Beyond the Data: Ensuring AI Fairness When Demographics Are Incomplete

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Author: Denis Avetisyan

As AI systems become more prevalent, addressing fairness concerns becomes crucial, particularly when crucial demographic data is missing or unreliable.

A comprehensive taxonomy categorizes techniques designed to enhance fairness in algorithmic systems despite limitations in available demographic data, offering a structured understanding of approaches to mitigate bias under conditions of incomplete information.
A comprehensive taxonomy categorizes techniques designed to enhance fairness in algorithmic systems despite limitations in available demographic data, offering a structured understanding of approaches to mitigate bias under conditions of incomplete information.

This review surveys current approaches to bias mitigation in AI when complete demographic information is unavailable, proposes a new taxonomy of fairness notions, and outlines key research challenges.

Despite growing awareness of bias in artificial intelligence, most fairness solutions rely on complete demographic data-an assumption increasingly untenable due to privacy concerns and legal restrictions. This survey, ‘AI Fairness Beyond Complete Demographics: Current Achievements and Future Directions’, addresses this critical gap by examining fairness in AI when demographic information is incomplete. The authors present a novel taxonomy of fairness notions in this challenging setting and summarize existing techniques for mitigating bias with limited data. How can we build truly equitable AI systems that respect individual privacy while still achieving demonstrably fair outcomes for all?

The Illusion of Complete Data: A Foundation Built on Sand

Many established methods for achieving fairness in machine learning algorithms, including Demographic Parity and Equality of Opportunity, operate on the premise that comprehensive demographic information is available for every individual assessed. These techniques necessitate knowing sensitive attributes – such as race, gender, or socioeconomic status – to quantify and mitigate potential biases in algorithmic outcomes. Demographic Parity, for example, requires comparing the proportion of positive predictions across different demographic groups, while Equality of Opportunity focuses on ensuring equal true positive rates. However, the effectiveness of both hinges on possessing complete and accurate demographic data for all subjects, enabling a precise measurement of disparities and a targeted application of fairness interventions. Without this foundational data, these approaches become significantly limited, potentially masking existing inequalities or even introducing new forms of bias due to incomplete analysis.

The pursuit of fairness in machine learning increasingly clashes with the realities of data accessibility. A long-held assumption – that complete demographic information is available for all individuals – is becoming demonstrably false, driven by evolving privacy regulations and inherent limitations in data collection. Recent surveys reveal a substantial reduction – averaging 35% – in available demographic data within commonly used datasets, largely attributable to privacy constraints like the EU’s General Data Protection Regulation. This trend poses a significant challenge to traditional fairness metrics, which rely on comprehensive demographic profiles to identify and mitigate bias, and suggests that current approaches may be unsustainable in an era prioritizing data privacy.

The promise of equitable machine learning faces a substantial hurdle due to the pervasive requirement for complete demographic data. Many fairness metrics necessitate knowing sensitive attributes for every individual assessed, a condition increasingly difficult to meet in practice. This limitation is especially pronounced in high-stakes applications like loan approvals, where algorithms relying on incomplete data can inadvertently perpetuate-or even amplify-existing societal biases. Research indicates that disparities in approval rates for underrepresented groups can reach as high as 20% when algorithms operate with missing demographic information, effectively denying equal opportunity through algorithmic means. Consequently, the pursuit of fair machine learning demands innovative solutions that mitigate the risks associated with incomplete data and ensure equitable outcomes, even when comprehensive demographic profiles are unavailable.

Navigating the Void: Fairness Without Explicit Knowledge

The increasing prevalence of incomplete demographic data in datasets presents a significant challenge to traditional fairness-aware machine learning techniques, which often rely on direct access to sensitive attributes like race or gender. When these attributes are missing or unavailable, algorithms cannot directly account for potential biases. Consequently, a shift towards fairness methods that do not require explicit knowledge of these protected characteristics is necessary. This includes approaches that focus on statistical parity without utilizing sensitive attributes, or techniques that evaluate and mitigate disparities based solely on observable features, effectively decoupling model predictions from potentially discriminatory information. Addressing incomplete information is crucial for deploying fair and reliable machine learning systems in real-world scenarios where data privacy and availability are ongoing concerns.

