Publications and manuscripts

Twelve studies on one question: whether machine learning results mean what they claim. 1 published, 5 under review, 2 working papers, 3 manuscripts and 1 in preparation. Every status is stated exactly and never upgraded.

  1. Under review2026

    Benchmark Accuracy Is Not an Identified Quantity

    Rajveer Singh Pall

    Most orderings on a published MATH-Hard leaderboard cannot be separated once the responses a scorer could not read are counted.

    • Benchmark Evaluation
    • NLP · LLMs
    • Deployment Shift
  2. Under review2026

    Higher AUC, Fewer Cases Flagged: Subgroup Fairness Metrics Can Reverse at the Decision Threshold in Federated Diabetes Screening

    Rajveer Singh Pall

    Federated training narrows the White-Black AUC gap while a race-blind screening policy flags fewer Black respondents.

    • Fairness
    • Healthcare AI
    • Privacy · Federated
  3. Under review2026

    Benchmarks Do Not Report Whether They Could Read the Answer

    Rajveer Singh Pall

    Two answer extractors shipped in the same evaluation harness disagree by 88.5 points on the same 400 MMLU responses.

    • Benchmark Evaluation
    • NLP · LLMs
  4. Under review2026

    Scoring-Rule Sensitivity in LLM Evaluation: Evidence from Indian Financial Regulatory Text

    Rajveer Singh Pall

    Changing only the scoring rule reorders twelve LLMs on a new Indian financial-regulation benchmark, and no model keeps its rank.

  5. Published · IEEE Xplore (CIPHER-2026)2026

    Comprehensive Evaluation of Machine Learning for Type 2 Diabetes Risk Prediction: Large-Scale External Validation and Fairness Analysis

    Rajveer Singh Pall, Sameer Yadav, Siddharth Bhalerao, Sourabh Sahu, Ritu Ahluwalia, Bhaskar Awadhiya

    Internally validated diabetes models lose discrimination on 1.28M external records, and adults over 60 lose the most.

    • Healthcare AI
    • Fairness
    • Deployment Shift
  6. Manuscript2026

    TrustShift: A Mechanism-Aware Audit of Machine-Learning Deployment Shift

    Rajveer Singh Pall

    Across four real deployment shifts, shift magnitude alone does not consistently explain which trustworthiness axis fails.

  7. Manuscript2026

    Confidently Wrong: Ranking Inversion in Cross-Network Denial-of-Service Detection

    Rajveer Singh Pall

    On a new network, DoS detectors do not decay toward chance: many pass through it and score attacks below benign traffic.

    • Network Security
    • Deployment Shift
  8. In preparation2026

    Scorer-Induced Partial Identification of Benchmark Comparisons: An Audit-Design Approach

    Rajveer Singh Pall

    Once a scorer’s error rate is audited rather than assumed, audit uncertainty outweighs sampling uncertainty in all 120 well-defined comparisons.

  9. Under review · Journal of Housing Economics2026

    Persistent Racial Disparities in U.S. Mortgage Approval: Evidence from 42 Million Applications, 2020-2024

    Rajveer Singh Pall

    The public-data Black-White mortgage approval gap at national scale: how large, where it lives, and how it responds to institutional boundaries.

  10. Working paper · SSRN2026

    Who Bears the Burden? Heterogeneous Racial Approval Differentials in U.S. Mortgage Lending: Causal Forest DML on 42 Million HMDA Applications

    Rajveer Singh Pall

    Not whether an average penalty exists, but who bears it, and through which underwriting channel.

  11. Working paper · SSRN2026

    When the Gate Stays Closed: Empirical Evidence of Near-Zero Cross-Sectional Predictability in Large-Cap NASDAQ Equities Using an IC-Gated Machine Learning Framework

    Rajveer Singh Pall

    A deployment gate for financial ML, and the discipline to report that it stayed closed.

  12. Manuscript2026

    Text Genre, Not Platform Identity, Predicts Transfer Failure in Mental Health Natural Language Processing: A Five-Axis Deployment Audit Across Five Corpora

    Rajveer Singh Pall, Sameer Yadav

    A five-axis pre-deployment audit of mental-health text classifiers moved across platforms and corpora.

    • NLP · LLMs
    • Fairness
    • Healthcare AI