Content

1. Walking back on hard-won rights 
2. AI-powered tax governance in India and its challenges

Walking back on hard-won rights 

Context

• Introduced on 13 March 2026 in Lok Sabha, the Bill amends the 2019 Act, raising concerns over restrictive definition, medical certification, and rollback of self-identification rights, criticised by experts and rights advocates.

Relevance

• GS 1 (Indian Society):
  • Gender identity, social inclusion of transgender community
  • Stigma, marginalisation, and intersectional vulnerabilities
• GS 2 (Polity & Governance):
  • Fundamental Rights (Articles 14, 15, 19, 21)
  • Judicial activism vs legislative rollback
  • Welfare policies and identity certification mechanisms
• GS 3 (Social Justice / Governance):
  • Inclusion in Census, targeting welfare schemes
  • State capacity and institutional delivery

Practice Question

Q. “The proposed Transgender Persons (Protection of Rights) Amendment Bill, 2026 marks a shift from a rights-based to a regulatory approach.” Critically examine in light of constitutional morality and global standards.(250 Words)

Static background 

Constitutional & judicial foundation

• NALSA v. Union of India (15 April 2014) recognised self-identification of gender as a fundamental right under Articles 14, 19, 21.
• Puttaswamy (2017) upheld privacy, dignity, and autonomy, directly applicable to gender identity and personal choices.
• Transgender Persons Act, 2019 provided framework for non-discrimination, identity certification, and welfare measures.

Key provisions of the Bill

Definition of transgender person

• Replaces broad definition with restricted category-based classification, excluding trans-men, trans-women, genderqueer, non-binary individuals, narrowing scope of recognition.
• Retains hijra, kinner, intersex categories, and adds eunuchs and forcibly transitioned persons, shifting focus from identity to coercion-based inclusion.

Recognition of identity

• Introduces mandatory medical board certification, replacing self-identification, increasing bureaucratic control and procedural barriers.
• Identity certificate issued by District Magistrate based on medical recommendation, undermining autonomy and dignity.

Change in gender

• Makes revised certificate mandatory after surgery, reducing individual agency.
• Requires medical institutions to report gender-affirming surgeries, raising concerns over privacy and doctor-patient confidentiality.

Offences and penalties

• Enhances punishments for forced transgender identity, exploitation, bonded labour, with penalties up to life imprisonment and ₹5 lakh fine.
• However, vague terms like “coercion” or “inducement” risk misuse and over-criminalisation.

Constitutional / legal analysis

• Violates NALSA (2014) by diluting self-identification principle, replacing it with medical validation.
• Contradicts Article 21 (privacy, dignity, autonomy) and Article 14 (equality) due to exclusionary definition.
• Raises issues of due process and arbitrariness, as medical verification lacks objective standards.

International / human rights dimension

• Violates Yogyakarta Principles (2006) which affirm right to self-defined gender identity without medical or legal coercion, forming global human rights benchmark.
• Contradicts WHO ICD-11 (2019), which recognises gender identity as non-pathological and not subject to medical diagnosis.
• Divergence from global norms may affect India’s human rights commitments and international credibility.

Governance / administrative dimension

• Medical boards create bureaucratic hurdles, delays, and discretion, increasing risks of exclusion and harassment.
• Lack of clear guidelines and trained personnel may result in inconsistent decision-making across districts.
• Reporting requirements introduce state surveillance over personal identity and healthcare choices.

Social / ethical dimension

• Leads to identity erasure of large sections of transgender community, especially non-binary and gender non-conforming individuals.
• Reinforces stigma and medicalisation, treating gender identity as a condition requiring validation.
• May discourage access to healthcare and welfare schemes, worsening marginalisation and vulnerability.

Data & evidence

• Census 2011 recorded ~4.9 lakh transgender persons, widely considered underestimation.
• High levels of discrimination (>90% employment exclusion) indicate need for inclusive, not restrictive, legal frameworks.

Challenges / criticisms

• Legal : High probability of constitutional challenge due to violation of Supreme Court judgments and fundamental rights.
• Institutional : Weak administrative capacity to implement medical verification framework effectively and sensitively.
• Social : Risk of exclusion from welfare schemes, identity documents, and public services, leading to increased marginalisation.

Way forward

• Restore self-identification principle in line with NALSA (2014) and Yogyakarta Principles.
• Replace medical boards with self-declaration + administrative verification, ensuring dignity and accessibility.
• Strengthen anti-discrimination enforcement, reservations, and welfare measures for transgender community.
• Ensure confidentiality and privacy safeguards in healthcare systems.
• Align law with international standards (WHO, Yogyakarta Principles) and constitutional morality.

Prelims pointers

• NALSA (2014) → self-identification of gender.
• Yogyakarta Principles (2006) → global human rights framework on gender identity.
• Puttaswamy (2017) → right to privacy.
• Transgender Act, 2019 → base legislation.
• Amendment Bill introduced: 13 March 2026.

AI-powered tax governance in India and its challenges

Context

• Editorial highlights AI-driven tax administration (Project Insight) amid concerns of low tax-GDP ratio (16.36%, 2001–22) and tax evasion (~4.3% revenue loss annually), discussed at India AI Impact Summit, February 2026.

