Skip to main content

Predicting Delays in Indian Lower Courts Using AutoML and Decision Forests

  • Conference paper
  • First Online:
Computing, Internet of Things and Data Analytics (ICCIDA 2023)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1145))

Included in the following conference series:

  • 92 Accesses


This paper presents a classification model that predicts delays in Indian lower courts based on case information available at filing. The model is built on a dataset of 4.2 million court cases filed in 2010 and their outcomes over a 10-year period. The data set is drawn from 7000+ lower courts in India. The authors employed AutoML to develop a multi-class classification model over all periods of pendency and then used binary decision forest classifiers to improve predictive accuracy for the classification of delays. The best model achieved an accuracy of 81.4%, and the precision, recall, and F1 were found to be 0.81. The study demonstrates the feasibility of AI models for predicting delays in Indian courts, based on relevant data points such as jurisdiction, court, judge, subject, and the parties involved. The paper also discusses the results in light of relevant literature and suggests areas for improvement and future research. The authors have made the dataset and Python code files used for the analysis available for further research in the crucial and contemporary field of Indian judicial reform.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions


  1. Law Commission of India: Reform of Judicial Administration (Law Comm No 14, 1958) para 4.2; Over 71,000 Cases Pending in Supreme Court, 59 Lakhs in High Courts: Law Minister Tells Rajya Sabha. Live Law (2022). Accessed 11 July 2023

  2. Verma, K.: E-courts project: a giant leap by Indian judiciary. Legal Inf. Inst. 4(12), 1–12 (2018)

    Google Scholar 

  3. Khaitan, N.: Inefficiency and Delay. Vidhi Cent. Legal Policy 1–24 (2017)

    Google Scholar 

  4. E-Committee: Supreme Court of India. National Policy and Action Plan for Implementation of Information and Communication Technology in the Indian Judiciary (2005).

  5. National Judicial Data Grid Homepage.

  6. 50 million cases were still unresolved as of 2022, including more than 169,000 cases in district and high courts that had been languishing for more than 30 years. Over 85% of all cases—43 million out of 50 million—were still pending in district courts as of December 2022

    Google Scholar 

  7. Cui, Y.: Artificial Intelligence and Judicial Modernization. Shanghai People’s Publishing House (2020).

  8. Kinhal, D., Jauhar, A., et al.: Virtual courts in India: a strategy paper. Vidhi Centre for Legal Policy (2020).

  9. Chau, K.W.: Prediction of construction litigation outcome – a case-based reasoning approach. In: Ali, M., Dapoigny, R. (eds.) IEA/AIE 2006. LNCS (LNAI), vol. 4031, pp. 548–553. Springer, Heidelberg (2006).

    Chapter  Google Scholar 

  10. Mahfouz, T., Kandil, A.: Litigation outcome prediction of differing site condition disputes through machine learning models. J. Comput. Civ. Eng. 26, 298–308 (2012)

    Article  Google Scholar 

  11. Aletras, N., et al.: Predicting judicial decisions of the european court of human rights: a natural language processing perspective. PeerJ Comput. Sci. 2, 1–19 (2016).

  12. Şulea, O.-M., Zampieri, M., Vela, M., van Genabith, J.: Predicting the law area and decisions of French supreme court cases. In: In: Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, Varna, Bulgaria, pp. 716–722. INCOMA Ltd. (2017)

    Google Scholar 

  13. Strickson, B., De La Iglesia, B.: Legal judgement prediction for UK courts. In: Proceedings of the 2020 3rd International Conference on Information Science and System, pp. 204–209 (2020)

    Google Scholar 

  14. Lage-Freitas, A., Allende-Cid, H., Santana, O., de Oliveira-Lage, L.: Predicting Brazilian court decisions. PeerJ Comput. Sci. 8 (2022)

    Google Scholar 

  15. Shaikh, R.A., Sahu, T.P., Anand, V.: Predicting outcomes of legal cases based on legal factors using classifers. Procedia Comput. Sci. 167, 2393–2402 (2020)

    Article  Google Scholar 

  16. Medvedeva, M., et al.: Rethinking the field of automatic prediction of court decisions. Artif. Intell. Law 31, 195–212, 198 (2023)

    Google Scholar 

  17. Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the European court of human rights. Artif. Intell. Law 28, 237–266 (2020)

    Article  Google Scholar 

  18. Supreme Court Vidhik Anuvaad Software’ is a machine-assisted translation tool trained by AI. Supreme Court of India. Press Release (2019).

