Research program · Trustworthy machine learning
Building trustworthy machine learning for real-world deployment
Modern AI systems achieve excellent benchmark performance, yet fail in structured, predictable ways once deployed. I build the audits that expose those failures — across clinical risk, lending, language, and markets — and the evaluation infrastructure that anticipates them.
- Trustworthy AI
- Deployment Shift
- Fairness
- Calibration
- External Validity
- Interpretability
Three findings, one program
Research vision
A model can be accurate, and the decision it drives can still be unfair, miscalibrated, and invalid for the people it meets in deployment. My research asks the question benchmarks skip: not “is it accurate?” but “does the decision remain fair, calibrated, externally valid, private, and economically deployable — under real constraints?”
- External validitydoes it survive a new population?
- Subgroup fairnesswho pays for the average?
- Calibrationare the probabilities honest?
- Shift diagnosiswhich failure, before labels arrive?
- Deployment disciplinewhen should the gate stay closed?
Flagship research · Under review — Applied Intelligence
TrustShift
One pre-registered audit across four dissimilar domains: the type of distribution shift — not its magnitude — determines which axis of trustworthiness fails at deployment.
- 01Deploymentmodel meets a shifted world
- 02Diagnosis3 label-free shift probes
- 03Failure taxonomywhich axis breaks, and why
- 04Audit → remediationrecalibrate · reweight · retrain
The claim
Shift type, not shift magnitude, predicts which trustworthiness axis degrades — and the responsible shift type is diagnosable in advance from unlabeled data, mapping each diagnosis to the appropriate remediation: recalibration, reweighting, or retraining.
- 4domains, one pre-registered protocol
- 3label-free shift probes
- N=2000bootstrap CIs, BH-FDR controlled
- exit 0full pipeline re-run, byte-identical tables
Research trajectory
Every question was earned by the one before it
- Early 2025Healthcare AI
Does a model that passes internal validation survive a new population?
Clinical ML meets external validation
Diabetes risk models built on NHANES are stress-tested on 1.28M BRFSS records. External AUC falls 9.7% — and the elderly lose the most. First evidence that the benchmark hides the failure.
- 2025Federated Learning
Can privacy-preserving training also be fair, calibrated, and generalisable?
Privacy joins the audit
The same clinical problem, restructured across heterogeneous hospital nodes. Federated training generalises better than centralized — but differential privacy collapses at realistic budgets. Four axes, audited together.
- 2025NLP · Fairness
Does fairness itself survive when the deployment platform changes?
Fairness fails under platform shift
Mental-health classifiers at AUC 0.98 collapse on Reddit and Twitter — calibration, equity, and even the models' explanations degrade. The audit grows a fifth axis: attribution stability.
- 2025–2026Causal Inference
Who actually bears an average disparity — and through what mechanism?
Causal structure of a 42M-application gap
Mortgage lending at national scale: the racial approval gap is quantified, bounded, and traced to a lender-controlled mechanism — manual underwriting more than doubles the penalty of automated systems.
- 2026LLM Evaluation
Can frontier LLMs reason over a non-Western regulatory rulebook?
LLMs meet an underrepresented jurisdiction
IndiaFinBench: 406 expert-annotated questions over SEBI/RBI regulation. Numerical reasoning splits frontier models by 36 points. Evaluation infrastructure becomes a public artifact.
- 2026Deployment Gates
Should a model be deployed when it cannot prove predictive skill?
The discipline to say no
A statistical deployment gate for financial ML stays closed across all 12 folds — published as a null result. The model stays near-perfectly calibrated with zero predictive skill — calibration is not deployment readiness.
- Mid 2026Capstone
Is deployment failure predictable before it happens?
TrustShift — the program becomes a theory
One pre-registered protocol across clinical, NLP, lending, and security domains: shift type, not shift magnitude, determines which trustworthiness axis fails — and cheap label-free probes can diagnose it in advance.
- NextFuture
Can evaluation itself become deployment infrastructure?
