Dissertations - M Tech (CS)

Permanent URI for this collectionhttps://dspace.isical.ac.in/handle/10263/2147

These Dissertations were submitted in partial fulfilment of the requirements for the award of M TECH (Computer Science) Degree of Indian Statistical Institute

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    Learning to See Lesions, Not Skin Tone: Counterfactual Multimodal Learning for Fair, Trustworthy, and Text-Free Dermatology AI
    (Indian Statistical Institute, 2026-06-16) Jangid, Shivam
    Recent advances in deep learning have significantly improved the performance of automated skin lesion classification systems, enabling accurate detection of various dermatological conditions from medical images. Despite these achievements, concerns regarding fairness and generalization remain a major challenge for the deployment of such systems in real-world clinical settings. A key factor contributing to these challenges is the presence of bias in training datasets, particularly with respect to skin-tone representation. Most publicly available skin lesion datasets contain a disproportionate number of samples from individuals with lighter skin tones. As a result, deep learning models trained on these datasets often learn representations that perform well for majority populations while exhibiting degraded performance on underrepresented groups. Such disparities can lead to unequal diagnostic outcomes and raise important concerns regarding the reliability and fairness of artificial intelligence systems in healthcare. The problem is especially relevant in the Indian context, where substantial diversity exists in skin tone, ethnicity, and geographical distribution. Variations in lesion appearance across di!erent skin types, combined with the limited availability of representative datasets, make it di”cult to develop models that generalize e!ectively to the broader population. Consequently, addressing dataset bias and improving model robustness across diverse demographic groups has become an important research objective in skin lesion analysis. Motivated by these challenges, this dissertation investigates fairness-aware approaches for skin lesion classification. The work focuses on the creation and analysis of diverse skin lesion datasets, the study of bias mitigation techniques, and the development of robust deep learning models capable of learning equitable representations. Furthermore, the dissertation explores invariant and equivariant learning paradigms as potential mechanisms for improving generalization across heterogeneous data distributions. To enhance model transparency and support trustworthy decision-making, explainability techniques are also examined to provide insights into the features utilized by the learned models during classification. Through these investigations, the dissertation aims to contribute toward the development of more reliable, interpretable, and fair deep learning systems for skin lesion analysis, with particular emphasis on improving performance across diverse skin-tone distributions.
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    Bias Before Generation: Attention-based Preemptive Fairness Signals in Large Language Models
    (Indian Statistical Institute, 2026-06-15) Das, Aniket
    Warning: This paper includes examples of language that may be perceived as inappropriate or offensive. Large language models (LLMs) are known to propagate social biases embedded in their training corpora, producing outputs that disproportionately disadvantage individuals based on sensitive attributes such as gender, religion, race, sexual orientation and nationality. Existing mitigation strategies are either computationally prohibitive, require access to model parameters, or apply corrections only after biased content has already been generated. This work addresses a different question: can the model’s own internal attention dynamics, observed at inference time, serve as a reliable early-warning signal for bias, enabling intervention before generation proceeds? We propose Bias Before Generation (BBG), an attention-based, trainingfree framework for preemptive fairness intervention in generative language models. BBG analyses three complementary attention-based signals during a single forward pass: Protected Attribute Attention, which quantifies the proportion of generative attention directed at protected demographic tokens; Attention Entropy, which captures the global dispersion of attention across the input; and the Identity-Conditioned Entropy Ratio (ICER), a novel metric that isolates the fraction of total attention entropy attributable to identity-bearing tokens, thereby distinguishing legitimate identity-aware discourse from stereotype-driven uncertainty. These three signals are combined into a weighted bias score, and prompts whose score exceeds a learned threshold receive an automatically prepended alert prefix that steers the model toward neutral reasoning before generation. The framework is evaluated on multiple open-weight LLM families across two standard fairness benchmarks: BBQ and CrowS-Pairs. Experimental results demonstrate consistent, statistically significant reductions in bias scores across all tested models and social-group categories, with minimal degradation in overall response quality. These findings indicate that attention-level signals offer a principled and computationally efficient basis for preemptive fairness intervention in generative language models. We hope this work opens further inquiry into inference-time pproaches for bias detection and mitigation.