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Integrating Fairness and Explainability in a Multiple Instance Reinforcement Learning System
Bente Hinkenhuis, Seyed Sahand Mohammadi Ziabari, Ali Mohammed Mansoor Alsahag
TL;DR
Student-performance prediction must identify useful interactions for intervention while avoiding unfair predictions associated with demographic information. This study adds adversarial debiasing and preference-conditioned hypernetworks to interpretable RL–MIL to test fairness–performance control. The hypernetwork designs do not reliably control that trade-off, and the paper concludes that conditioning alone is insufficient.
Problem
Student outcomes have labels at the course level, but the most predictive interactions are unknown, and transparent, fair predictions matter for intervention.
Source: §1 · p. 1Method
Results
Takeaways & Limitations
Preference conditioning alone is insufficient for reliable fairness optimization; future designs should prioritize optimization stability and objective separation.
Source: §6 · p. 9The learning rate was not adjusted separately for different framework components, which the authors identify as a likely mistake.
Source: §5.2 · p. 6
Abstract
from arXiv · showhide
Predicting student performance from educational interaction data requires models that are both accurate and sufficiently transparent to support meaningful intervention, while demographic information introduces an additional risk of unfair predictions. This study investigates a multi-objective framework that combines reinforcement learning-based multiple instance learning (RL-MIL), adversarial debiasing, and preference-conditioned hypernetworks for student-at-risk prediction. MIL represents each student as a bag of weakly labeled interactions, while an RL agent selects informative instances for downstream classification. Two hypernetwork variants are evaluated to determine whether a user-defined preference scalar can continuously control the trade-off between predictive performance and Equalized Odds. The underlying RL-MIL baseline achieves strong classification performance, but both hypernetwork extensions exhibit mode collapse: changing the preference weight produces little systematic movement along the intended fairness-performance frontier. The failure is associated with objective dominance, weak gradient propagation through the conditioning mechanism, and interactions between dynamically generated parameters. The results show that fairness objectives can be incorporated into an interpretable RL-MIL pipeline, but preference conditioning alone does not guarantee controllable multi-objective behavior. Robust fair RL-MIL therefore requires explicit mechanisms for gradient balancing, objective separation, and stability analysis.
1 Introduction
Student outcomes are labeled at the course level, leaving the most predictive interactions unidentified; useful interventions also require transparent instance selection. The study tests whether preference-conditioned hypernetworks can control fairness–performance trade-offs in an interpretable RL–MIL student-at-risk framework.
1 Introduction
- Student outcomes are observed for whole course trajectories, while the interactions driving those outcomes remain unlabeled, making the setting suitable for multiple instance learning.Each student’s information forms a bag labeled as a whole.Source: §1 · p. 1
- Transparent instance selection matters because interventions differ depending on whether a student struggles with assignments or does not use particular online resources.Prediction of the final outcome alone does not identify which information indicates the predicted outcome.Source: §1 · p. 1
- RL-based instance selection offers dynamic selection and multi-objective reward design, while prior student-outcome work reports interpretable context-aware attention policies that preserve predictive performance.The study evaluates selection for predictive utility, demographic fairness, and its effects on which interactions are retained.Source: §1 · p. 1
- Preference-conditioned hypernetworks are evaluated for controllable fairness–performance trade-offs and for whether stronger debiasing changes selected interactions while preserving selection sparsity and consistency.The framework combines adversarial debiasing with interpretable RL–MIL student-at-risk prediction.Source: §1 · p. 1
2 Related Work
Related work motivates fair, interpretable RL-based instance selection: it can support explanations, but selection stability, bias mitigation, and evaluation remain challenges. Prior student-prediction research provides an attention-based RL–MIL baseline, while fairness studies describe several mitigation strategies and caveats.
