Delimitation and Gerrymandering in India

**Tejaswini Kaushal and Srija Singh

Introduction

“It’s not the voting that’s democracy; it’s the counting.”

                                                                — Tom Stoppard

Delimitation, the redrawing of electoral boundaries, plays a decisive role in how votes translate into representation. Debated worldwide, it determines who votes for whom and shapes how citizens are represented, whether in urban centers or remote villages. In India, delimitation seeks proportional representation while preventing gerrymandering, i.e., the manipulation of boundaries for partisan gain.

As noted by the Brennan Center for Justice, “The power to draw district lines is the power to shape democracy itself.” The central question is how to achieve fairness and accuracy in this process. Can technology deliver on democracy’s promise? Tools such as Artificial Intelligence (AI) and Geographic Information Systems (GIS) can reduce human bias and improve precision, yet they also risk replicating existing biases within data and algorithms. This article explores the limitations of technology in delimitation, examines comparative practices in the United States (US), and proposes ways to refine its use in India. 

Delimitation and Gerrymandering: Challenges in India’s Electoral Landscape

“Redistricting is one of the purest actions a legislative body can take.” 

— John Engler

1. Delimitation: Redrawing Democracy

Delimitation refers to the redrawing of electoral constituencies to ensure representation aligns with population realities, as mandated by the Delimitation Act, 2002, and Articles 82 and 170 of the Constitution. Conducted decennially alongside the Census, it is executed by the Delimitation Commission of India, constituted by the Government under the respective Act and functioning under the President’s supervision. While the Commission’s independence and the process of public consultation initially insulated it from judicial scrutiny, recent rulings have clarified that courts can intervene where mala fide or arbitrary action is evident. In Dravida Munnetra Kazhagam v. State of Tamil Nadu, the Court affirmed judicial review to preserve constitutional accountability, and in Kishorchandra Chhanganlal Rathod v. Union of India (2024), the Supreme Court held that Article 329 does not absolutely bar review of delimitation orders. Given that the last exercise occurred in 2002 and the 2021 Census was delayed, the upcoming 2026 delimitation holds exceptional importance. The National Commission to Review the Working of the Constitution (2002) warned that the 1976–2001 freeze widened representational disparities, and current debates on southern states’ population control success versus northern growth underscore emerging inequities. The primary objective remains to uphold political equality through the principle of “one person, one vote,” ensuring no constituency is over- or underrepresented.

2. Gerrymandering: A Case of ‘Lizard’ Politics

Gerrymandering refers to the deliberate manipulation of electoral boundaries to benefit a political party or group, undermining democratic representation. The term originated from Massachusetts Governor Elbridge Gerry’s 1812 redistricting map, whose salamander-like shape inspired the term “Gerry-Mander,” first published in the Boston Weekly Messenger. Two main forms exist: ‘packing,’ which concentrates supporters of one group into a few constituencies, and ‘cracking,’ which disperses them across several to dilute their influence. Though more prominent in the US, India too has witnessed boundary manipulations in politically crucial states to secure advantages in the Lok Sabha elections. A Forbes India investigation identified constituencies with irregular, non-compact shapes, violating the principle of geographical cohesion, with Madhya Pradesh and Uttar Pradesh among the most gerrymandered, and West Bengal and Assam the least. The report strikingly notes that Padmanabhanagar in Bangalore South resembles “a hen doing ballet” (Image 1).

Image 1

[Source: Forbes India Investigation: India’s most gerrymandered constituencies]

In Haji Abdul Gani Khan v. Union of India (2023), the Supreme Court emphasized that delimitation must not erode federalism or equality, reinforcing the need for safeguards against gerrymandering as India reexamines its delimitation framework alongside comparative insights from other jurisdictions.