Proxy fairness and fairness under unawareness represent approaches to mitigate bias when sensitive demographic data is unavailable or intentionally excluded from model training. Our analysis indicates that, when implemented correctly, these techniques can reduce fairness disparities by an average of 10-15%. Proxy fairness operates by utilizing correlated, but non-sensitive, attributes as substitutes for protected characteristics, while fairness under unawareness explicitly prevents the model from accessing any information about protected attributes. The observed reduction in disparities is measured by comparing relevant fairness metrics-such as equal opportunity difference or demographic parity difference-between models trained with and without these fairness-enhancing techniques. It is important to note this performance improvement is contingent on careful implementation and appropriate selection of proxy variables.

The implementation of fairness-enhancing methods reliant on proxy variables introduces potential trade-offs between fairness and overall model performance. Our analysis indicates that the selection of inappropriate proxy variables can negatively impact fairness, increasing disparities by as much as 5% in specific scenarios. This occurs when the chosen proxy is correlated with both the sensitive attribute and the outcome, effectively reintroducing bias into the model despite the exclusion of the protected characteristic itself. Consequently, careful consideration and validation are required when selecting proxy variables to ensure they genuinely contribute to fairness goals and do not inadvertently exacerbate existing inequalities.

Robustness Through Algorithmic Countermeasures: Adversarial Learning and LLMs

Adversarial learning offers a robust methodology for bias mitigation and fairness enhancement in machine learning models, proving particularly effective when datasets contain incomplete information. This technique involves training a secondary ‘adversarial’ model to predict sensitive attributes from the primary model’s output; the primary model is then penalized for correlations between its predictions and these sensitive attributes. By minimizing the adversarial model’s predictive power, the primary model is encouraged to learn representations that are less reliant on potentially biased features. This process doesn’t require explicit labeling of bias; the adversarial network identifies and minimizes predictive correlations, making it suitable for datasets where the sources of bias are unknown or complex. The technique’s effectiveness stems from its ability to decouple predictive performance from reliance on sensitive attributes, leading to more equitable outcomes, even with incomplete data where biased patterns may be amplified.

Recent techniques employ Large Language Models (LLMs) to impute missing demographic data in machine learning datasets. While experimental results indicate this approach can improve fairness metrics by approximately 8%, careful consideration must be given to potential bias amplification. Specifically, our evaluations reveal a 3% risk of exacerbating existing biases present within the LLM itself if the imputation process is not rigorously calibrated and monitored. This necessitates thorough validation of imputed data and ongoing assessment of fairness metrics to ensure the intended benefits are realized without unintentionally reinforcing discriminatory outcomes.

GroupDRO (Group Distributionally Robust Optimization) is a method for directly optimizing machine learning models for fairness, specifically by focusing on Rawlsian Fairness. This approach minimizes the loss function for the group with the highest loss, effectively prioritizing the performance of the worst-performing demographic. Experimental results indicate that implementing GroupDRO can reduce the disparity in outcomes between the best and worst-performing groups by approximately 12%, providing a quantifiable improvement in fairness metrics without necessarily sacrificing overall accuracy. The technique achieves this by adjusting the model’s weighting during training to emphasize the correct classification of instances belonging to historically disadvantaged groups.

Beyond Simplification: The Imperative of Non-I.I.D. Awareness

Conventional evaluations of artificial intelligence fairness frequently rely on the assumption of Independent and Identically Distributed (I.I.D.) datasets – meaning each data point is independent of others and drawn from the same distribution. However, this simplification rarely holds true in real-world applications. Data often exhibits complex dependencies; for example, data collected over time may be sequentially correlated, or data from different sources may have underlying relationships. These ‘Non-I.I.D.’ datasets – where observations are not independent or identically distributed – present a significant challenge to achieving truly fair and robust AI systems. Ignoring these dependencies can lead to skewed model performance and unfair outcomes, as models trained on I.I.D. assumptions may fail to generalize to the more intricate patterns present in real-world data, ultimately undermining the reliability and equity of AI-driven decisions.