2.1 Instance Selection using RL
- RL instance selection supports dynamic instance combinations and online adaptation, but prior work reports over-selection of frequent, less informative instances, no guarantee of selecting the most valuable instances, and stability challenges.Reported challenges include early-training exploration and delayed rewards, policy collapse, and manual stability tuning [4].Source: §2.1 · p. 2
2.2 Interpretability of RL Methods
- RL-based instance selection offers a natural way to assess policy interpretability; on OULAD, gated and multi-head attention policies can match an epsilon-greedy RL–MIL baseline’s predictive performance while reducing explanation latency.Prior work notes that interpretability receives less focus as model complexity grows and calls for standardized interpretability metrics.Source: §2.1 · p. 2, §2.2 · p. 2
- Interpretable student-performance predictions can help educators derive interventions suited to students’ needs, while predictions also need to be as unbiased as possible.Source: §2.2 · p. 2
2.3 Fairness in RL
- Fairness methods include pre-processing, in-processing, and post-processing; student-prediction studies examine balancing, latent-space mitigation, and adversarial debiasing.Fairness metrics can conceal label- or domain-specific disparities [18], while proxy variables and unobserved confounding constrain associational debiasing and demographic-leakage suppression may trade off against utility [19].Source: §2.3 · pp. 2–3
3 Methodology
The methodology extends an RL–MIL student-at-risk baseline with adversarial debiasing and preference-conditioned hypernetworks, then evaluates performance, fairness, and selection interpretability. It tests whether preference settings yield a useful trade-off while holding the experimental setup consistent across model configurations.
3 Methodology
- Student performance is formulated as multiple instance learning: each student has a labeled super-bag, from which a smaller subset is selected for a single classifier prediction.The label indicates course completion versus at-risk status; protected feature instances are separately indexed for bias mitigation.Source: §3 · p. 3
3.1 Data
- OULAD provides student demographics, registration details, assessment results, and virtual-learning-environment participation measured as daily clicks on learning materials.Source: §3.1 · p. 3
3.2 Preprocessing
- Preprocessing aggregates VLE interactions by activity type while retaining click timing, min-max scales numerical features, embeds categorical features, and pads bags to 39 instances with masks.The three retained timing columns are first click day, last click day, and number of click days.Source: §3.2 · p. 3
3.3 Exploratory Data Analysis
- A correlation matrix was used to form hypotheses about which features might be downweighted due to association with protected features and which exclusions might most reduce performance.Source: §3.3 · p. 3
3.4 Baseline model
- The baseline RL agent selects instances from super-bags for a MIL classifier, with classification cross-entropy backpropagated to update the classifier and agent.The classifier uses a pretrained autoencoder, one of four pooling schemes, and a multilayer perceptron.Source: §3.4 · p. 3
3.5 Proposed multi-objective framework
- The extended framework predicts four protected demographic features from the classifier’s final hidden layer and uses mean cross-entropy as a fairness signal alongside prediction performance.A preference-conditioned hypernetwork generates parameters mixed with learned parameters; task-agnostic conditioning controls selection, while task-aware conditioning also controls the task-model MLP.Source: §3.5 · p. 4
3.6 Evaluation
- Evaluation compares the training Pareto front with the baseline on test-set F1, maximum sensitivity–specificity difference across protected features, selection sparsity, and selection consistency.Sparsity is measured by Gini coefficient; consistency uses split-half Spearman correlation and variance across seeds.Source: §3.6 · p. 4
3.7 Experimental setup
- Experiments cover three architectures and four pooling methods across five seeds, using 80/10/10 train-validation-test splits, balanced batches, and fixed bag size 20.Hyperparameters were selected using Bayesian optimization.Source: §3.7 · p. 4
4 Results
Results show little preference-sensitive change: classification and Equalized Odds comparisons lack a clear winner, while selected-instance frequencies and explanation metrics remain similar across preference settings.
4.1 Exploratory Data Analysis
- Prior education and IMD weakly correlated with outcomes and assessment scores; authors expected them to be dropped less often than age and gender, with age expected first.These expectations were based on the exploratory correlations and relationships described in the analysis.Source: §4.1 · pp. 4–5
4.2 Model performance analysis
- Across pooling methods, models had similar classification performance, preference extremes produced little adaptation, and no Equalized Odds comparison was significant.The only significant F1-score comparison was max pooling with the task-aware hypernetwork; results showed low standard deviations across seeds.Source: §4.2 · p. 5, Table 2 · p. 6
4.3 Model interpretability analysis
- Selected-instance frequencies shifted only slightly and nonsystematically across the baseline and task-agnostic extremes, with protected-feature presence remaining similar.The comparison covers common instance types sorted by the fraction of bags in which they appear.Source: §4.3 · p. 5
- Explanations were comparably sparse and consistent, with Spearman’s ρ and Jaccard’s K close to 1 across models and pooling methods, and did not differ by preference.Source: §4.3 · p. 5
5 Discussion
Both preference-conditioned hypernetworks fail to yield reliable control of demographic bias or a meaningful fairness–performance frontier. The discussion examines possible optimization and state-space explanations, future research directions, and ethical constraints on deployment.