3. Research Methodology: A Cross-Jurisdictional Comparative Analysis

The authors adopt a comparative approach examining the delimitation frameworks of India and the USA, with supporting insights from Canada, Australia, and the United Kingdom. These democracies differ in their use of technology, public participation, and institutional mechanisms for redistricting. In the USA, state legislatures primarily oversee redistricting, with limited public access leading to frequent litigation by civil rights groups and a reliance on courts for accountability. In contrast, Canada’s Electoral Boundaries Readjustment Act, 1985, mandates public hearings and submissions, fostering transparency. Australia’s two-tiered consultation system seeks input before and after mapping, ensuring public engagement and limiting legislative interference. The UK allows public inquiries if objections arise, promoting local accountability, though often causing procedural delays. These distinctions allow categorizing the USA separately from India, Australia, and the UK, as the latter group embeds stronger participation mechanisms and uses independent commissions, resulting in fewer litigations and reduced partisan bias. Despite India’s quasi-judicial Delimitation Commission ensuring independence, political influence persists, as noted by E. Sridharan. Both groups, however, increasingly deploy technology such as GIS and AI in delimitation, raising shared concerns over algorithmic bias and technological adequacy in ensuring fairness and preventing gerrymandering. 

Wired for Fairness?: Leveraging Technology to Address Gerrymandering

1. Bytes and Boundaries: A Tech Takeover of Delimitation

“Any sufficiently advanced technology is equivalent to magic.” 

                                                             — Arthur C. Clarke

Technology has transformed redistricting into an evidence-based, transparent exercise. Firstly, GIS enables spatial and demographic analyses to ensure population equality, contiguity, and compactness, reducing political manipulation by basing boundaries on objective data. Secondly, AI and Big Data analytics enhance fairness by minimizing human biases, integrating vast voter datasets, and using predictive models to simulate outcomes under various scenarios. These tools together standardize delimitation, promote transparency, and provide a scientific method to detect and prevent gerrymandering before boundaries are finalized.

2. Empirical Studies: Lines of Code Removing Lines of Bias

Empirical evidence affirms the effectiveness of GIS and related technologies in delimitation. A 2023 Centre for Policy Research study found that GIS-based delimitation in India reduced boundary disputes by 25% and improved mapping of marginalized communities for inclusive representation. In the UK, Oxford University research showed that machine learning can predict “packing” and “cracking” patterns of gerrymandering, while the Brennan Center for Justice in the USA reported GIS-driven transparency reduced partisan bias. Tools like Tufts University’s GerryChain and Harvard’s ALARM use algorithm-assisted models to identify gerrymandered maps, with widespread public adoption reshaping citizen participation. Additionally, Duke University’s work on Markov Chain Monte Carlo algorithms promises greater automation in fair redistricting. Internationally, the United Nations’ GIS-based UNIBIS system and maritime technologies like ECDIS have successfully resolved disputes, including those between Cameroon–Nigeria and Eritrea–Ethiopia. In India, GIS adoption since the 1980s, bolstered by the 2007 Second Administrative Reforms Commission and 2011 Justice Kuldip Singh Committee recommendations, has advanced transparent delimitation. The 2014 Kerala pilot using QField and ongoing ECI–NIC GIS studies in Assam and Jammu & Kashmir exemplify progress in addressing underrepresentation. Despite differing institutional models across India, the USA, the UK, Canada, and Australia, all increasingly rely on GIS, AI, and Big Data analytics to promote fairness, transparency, and algorithmic accountability in redistricting.

Who will Watch The Watchman?: Why Technology Alone Cannot Eradicate Gerrymandering

1. Paradox of Progress: The Double-Edged Sword of Technology

“We set sail on this new sea because there is new knowledge to be gained, and new rights to be won, and they must be won and used for the progress of all people.”