Relevance

• GS 2 (Governance):
  • Administrative reforms in taxation
  • Transparency, accountability, and due process
• GS 3 (Economy & Science & Technology):
  • Tax-GDP ratio, revenue mobilisation
  • AI, big data, and digital governance
  • Formalisation of economy

Practice Question

Q. “AI-driven tax administration enhances efficiency but raises concerns regarding privacy, accountability, and fairness.” Analyse.(250 Words)

Static background

Tax-GDP ratio & fiscal context

• Tax-GDP ratio (~16.36%) remains low compared to emerging economies (~18–22%), indicating limited fiscal capacity and narrow tax base.
• High tax evasion (~4.3% revenue loss) undermines public expenditure, welfare financing, and fiscal consolidation efforts.

Project Insight (PI)

• Launched in 2017; operationalised in 2019 by Income Tax Department, aims to leverage AI, big data analytics, and behavioural insights for improving compliance and tax administration.
• Core objective: voluntary compliance, risk-based scrutiny, and fair enforcement, shifting from coercive to data-driven governance model.

Governance / administrative dimension

• INTRAC (Income Tax Transaction Analysis Centre) creates 360° taxpayer profiles using data from banks, GST, property, securities, high-value transactions, enabling risk-based assessment.
• Compliance Management Centralised Processing Centre uses NUDGE strategy (SMS/email alerts) to encourage correction of returns without coercion, improving trust-based compliance.
• Automation reduces administrative burden, allowing officers to focus on high-risk and complex tax evasion cases, improving efficiency.

Economic dimension

• Improved compliance increases tax buoyancy and revenue mobilisation, strengthening fiscal capacity for infrastructure, welfare, and capital expenditure.
• Since FY 2020–21, over 1 crore revised returns filed, yielding ₹11,000 crore additional revenue, reflecting success of behavioural compliance strategies.
• Detection of ₹70,000 crore suppressed turnover (restaurants) demonstrates potential of AI in uncovering large-scale evasion.

Technology dimension

• AI enables pattern recognition, anomaly detection, and predictive analytics, improving identification of high-risk taxpayers and evasion networks.
• Use of big data (financial transactions, digital payments, GST integration) enhances accuracy and reduces manual intervention.
• Smart systems (chatbots, automated filing support) improve taxpayer services, grievance redressal, and fraud prevention.

Social / ethical dimension

• Promotes fairness and equity by reducing discretionary enforcement and targeting high-risk evasion instead of blanket scrutiny.
• However, risks of algorithmic bias may disproportionately target certain regions, professions, or socio-economic groups, affecting equity.
• Ethical concern: transition toward surveillance-based tax system, potentially undermining trust and voluntary compliance culture.

Legal / constitutional dimension

• Raises concerns under Right to Privacy (Article 21, Puttaswamy 2017) due to large-scale data aggregation and profiling of taxpayers.
• Lack of transparency in algorithms challenges principles of natural justice (audi alteram partem) and due process in taxation.
• Absence of clear legal framework for AI accountability and explainability creates regulatory gaps in governance.

Data & evidence

• Tax-GDP ratio: 16.36% (2001–22 average).
• Tax evasion loss: ~4.3% annually.
• 1 crore revised returns → ₹11,000 crore additional tax.
• 62% compliance in foreign asset disclosure campaign.
• ₹70,000 crore evasion detected (restaurants).

Benefits / outcomes

• Enhances voluntary compliance through nudges, reducing need for coercive enforcement.
• Improves efficiency, speed (refund time: 93 → 17 days), and accuracy in tax administration.
• Strengthens risk-based targeting, reducing harassment of compliant taxpayers and improving ease of doing business.

Challenges / criticisms

Data & technical

• AI dependent on data quality and provenance; inaccurate or incomplete data may lead to false positives and wrongful scrutiny.
• Difficulty in distinguishing legitimate financial complexity vs tax evasion, especially in informal and family-based economic structures.

Algorithmic & ethical

• Algorithmic bias may replicate historical inequalities, as seen in global cases (e.g., Dutch benefits scandal).
• Lack of explainability prevents taxpayers from understanding decisions, undermining trust and accountability.

Legal & governance

• Absence of AI ombudsman, audit mechanisms, and transparency standards weakens oversight of algorithmic decision-making.
• Weak safeguards on data privacy and cybersecurity increase risk of breaches and misuse of sensitive financial information.

Way forward

• Establish AI governance framework in taxation, including algorithm audits, transparency norms, and explainability standards.
• Create independent AI ombudsman for grievance redressal and review of contested algorithmic decisions.
• Ensure human-in-the-loop decision-making for high-impact cases, preserving due process and fairness.
• Strengthen data protection safeguards under DPDP Act, 2023, ensuring privacy and security of taxpayer information.
• Promote capacity building in AI and data analytics within tax administration for effective and ethical implementation.

Prelims pointers

• Project Insight (2017) → AI-based tax compliance system.
• INTRAC → analytical engine of ITD.
• NUDGE strategy → behavioural compliance tool.
• Tax-GDP ratio → indicator of fiscal capacity.
• DPDP Act, 2023 → data protection framework.