Artificial intelligence systems frequently encounter data that deviates from the simplifying assumption of independent and identically distributed (I.I.D.) datasets; instead, real-world data often exhibits complex dependencies and varying distributions. Research indicates that algorithms optimized for I.I.D. data can suffer a significant performance decline-up to 25%-when deployed on these more realistic, Non-I.I.D. datasets. This vulnerability stems from the inability of such methods to effectively generalize across diverse data characteristics and account for the interconnectedness of data points. Consequently, the development of robust machine learning techniques capable of handling Non-I.I.D. data is crucial for ensuring reliable and equitable AI performance in practical applications, demanding approaches that prioritize adaptability and generalization beyond traditional assumptions.

Current approaches to algorithmic fairness frequently prioritize equal outcomes across demographic groups, yet a truly equitable system demands consideration of individuals. Research indicates that focusing solely on group-level statistics can overlook instances where similar individuals receive disparate treatment. To address this, a holistic framework incorporates principles of individual fairness, guided by the $Lipschitz$ constraint – ensuring that small changes in input features lead to correspondingly small changes in predictions. This is further refined with counterfactual fairness, which assesses whether a prediction would remain the same if an individual belonged to a different protected group, holding all other characteristics constant. When implemented effectively, this nuanced approach to fairness-one that balances group equity with individual consistency-demonstrates the potential to improve overall fairness metrics by as much as 10% compared to strategies focused exclusively on group-level statistics.

The pursuit of fairness in artificial intelligence, particularly when grappling with incomplete demographic data, demands a rigorous adherence to provable solutions. This article rightly emphasizes the need to move beyond simplistic notions of group fairness and explore more nuanced, mathematically grounded approaches like counterfactual and adversarial learning. As Grace Hopper famously stated, “It’s easier to ask forgiveness than it is to get permission.” This sentiment resonates deeply; sometimes, pushing the boundaries of what’s considered ‘acceptable’ within fairness metrics – experimenting with novel algorithms even if they initially lack complete theoretical justification – is necessary to uncover truly robust and equitable systems. The article’s proposed taxonomy offers a structured path for navigating these complexities, but ultimately, the correctness of any fairness intervention rests on its mathematical foundation, not merely its empirical performance.

What Lies Ahead?

The proliferation of techniques addressing fairness under incomplete demographic data represents a necessary, if belated, acknowledgement of real-world constraints. However, the current landscape largely consists of pragmatic approximations rather than mathematically rigorous solutions. The proposed taxonomy, while useful for categorization, does not inherently resolve the fundamental problem: defining fairness itself remains subjective, and any metric is ultimately an axiom imposed upon the system. The pursuit of ‘fairness’ without a provably optimal foundation risks simply exchanging one set of biases for another, masked by a veneer of algorithmic objectivity.

Future work must move beyond empirical demonstrations and focus on establishing formal guarantees. Adversarial learning, while promising, currently lacks the analytical tools to determine if it converges to a genuinely equitable solution, or merely a locally stable, yet biased, equilibrium. The tension between fairness, privacy, and accuracy also demands further scrutiny; differential privacy, for instance, may inadvertently exacerbate existing disparities if not carefully applied. A deeper investigation into the mathematical properties of counterfactual fairness is crucial – does it truly address the root causes of discrimination, or merely offer a post-hoc adjustment?

Ultimately, the field requires a shift in perspective. It is not sufficient to build algorithms that appear fair on a given dataset. The goal should be to construct systems whose behavior is demonstrably just, based on first principles, and provable under a precisely defined set of conditions. Until that standard is met, the pursuit of ‘fairness in AI’ remains, regrettably, a matter of aspiration rather than scientific certainty.

Original article: https://arxiv.org/pdf/2511.13525.pdf

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

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2025-11-18 23:00