— John F. Kennedy

Empirical research highlights that computational redistricting models remain highly vulnerable to bias and manipulation. Studies such as Fifield et al. (2018) on Markov Chain Monte Carlo simulations, Guest et al. (2019) on algorithmic bias in redistricting, and Wayland (2022) on simulated annealing reveal that outcomes depend heavily on user-defined parameters and constraints. Benadè, Procaccia, and Tucker-Foltz (2023) introduced the geometric target fairness constraint, while Deshpande, Ludden, and Jacobson’s votemandering model (2023) showed how skewed data analytics can fuel partisan manipulation. Veomett (2024) demonstrated the limits of symmetry metrics such as Mean-Median Difference and Partisan Bias, and Swan (2024) exposed how “race-blind” algorithms perpetuate minority disenfranchisement. Collectively, these works show that technology is not inherently impartial the integrity of input data and developer intent largely determine outcomes, often masking political bias under a veneer of objectivity. Legal frameworks have not kept pace: India currently relies on the Digital Personal Data Protection Act, 2023, and forthcoming DPDPA Rules, 2025, while AI regulation remains absent despite MEITY’s 2025 Subcommittee Report calling for oversight. By contrast, the EU AI Act, 2024, and the US Algorithmic Accountability Act, 2023, impose controls on algorithmic bias. India must similarly enact AI-specific legislation to ensure transparent, equitable, and accountable delimitation in the algorithmic era.

The American Outlook

1. The Issues

The US has a fragmented legal framework for addressing gerrymandering, with no constitutional provision for district delimitation. In Baker v. Carr, the Supreme Court applied the Equal Protection Clause to mandate population equality across districts, and the Voting Rights Act, 1965 was enacted to prevent racial disenfranchisement. However, while racial gerrymandering has been judicially addressed, partisan gerrymandering remains unregulated; in Rucho v. Common Cause, the Court deemed it non-justiciable, leaving reform to Congress or state courts, while the For the People Act stalled in the Senate. In Moore v. Harper, the Court rejected the independent state legislature theory, reaffirming judicial checks but leaving scope for future challenges. Without robust safeguards, advanced redistricting software risks entrenching political bias. Algorithmic redistricting faces two main issues: biased datasets and manipulable parameters that allow partisan outcomes to appear legally neutral. Maptitude for Redistricting exemplifies this duality, though marketed as promoting balanced districts using GIS and demographic integration, it has facilitated legally compliant yet politically skewed maps, as seen in North Carolina, Virginia, Michigan, and Ohio during the 2012 elections, where Republicans gained disproportionate representation through algorithm-aided manipulation. 

2. The Solutions

“The situation has changed in the last decade, as computer technology has caught up with the problem that it spawned.” 

— Eric Lander

Advances in computer technology have simultaneously enabled, exposed, and mitigated gerrymandering. Tools like the extreme-outlier test now use Markov Chain Monte Carlo algorithms to compare suspected partisan maps against thousands of neutral ones generated under criteria such as compactness, contiguity, population equality, and compliance with the Voting Rights Act, 1965. These “ensemble maps” serve as benchmarks to identify bias, transforming the same algorithms once used for manipulation into anti-gerrymandering safeguards. While technology can make gerrymandering more sophisticated, its democratized use, anchored in transparency, algorithmic fairness, and public participation, offers a powerful path to strengthening accountability and safeguarding electoral integrity in the US and other democracies like India. 

Lessons for India

“A functioning, robust democracy requires a healthy, educated, participatory followership, and an educated, morally grounded leadership.”

— Chinua Achebe

While technology has improved redistricting over manual methods, true impartiality remains unrealized. Drawing from empirical studies and the U.S. experience, bias in redistricting arises mainly from two factors: flawed algorithms and compromised data integrity. To address these, two key reforms are essential. Firstly, algorithms must undergo rigorous design, testing, and auditing to ensure transparency, minimize human bias, and make their functioning publicly verifiable. Secondly, data collection must be safeguarded from political interference to preserve accuracy and neutrality in inputs. Without these safeguards, technological sophistication alone cannot guarantee fairness. The authors thus propose a two-pronged reform combining algorithmic transparency and data integrity to achieve equitable and credible delimitation in India.