5 Discussion
- Both hypernetwork frameworks fail to reliably control demographic bias, and the experiments establish no meaningful controllable fairness–performance frontier.Preference-conditioned configurations converge toward essentially the same behavior.Source: §5 · p. 6, §5.1 · p. 6
5.1 Elaboration
- Mode collapse prevents assessing how instance-selection policies and explanations change in low-bias environments; the models offer no value over the baseline.Reproducibility and reliability metrics reflect similarity to the baseline, not validity for the study’s goals.Source: §5.1 · p. 6
5.2 Hyperparameter grid
- The hyperparameter grid should have used separate learning-rate ranges for the task model, debiasing model, policy network, and hypernetwork.The authors consider not adjusting learning rates separately a likely mistake.Source: §5.2 · pp. 6–7
5.3 Complex implications of small parameter changes
- The hypernetwork must learn complex nonlinear parameter relationships, while small parameter changes can substantially alter instance selection.Fourier embeddings may not suffice; proposed options include another hidden layer or soft constraints on the output parameter space.Source: §5.3 · p. 7
5.4 State-action space shift
- Joint training may create an uncertain state–action space, especially when the task-aware model adapts to the debiasing model’s outputs.That uncertainty reaches the hypernetwork and may obscure the effects of parameter adjustments.Source: §5.4 · p. 7
5.5 Future work
- Future work could address optimization failures with alternative conditioning, gradient balancing, or separate objective-specific parameters, and investigate causal regularization.The proposed educational causal model treats assessment performance as an ancestor of course completion and gender as an ancestor of assessment performance through test-taking strategies.Source: §5.5 · pp. 7–9
- The proposed causal model is motivated by a correlation matrix in which protected-feature correlations are low but comparatively higher for scores and click statistics.The matrix assumes interval scaling for categorical variables, which the caption identifies as technically incorrect.Source: Figure 2 · p. 8, §5.5 · pp. 8–9
5.6 Ethical considerations
- Real-world student-at-risk systems should leave intervention choices to humans and avoid severe bias and excessive false positives or negatives.The authors say it remains unclear whether an algorithm meeting all these requirements can be built.Source: §5.6 · p. 9
6 Conclusion
The study extends RL–MIL with preference-conditioned hypernetworks to seek a configurable fairness–performance trade-off, but the proposed network structure did not capture the intended patterns. The authors conclude that conditioning alone is insufficient for reliable preference-controlled fairness optimization in its current form.
6 Conclusion
- The study extends RL–MIL with preference-conditioned hypernetworks to test a configurable fairness–performance frontier and changes in instance selection under greater fairness pressure.Source: §6 · p. 9
- The proposed hypernetwork design is not suitable for reliable preference-controlled fairness optimization in its current form.The study reports that its novel network structure failed to capture the patterns it aimed to examine.Source: §6 · p. 9
- A conditioning scalar is insufficient when competing-objective gradients do not produce distinct parameter regimes; future work should prioritize optimization stability and objective separation.The authors also suggest exploring causal regularization as a possible way to reduce model bias.Source: §6 · p. 9
B Tables
The appendix tables list model performance metrics and statistical comparisons across frameworks, pooling methods, seeds, and preferences. Some task-aware pooling results were run on only one seed because of supercomputer maintenance.
B Tables
- Table 5 lists performance metrics for the best models, while Table 6 lists metrics across framework, pooling method, seed, and preference combinations.Source: Table 5 · p. 16, Table 6 · p. 21
- Task-aware Hypernetwork Mean, Max, and Attention pooling results in Tables 5 and 6 were based on only one seed because of supercomputer maintenance.Source: Table 5 · p. 16, Table 6 · p. 21
- Tables 7 and 8 report t-test results for Equalized Odds Deviation and F1-score, respectively, across frameworks and pooling methods.Source: Table 7 · p. 22, Table 8 · p. 22
- Table 9 reports t-tests comparing Equalized Odds Deviation between preferences 0 and 1 for both hypernetwork architectures and all pooling methods.Source: Table 9 · p. 22