4 papers
Fairness Evaluation and Inference Level Mitigation in LLMs
Afrozah Nadeem, Mark Dras, Usman Naseem
Large language models often display undesirable behaviors embedded in their internal representations, undermining fairness, inconsistency drift, amplification of harmful content, a…
Bias Beyond Borders: Political Ideology Evaluation and Steering in Multilingual LLMs
Afrozah Nadeem, Agrima Seth, Mehwish Nasim +1
Large Language Models (LLMs) increasingly shape global discourse, making fairness and ideological neutrality essential for responsible AI deployment. Despite growing attention to p…
Framing Political Bias in Multilingual LLMs Across Pakistani Languages
Afrozah Nadeem, Mark Dras, Usman Naseem
Large Language Models (LLMs) increasingly shape public discourse, yet most evaluations of political and economic bias have focused on high-resource, Western languages and contexts.…
Steering Towards Fairness: Mitigating Political Bias in LLMs
Afrozah Nadeem, Mark Dras, Usman Naseem
Recent advancements in large language models (LLMs) have enabled their widespread use across diverse real-world applications. However, concerns remain about their tendency to encod…