing Disparities

Early warning systems (EWS) utilized in higher education often rely on historical student data to predict academic outcomes; however, this training data frequently exhibits pre-existing disparities in completion rates among different demographic groups. Specifically, data reflects systemic inequalities in access, resources, and support that impact student success. Consequently, these systems are not evaluating students on a level playing field but rather learning from a dataset that already encodes societal biases. This means that observed completion rates are not necessarily indicative of a student’s potential for success, but rather a reflection of the barriers they may have faced – and the data used to train EWS doesn’t account for those barriers.

The composition of training data for Early Warning Systems (EWS) is not a coincidental byproduct of data collection, but is systematically shaped by the Academic Success-Higher Education Institution (ASP-HEI) cycle. This cycle incentivizes institutions to prioritize metrics directly linked to financial outcomes, such as student retention and graduation rates. Consequently, data collection efforts and the resulting training datasets often emphasize factors correlating with institutional financial health, rather than comprehensive indicators of student need or risk. This focus leads to datasets that reflect existing systemic biases and inequalities, which are then inadvertently learned and perpetuated by the EWS models.

Early warning systems (EWS) demonstrate a tendency to predict student dropout based on historically observed inequalities rather than actual risk factors. Analysis of false positive rates reveals this bias: female domestic students are flagged as at-risk at a rate of 32%, compared to 23% for male domestic students. Similarly, female international students experience a 26% false positive rate, while the rate for male international students is 18%. These statistically significant differences indicate that the models are disproportionately identifying female students as being at risk of dropping out, even when they are not, suggesting the models are learning and perpetuating existing systemic biases present in the training data.

The Illusion of Categorization

The application of percentile-based post-processing to categorize students into risk tiers – Low, Medium, and High – is not an objective assessment; it represents a deliberate methodological choice with inherent biases. This process involves ranking students based on predictive scores and assigning them to predefined categories based on percentile thresholds. Consequently, the resulting categorization is directly influenced by the distribution of scores within the student population and the specific percentile cutoffs established. These cutoffs are not empirically determined to represent meaningful differences in student performance or potential, but rather serve to create discrete, manageable groups for administrative purposes, inherently introducing a level of subjectivity into the risk assessment.

The Percentile-Based Post-Processing method facilitates the ASP-HEI Cycle by translating predictive scores into discrete risk categories – Low, Medium, and High – which are readily usable for institutional reporting and action. This categorization allows for efficient allocation of resources towards targeted interventions designed to improve student retention rates. Specifically, students assigned to the ‘High Risk’ tier become the focus of proactive support programs, such as mandatory advising or supplemental instruction, directly addressing institutional goals related to performance metrics and overall student success rates. The quantifiable nature of these risk tiers also enables institutions to track the effectiveness of interventions and demonstrate accountability to stakeholders.

The categorization of students into risk tiers via percentile thresholds prioritizes efficient resource allocation at the potential expense of individualized assessment. This approach, while streamlining intervention efforts, can exacerbate existing disparities; data indicates unsuccessful international students are 1.12 times more likely to be classified as ‘High Risk’ than their unsuccessful domestic counterparts. This disproportionate categorization suggests the system may not account for factors specific to international student experiences, potentially leading to misallocation of support services and reinforcing inequities in student outcomes. The focus on readily quantifiable percentile data, therefore, directly impacts equity by potentially obscuring nuanced individual circumstances.

Predicting Failure, Ignoring Need

A fundamental disconnect exists between the intention of predictive modeling and its practical application in student support services. The model itself flags students as being ‘at risk’ of dropping out – a prediction of future behavior – yet advisors routinely interpret this designation as an immediate indication of comprehensive support needs. This ‘Task Formulation Mismatch’ creates a situation where a probabilistic assessment is treated as a definitive statement of current deficiency, potentially leading to interventions that address perceived shortcomings rather than the underlying causes of academic struggle. Consequently, resources may be allocated based on risk scores rather than a holistic understanding of the student’s situation, inadvertently reinforcing existing inequalities and failing to effectively address the complexities of student wellbeing.

The analytical structure underpinning many higher education interventions, termed the ASP-HEI Cycle, frequently prioritizes easily quantifiable metrics – such as attendance or assignment completion – over a holistic assessment of student wellbeing. This emphasis on data points, while intended to facilitate early intervention, inadvertently frames student struggles as statistical anomalies rather than complex individual challenges. Consequently, support systems often become reactive and focused on addressing symptoms – improving scores or attendance – rather than proactively tackling the underlying causes of difficulty, like financial hardship, mental health concerns, or systemic barriers to access. This cycle reinforces a system where student need is reduced to a number, potentially leading to misdirected resources and a failure to truly support students facing genuine hardship, ultimately perpetuating inequalities within the educational landscape.

Interventions designed to support at-risk students often prove misdirected or insufficient, failing to address the underlying issues contributing to academic struggle and, instead, perpetuating existing inequalities. This inadequacy is further compounded by systemic biases evident in risk categorization; data reveals unsuccessful male students are flagged as ‘High Risk’ at a rate ten percentage points higher than their female counterparts, and younger students (aged 25 or under) are nearly twice as likely to receive this designation (94%) compared to older learners (36+ at 75%). These disparities suggest that predictive models, while intending to identify those needing assistance, may inadvertently amplify pre-existing disadvantages, creating a feedback loop where certain demographics are consistently flagged as problematic without receiving genuinely effective support tailored to their specific challenges.

The pursuit of algorithmic fairness often feels like rearranging deck chairs on the Titanic. This work on early warning systems highlights how even well-intentioned models can formalize existing inequities, particularly through seemingly innocuous post-processing steps. The amplification of disparities through percentile-based adjustments isn’t a bug; it’s a feature, effectively encoding institutional policy into automated systems. As Henri Poincaré observed, “Mathematics is the art of giving reasons.” But reason, devoid of critical examination of the underlying societal biases, simply provides a more efficient means of perpetuating them. The bug tracker, inevitably, will fill with complaints about a system that accurately reflects existing power structures. They don’t deploy – they let go.

What’s Next?

The predictable march continues. This work confirms what anyone who’s maintained a production model for more than a fiscal quarter already knows: ‘fairness’ interventions don’t solve problems, they become problems. The ASP-HEI cycle isn’t a bug; it’s a feature, meticulously encoding institutional priorities-and their inherent biases-into algorithmic form. They’ll call it ‘responsible AI’ and raise funding, naturally. The real question isn’t whether these models are fair, but who defines ‘fair’ and, more importantly, who benefits when the definition is operationalized.

Future work will inevitably focus on ‘better’ post-processing techniques, more granular fairness metrics, and increasingly complex adversarial training schemes. It’s a distraction. The core issue isn’t a technical one; it’s a political one. Until institutions acknowledge that these systems are policy, and subject them to the same scrutiny and accountability as any other form of governance, the ‘audits’ will remain elaborate performance reviews for a system designed to perpetuate itself.

One anticipates a surge in ‘explainable AI’ aimed at justifying these outcomes, rather than challenging them. It used to be a simple bash script, honestly. Now, it’s a multi-stage pipeline with layers of abstraction, and everyone pretends they don’t understand how it arrived at its conclusions. Tech debt is just emotional debt with commits, and the bill is coming due.

Original article: https://arxiv.org/pdf/2604.19468.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

The Illusion of Proactive Support

Data’s Echo of Existing Disparities

The Illusion of Categorization

Predicting Failure, Ignoring Need

What’s Next?

See also: