Search arXiv⌕ Search

arXiv · 2307.10615

Analyzing HC-NJDG Data to Understand the Pendency in High Courts in India

Abstract

Indian Judiciary is suffering from burden of millions of cases that are lying pending in its courts at all the levels. In this paper, we analyze the data that we have collected on the pendency of 24 high courts in the Republic of India as they were made available on High Court NJDG (HC-NJDG). We collected data on 73 days beginning August 31, 2017 to December 26, 2018, including these days. Thus, the data collected by us spans a period of almost sixteen months. We have analyzed various statistics available on the NJDG portal for High Courts, including but not limited to the number of judges in each high court, the number of cases pending in each high court, cases that have been pending for more than 10 years, cases filed, listed and disposed, cases filed by women and senior citizens, etc. Our results show that: 1) statistics as important as the number of judges in high courts have serious errors on NJDG (Fig. 1, 2, 10, 11, Table V). 2) pending cases in most of the high courts are increasing rather than decreasing (Fig. 3, 13). 3) regular update of HC-NJDG is required for it to be useful. Data related to some high courts is not being updated regularly or is updated erroneously on the portal (Fig. 14). 4) there is a huge difference in terms of average load of cases on judges of different high courts (Fig. 6). 5) if all the high courts operate at their approved strength of judges, then for most of the high courts pendency can be nullified within 20 years from now (Fig. 21, 22). 6) the pending cases filed by women and senior citizens are disproportionately low, they together constitute less than 10% of the total pending cases (Fig. 23 - 27) 7) a better scheduling process for preparing causelists in courts can help reducing the number of pending cases in the High Courts (Fig. 29). 8) some statistics are not well defined (Fig. 31).

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kshitiz Verma. 2023-07-20. Analyzing HC-NJDG Data to Understand the Pendency in High Courts in India. https://arxiv.org/abs/2307.10615

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Political Persuasion and Endorsement in Large Language Models

Large Language Models (LLMs) are increasingly employed as proxies for human behavior in computational social science. However, their tendency to internalize biases from training data raises concerns about their reliability in politically sensitive domains, specifically regarding their endorsement of persuasive language. In this work, we examine whether LLMs endorse persuasion-infused messages and whether partisan persona prompting modulates such endorsement. We evaluate six LLMs from different geographic regions on content annotated with persuasion techniques drawn from real-world media sources, measuring the likelihood of endorsement using a five-point Likert scale. The models are prompted as either a neutral social media user or as a user with left- or right-leaning political views. Results show that, without political conditioning, LLMs generally do not endorse messages containing persuasion techniques, although model-level differences emerge, and that partisan persona prompting increases polarization of endorsement, particularly for persuasion-infused content. Endorsement further varies across persuasion techniques and topics. These findings reveal how political conditioning influences LLMs' endorsement behavior toward persuasion-infused content, raising concerns about their deployment as agents in politically sensitive environments and complicating their use as reliable simulators of human political cognition.

cs.CY↗

When Technically Plausible Advice Is Unsafe: A Cross-Ecosystem Measurement of Online Support for Technology-Facilitated Abuse

Technology-facilitated abuse (TFA) creates an adversarial setting where sound cybersecurity advice can be unsafe: changing credentials or resetting devices may alert an abuser, destroy evidence, or increase escalation risk. Victims seek guidance from search engines, peer forums, and conversational AI, often evaluated for relevance and correctness rather than contextual safety. We measure whether these sources meet victims' needs. From a decade of r/Stalking narratives, we construct 2,797 victim-derived queries spanning 11 misuse categories. We analyze 27,162 Google webpages, 2,476 Reddit query--thread responses, and 250 responses from three general-purpose LLMs and two survivor-support chatbots. Our framework measures technical quality and damaging guidance, plus secondary-link integrity on webpages, toxicity on Reddit, and trauma-informed support in conversational systems. We find failures & risks that relevance, accuracy, or actionability alone do not capture. Web Search and conversational systems frequently return relevant information; Reddit responses are less consistently relevant and actionable. In our evaluated accuracy sample, 17.3% of webpages, 13.3% of Reddit threads, and 19.6% of conversational AI responses contained damaging guidance. Further, 65.5% of victim queries led to a webpage with a secondary URL flagged by multiple VirusTotal engines, over 20% received a toxic Reddit comment, and every conversational system produced guidance that overlooked escalation risk. Specialization did not guarantee better support: HopeChat underperformed general-purpose LLMs on several dimensions, while Ruth remained limited in trauma-informed support. These findings expose a gap between technical quality and contextual safety. Safe TFA assistance requires risk-aware recommendations, trustworthy sources, uncertainty communication, and human support, beyond technically plausible answers.

cs.CY↗

Auditing Institutional Heterogeneity for Generative AI in Patient Education: A Large-Scale Study of 102 US Transplant Handbooks

Health systems are rapidly deploying generative-AI assistants that answer patient questions from institution-authored education materials, on the premise that grounding in local content yields consistent guidance. Do the underlying documents themselves agree? We use a structured-output large-language-model judge to audit 1{,}772{,}261 pairwise comparisons across 102 patient-education handbooks from 23 US solid-organ transplant centers, paired with 1{,}115 patient-derived questions (TransplantQA). Four findings bear directly on deployment: (1) same-center cross-organ agreement exceeds cross-center same-organ agreement by $0.024$ in the primary analysis (Holm-adjusted $p=0.011$), with sensitivity to document selection; (2) information gaps concern topics relevant to underrepresented subgroups, with reproductive health a \emph{double jeopardy}: 82\% absence and 86\% judge-rated high significance among divergent/contradictory pairs; (3) judge-derived themes form 991 clusters, with immunosuppression and pregnancy timing among the highest judge-rated priorities; (4) question and observed-coverage features predict high-divergence questions retrospectively (AUC $0.77$). We discuss implications for deploying patient-facing generative AI in transplant care.

cs.CY↗