As artificial intelligence (AI) continues to evolve, ensuring that models behave responsibly and ... more As artificial intelligence (AI) continues to evolve, ensuring that models behave responsibly and align with human values has become a pressing concern. Constitutional AI (CAI), developed by Anthropic, proposes an approach wherein a large language model is guided by a transparent set of principles-its "constitution." This paper provides an expanded overview of Constitutional AI, its background, methodology, practical implementation details, and future directions. We also include placeholders for figures from the original CAI publication to illustrate its core workflow and contrasts with more traditional alignment methods such as Reinforcement Learning from Human Feedback (RLHF).
As artificial intelligence (AI) advances, so do concerns about bias, misuse, and larger societal ... more As artificial intelligence (AI) advances, so do concerns about bias, misuse, and larger societal impacts. Governments, international bodies, and the private sector are increasingly recognizing the urgency to regulate AI, leading to proposed frameworks such as the European Union's AI Act [1] and the U.S. Blueprint for an AI Bill of Rights [2]. This paper explores three critical angles in AI governance: (1) the ethics and legality of using large-scale training data without explicit permission, (2) the debate around industry self-regulation versus external oversight, and (3) inherent biases and fairness issues in AI data and systems. By examining these dimensions, we highlight both opportunities and pitfalls in the emerging regulatory landscape.
The evaluation of AI model robustness is a critical aspect of ensuring the reliability and effect... more The evaluation of AI model robustness is a critical aspect of ensuring the reliability and effectiveness of large language models (LLMs). While traditional evaluation methods often focus on performance metrics like accuracy and fluency, these approaches fail to capture a model's ability to handle edge cases, ambiguous inputs, or outlier scenarios. Contrast sets, which involve the use of carefully curated input pairs with subtle differences, provide a powerful tool to address this limitation. By testing an LLM on these contrast sets, researchers can gain deeper insights into how well the model generalizes across diverse situations, revealing its weaknesses and vulnerabilities that might otherwise go unnoticed. Contrast sets work by highlighting nuanced differences in input data that challenge a model's understanding and decision-making processes. This approach enables the detection of hidden biases, inconsistencies, and flaws that may impact the model's real-world application. Additionally, contrast sets can be tailored to target specific aspects of model performance, such as reasoning ability, knowledge representation, and contextual comprehension. This focused testing offers more fine-grained analysis compared to broad benchmarks. Incorporating contrast sets into LLM benchmarking not only enhances our understanding of model robustness but also promotes fairness and accountability in AI systems. As AI continues to play an increasing role in decision-making processes, it is essential to develop tools that ensure these systems are both reliable and trustworthy. Contrast sets present a promising avenue for improving the robustness evaluation of LLMs, providing valuable insights that drive the development of more reliable, transparent, and equitable AI models. Through the strategic application of contrast sets, we can move towards a more comprehensive and effective approach to AI model evaluation.
DeepSeek R1, introduced in early 2025, has garnered attention for its cutting-edge language and p... more DeepSeek R1, introduced in early 2025, has garnered attention for its cutting-edge language and predictive capabilities. However, emerging community reports and analyses highlight significant risks tied to security, data handling, and compliance-particularly for enterprises leveraging large language models (LLMs) at scale. This paper expands on previous findings to integrate newly available research on LLM security. We examine how DeepSeek R1's training data discrepancies, potential cross-border data transfers, and inherent vulnerabilities align with broader enterprise concerns about generative AI. We conclude with actionable recommendations for organizations seeking to responsibly adopt DeepSeek R1 while minimizing security and compliance pitfalls.
Uploads
Papers by Manish Sanwal