  19. SUPACE: Supreme Court Portal for Assistance in Courts Efficiency; Shanti, S.: Behind SUPACE: The AI Portal of the Supreme Court of India (2021).

  20. Koshy, J.: Can Artificial Intelligence, Machine Learning put judiciary on the fast track? The Hindu, New Delhi (2022).

  21. Department of Justice. National Judicial Data Grid (2023).

  22. Sumeda: The clogged state of the Indian judiciary. The Hindu (2022).

  23. Supreme Court of India: Subordinate Courts of India: A Report on Access to Justice 2016 (Centre for Research & Planning, New Delhi), pp. 4–12 (2016)

    Google Scholar 

  24. Bhowmick, A., et al.: In-group bias in the Indian judiciary: evidence from 5.5 million criminal cases. In: ACM SIGCAS Conference on Computing and Sustainable Societies, pp. 47–47 (2021)

    Google Scholar 

  25. He, X., Zhao, K., Chu, X.: AutoML: a survey of the state-of-the-art. Knowledge-Based Systems 212 (2021).

  26. Xin, D., Wu, E.Y., Lee, D.J.L., Salehi, N., Parameswaran, A.: Whither AutoML? Understanding the role of automation in machine learning workflows. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1–16 (2021).

  27. Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  28. Sagi, O., Rokach, L.: Approximating XGBoost with an interpretable decision tree. Inf. Sci. 572, 522–542 (2021).

    Article  MathSciNet  Google Scholar 

  29. Galar, M., Fernández, A., Barrenechea, E., Bustince, H., Herrera, F.: An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes. Pattern Recogn. 44(8), 1761–1776 (2011).

    Article  Google Scholar 

  30. Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, Long Beach, CA, USA, pp. 4765–4774 (2017)

    Google Scholar 

  31. The Constitution of India 1950, Pt VI, Ch 4 and 5

    Google Scholar 

  32. Share of case clearance rate in subordinate courts across India in the financial year 2019, by State. Stastista (2019).

  33. India Justice Report 2022, Sir Dorabji Tata Trust, 89–92, 97–99 (2023).

  34. Kaul, A., et al.: Deconstructing Delay: Analyses of Data from High Courts and Subordinate Courts (2023).

  35. Pratik Dutta and Suyash Rai: How to Start Resolving the Indian Judiciary’s Long-Running Case Backlog (2021).>; National Judicial Data Grid: Evidence/Argument/Judgement Wise Pendency,

  36. The national average of women’s representation in the lower judiciary is 35%, see India Justice Report 2022, 15, 94 (2023)

    Google Scholar 

  37. Tannvi, T., Narayana, S.: The challenge of gender stereotyping in Indian courts. Cogent Soc. Sci. 8, 1 (2022)

    Google Scholar 

  38. Pragati K.B.: Skewed Corridors of Justice: Women Continue to Face Sexism in Courts. The Wire (2019).

  39. Vidhi Centre for Law & Policy Search. Tilting the Scale (2018).

  40. Satish, M.: Discretion. Discrimination and the Rule of Law: Reforming Rape Sentencing in India. Cambridge University Press; Gender Diversity Portal, Vidhi Centre for Legal Policy (2016).

  41. Cerda, P., Varoquaux, G.: Encoding high-cardinality string categorical variables. IEEE Trans. Knowl. Data Eng. 34(3), 1164–1176 (2020)

    Google Scholar 

  42. Hancock, J.T., Khoshgoftaar, T.M.: Survey on categorical data for neural networks. J. Big Data 7(1), 1–41 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Mohit Bhatnagar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bhatnagar, M., Huchhanavar, S. (2024). Predicting Delays in Indian Lower Courts Using AutoML and Decision Forests. In: García Márquez, F.P., Jamil, A., Ramirez, I.S., Eken, S., Hameed, A.A. (eds) Computing, Internet of Things and Data Analytics. ICCIDA 2023. Studies in Computational Intelligence, vol 1145. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53716-5

  • Online ISBN: 978-3-031-53717-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics