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AI and Bullshit
It is argued that both AI and bullshitters are untrustworthy informants, and for similar reasons, it is natural to describe AI’s informational outputs as bullshit, as it signals their distinctive kind of epistemic deficiencies, which they share with bullshit.
Ai In Indian Judiciary: A Comprehensive Study Evaluating Operational Deployments, Administrative Obstacles, And Supreme Court Directives
Abstract The integration of Artificial Intelligence (AI) into the Indian judiciary represents a paradigm shift toward modernizing legal administration, enhancing processing efficiencies, and addressing massive case backlogs that have historically strained the justice delivery system. India's judiciary, burdened by one of the largest case pendency figures in the world, has increasingly turned toward digital and computational tools to reduce procedural delay without compromising the constitutional guarantees owed to litigants. This comprehensive research article evaluates operational deployments, administrative obstacles, and Supreme Court directives governing AI usage under the e-Courts framework. It examines the operational boundaries established by the Supreme Court White Paper, the active AI assistive ecosystem (including SUVAS, SUPACE, and LegRAA), legal integrity and professional accountability concerning generative AI hallucinations, and structural process engineering integrated under Phase-III. The study further situates these developments within a constitutional and comparative framework, arguing that India's cautious, human-centred model of AI adoption offers a template that other developing judiciaries may study as they confront similar backlogs, resource constraints, and linguistic diversity. The paper concludes that sustained capacity building, rigorous ethical audits, and close collaboration between the legal profession and technologists will determine whether AI ultimately strengthens, rather than erodes, public confidence in the administration of justice.
Generative AI Methodology for the Conceptual Topology Design of Bridges from Topography Images
This work presents an alternative, result-oriented, data-driven method based on generative AI to assist engineers in the conceptual design phase of bridge construction, and demonstrates that result-oriented, data-driven generative models can support early-stage bridge topology exploration under controlled conditions.
Impact of Artificial Intelligence Driven Recruitment and Employee Selection in IT Companies in Chennai
Abstract Artificial Intelligence (AI) has significantly transformed recruitment and employee selection by improving the efficiency, accuracy and transparency of hiring processes in IT organizations. This study examines the impact of Artificial Intelligence Driven Recruitment and Employee Selection in IT Companies in Chennai. A quantitative research design was adopted and simulated data from 175 respondents were analysed using descriptive statistics, reliability analysis, correlation and multiple regression techniques through SPSS. The simulated results indicate that AI-driven recruitment positively influences recruitment efficiency, candidate experience and the quality of employee selection. The regression analysis revealed that AI-driven recruitment and employee selection explained 71.5% (R² = 0.715) of the variation in recruitment effectiveness, with all proposed hypotheses supported in the simulated analysis. The findings suggest that AI-based recruitment systems enable organizations to reduce hiring time, improve decision-making, minimize recruitment bias and enhance overall talent acquisition. The study highlights the importance of integrating AI technologies into HR practices to strengthen recruitment outcomes in Chennai's IT sector. The data and findings presented are simulated solely for academic demonstration and methodological illustration.