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Fair procedure in digital governance: (a) Algorithmic decision-making challenges: (i) Opacity: 'Black box' algorithms hard to understand/challenge, (ii) Bias: Algorithms may perpetuate historical discrimination, (iii) Scale: Automated decisions affect millions; errors have widespread impact, (b) Fair procedure requirements for algorithmic decisions: (i) Explainability: Citizens entitled to understand basis of decision affecting rights (linked to right to information, reasoned orders), (ii) Human oversight: Critical decisions (welfare denial, law enforcement) require human review, appeal, (iii) Appeal mechanism: Opportunity to challenge algorithmic decision before human authority, (iv) Data quality: Decisions based on accurate, non-discriminatory data, (c) Legal basis: Article 14 (equality), Article 21 (fair procedure), DPDP Act (data protection) — interpreted to require procedural safeguards for algorithmic governance, (d) Applications: (i) Welfare eligibility: Algorithmic screening must allow appeal, human review, (ii) Policing: Predictive policing algorithms require transparency, oversight to prevent bias, (iii) Credit scoring: Financial algorithms must provide explanations, appeal routes, (e) Balance: Efficiency of automation vs. fairness of procedure; Constitutional Morality requires technology serve rights, not undermine them. Illustrates adaptive administrative law: applying enduring fairness principles to emerging technological contexts.