The rapid integration of Artificial Intelligence (AI) is transforming the modern workplace, offer... more The rapid integration of Artificial Intelligence (AI) is transforming the modern workplace, offering both opportunities and challenges. This paper explores the complexities of AI-driven digital workplace solutions offerings and its effects on the workforce. Key issues include job displacement, algorithmic bias, and data privacy, which have significant consequences for both individuals and organizations. Using a mixed-methods approach, I gather qualitative insights through interviews and quantitative data from surveys to evaluate employee perceptions of AI's impact on their work. The research proposes a human-centered ethical framework for AI adoption, focusing on fairness, transparency, and accountability. This framework aims to guide organizations in implementing AI in ways that support human workers rather than replace them. Our findings provide practical insights for policymakers, businesses, and researchers to navigate the ethical challenges AI poses in the digital workplace. The goal is to foster trust, promote innovation, and ensure that AI contributes to a future where all stakeholders benefit.
Large Language Models (LLMs) suffer from a critical "faithfulness gap". Their generated explanati... more Large Language Models (LLMs) suffer from a critical "faithfulness gap". Their generated explanations, such as Chain-of-Thought (CoT), are often post-hoc rationalizations that do not reflect the true computational process, posing a fundamental risk to AI safety. To resolve this, we propose a paradigm shift from generative to synthetic reasoning. I introduce Modular Reasoning Synthesis Transformers (MRSTs), a neuro-symbolic architecture that, for a given problem, synthesizes an explicit and verifiable reasoning program from a library of specialized modules. This program, a formal computational graph, is then executed to produce the solution. By design, the executed program serves as a complete and faithful audit trail of the model's reasoning, eliminating the faithfulness gap. Our multi-objective training regime optimizes for not only accuracy but also the causal validity and conciseness of the synthesized programs. This synthetic reasoning approach provides a concrete pathway toward building large-scale AI systems that are demonstrably robust, auditable, and trustworthy.
Artificial intelligence (AI) is revolutionizing the modern workplace, presenting transformative o... more Artificial intelligence (AI) is revolutionizing the modern workplace, presenting transformative opportunities alongside complex ethical challenges. This paper examines the impact of Al-driven automation on the workforce, focusing on the interrelated challenges of job displacement, algorithmic bias, and data privacy. Through a mixed-methods approach, including 450 employee surveys across diverse industries and 20 semi-structured interviews, we assess employee perceptions and concerns about Al's influence on their work lives. Our findings reveal statistically significant concerns (p < 0.01) about job displacement, particularly among employees in routine roles, highlighting the need for proactive reskilling programs. The paper also presents three case studies, illustrating ethical Al implementations and offering practical guidance to organizations. Critically, this research proposes a structured, human-centered ethical framework for Al implementation, emphasizing fairness, transparency, and accountability as guiding principles. The framework is implemented through specific measurable and actionable steps. Our research is designed to empower rather than displace human workers. Findings offer practical guidance for organizations, policymakers, and researchers seeking to navigate the ethical landscape of Al in the digital workplace, fostering trust, promoting innovation, and ensuring a future where Al helps all stakeholders.
This paper presents a comprehensive review of the challenges and opportunities in designing ethic... more This paper presents a comprehensive review of the challenges and opportunities in designing ethical Artificial Intelligence (AI) systems and integrating Electric Vehicles (EVs) within humanitarian engineering and development contexts. Utilizing a systematic literature review, this research synthesizes current knowledge on AI ethics frameworks, AI applications in disaster management, EV capabilities in emergency response, and the environmental and social impacts of EV supply chains. Key findings highlight the critical need for culturally sensitive, transparent, and accountable AI deployment to mitigate bias and privacy risks. Concurrently, the transformative potential of EVs as mobile power sources and resilient transport in underserved regions is underscored. This paper proposes an integrated ethical framework to address the compounded risks and synergies of these technologies. Recommendations emphasize interdisciplinary collaboration, robust governance, and sustainable supply chain practices to ensure AI and EVs contribute equitably to global development and humanitarian efforts.
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Papers by Kamal Pandey