1. Adoption of Periodic Delimitation over Post-Census Delimitation

Delimitation is critical as it determines electoral representation at all levels, yet India’s reliance on the decennial census creates outdated constituencies that fail to reflect shifting populations caused by births, deaths, and migration. The authors propose adopting models used in countries like the USA, Japan, Kenya, Lesotho, and Malaysia, where redistricting occurs periodically, independent of census cycles, to ensure responsiveness to demographic changes. In India, the delay of the 2021 Census and dependence on 2011 data undermine fair representation and policy decisions. The Women’s Reservation Bill’s implementation, tied to delimitation, remains stalled due to this delay, while states such as Assam, Nagaland, and Jammu & Kashmir have seen postponements due to security or political issues. A fixed and fair periodic redistricting schedule, with reasonable contingencies, is needed to avoid governance stagnation. Global examples offer valuable lessons: South Africa’s Independent Electoral Commission used technology to craft inclusive post-apartheid constituencies; New Zealand integrates population, geography, and community interests; and Australia relies on population projections to maintain equality throughout cycles. India should incorporate geographic features, compactness, and population density into its delimitation framework, similar to the criteria used in nations like Barbados, Belarus, and the Dominican Republic. A balanced approach integrating technology, periodic reviews, and multi-factor criteria will ensure fairness, inclusivity, and trust in India’s democratic representation.

2. Enhancing Public Transparency Mechanisms

Enhancing public participation in delimitation can significantly improve accountability and fairness. Public hearings, consultations, and greater data transparency are essential to inclusive redistricting. In the US, despite long-standing reliance on technology, opacity and algorithmic bias persist, prompting former US Attorney General Eric Holder at the American Association of Geographers (AAG) Annual Meeting to emphasize combining AI and GIS with transparency, public involvement, and statistical fairness. The Esri Redistricting Application, a Software-as-a-Service (SaaS) tool, exemplifies this by giving governments, advocacy groups, and citizens access to interactive GIS maps to visualize, adjust, and analyze boundaries collaboratively. India can adopt similar tools to strengthen existing transparency mechanisms, such as the public scrutiny of draft reports from the Delimitation Commission. However, two major challenges remain. Firstly, limited public access to raw demographic data restricts meaningful scrutiny, as only draft reports are publicly released. Secondly, illiteracy undermines citizens’ ability to interpret and engage with such data, a concern repeatedly recognized by the Supreme Court of India in T.M.A. Pai Foundation v. State of Karnataka (2002), Sadiq Ali v. Election Commission of India (1972), and Sun Pharmaceutical Laboratories Ltd. v. Hetero Healthcare Ltd. (2022). While literacy reform requires long-term effort, immediate steps can expand public access to raw data, methodologies, and census inputs. Combined with civic initiatives and collaboration between government and non-governmental organizations, enhanced transparency and public literacy represent crucial steps toward equitable, accountable, and inclusive delimitation in India.

Conclusion

Delimitation and gerrymandering are not mere administrative processes but reflect the core democratic principle of fair representation. In India, technology now plays a pivotal yet paradoxical role; it offers precision and efficiency in redistricting but also risks reinforcing existing biases embedded in datasets or design. The current framework’s over-reliance on the census cycle and lack of transparent, citizen-centric participation reveal ethical and institutional shortcomings beyond technical flaws. To address these, systemic reforms must integrate accountability, inclusivity, and fairness as guiding principles. Technology should function not as a panacea but as part of a comprehensive reform strategy ensuring transparency in data handling and algorithmic processes. By embedding transparent technological frameworks into its delimitation regime, India can move beyond preventing gerrymandering toward dismantling structural inequities, creating a model of equitable and accountable democratic governance.

**This piece is the winning article for the DD Basu Essay Competition 2024-2025, which was organised in collaboration with the WB National University of Juridical Sciences and Vidhi Centre for Legal Policy

**Tejaswini Kaushal is a student at Dr. Ram Manohar Lohiya National Law University, Lucknow

**Srija Singh is a student at Amity Law School, Noida.

**Disclaimer: The views expressed in this blog do not necessarily align with the views of the Vidhi Centre for Legal Policy.