Audit infrastructure as a field
Shift-type diagnosis before deployment, runtime audit for generative systems, and evaluation that treats fairness, calibration, and validity as one object — not three papers.
Research map
One ecosystem, not a list of results
Hover or tap a theme — TrustShift binds them all
Publications & manuscripts
Eight studies, one argument
TrustShift: Shift Type, Not Shift Magnitude, Determines Machine-Learning Failure Modes
One pre-registered audit across four dissimilar domains: the type of distribution shift — not its magnitude — determines which axis of trustworthiness fails at deployment.
- Trustworthy ML
- Deployment Shift
- Healthcare AI
Who Bears the Burden? Heterogeneous Racial Approval Differentials in U.S. Mortgage Lending: Causal Forest DML on 42 Million HMDA Applications
Not whether an average penalty exists, but who bears it — and through which underwriting channel.
- Causal Inference
- Fairness
- Trustworthy ML
Persistent Racial Disparities in U.S. Mortgage Approval: Evidence from 42 Million Applications, 2020–2024
The public-data Black–White mortgage approval gap at national scale: how large, where it lives, and how it responds to institutional boundaries.
- Fairness
- Causal Inference
- Deployment Shift
IndiaFinBench: An Evaluation Benchmark for Large Language Model Performance on Indian Financial Regulatory Text
The first expert-annotated benchmark for LLM reasoning over Indian financial regulation — where numerical thresholds and amendment chains break strong models.
- NLP · LLMs
- Trustworthy ML
- Financial ML
Privacy-Preserving Federated Learning for Diabetes Risk Prediction Across Demographically Heterogeneous Hospital Nodes
Federated diabetes screening audited on four deployability axes at once — generalisation, fairness, calibration, and privacy — not just accuracy.
- Healthcare AI
- Privacy · Federated
- Fairness
Comprehensive Evaluation of Machine Learning for Type 2 Diabetes Risk Prediction: Large-Scale External Validation and Fairness Analysis
Internally validated diabetes models lose discrimination and fairness when externally validated on 1.28M records — age subgroups suffer most.
- Healthcare AI
- Fairness
- Deployment Shift
Cross-Platform Generalisation Failure in Mental Health NLP: A Five-Axis Fairness Audit of Transformer Models on Social Media
Mental-health classifiers that look near-perfect in-platform collapse across platforms — on discrimination, calibration, equity, and even their explanations.
- NLP · LLMs
- Fairness
- Healthcare AI
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
A deployment gate for financial ML — and the discipline to report that it stayed closed.
- Financial ML
- Deployment Shift
- Trustworthy ML
Research infrastructure
One reproducible ecosystem
The research ships as usable infrastructure: every manuscript regenerates its numbers from result files, and the datasets, benchmarks, and audit libraries are public, documented, and built for other people to reuse.
- Benchmark · Reproduction
trustshift
Cross-domain deployment-shift audit: full pipeline, prediction parquets, result JSONs. Fresh-clone reproduction verified byte-identical.
- Dataset · Live demo
IndiaFinBench
406 expert-annotated regulatory QA items, leaderboard for 12 LLMs, and a deployed hybrid-RAG open-book system.
- Python library
fairscope
Subgroup-stratified, calibration-aware fairness auditing: DeLong CIs per subgroup, subgroup ECE/MCE, gap significance testing, recalibration — with healthcare, NLP, federated, and lending modules.
- Runtime audit · Dataset
aria-audit
Five-axis runtime fairness audit for locally deployed LLMs — calibration, faithfulness, consistency, equity, attribution — wrapping any model on consumer hardware.
- Research code
Reproducibility repos
Every manuscript ships a pipeline that regenerates its numbers from result files — federated diabetes, CATE-HMDA, ICGDF and more.
Research output
- 8research manuscripts — 1 presented, 7 under review or working
- 4domains bound by one audit protocol
- 42Mmortgage applications analysed
- 1.28Mrecords in external clinical validation
- 406expert-annotated benchmark items
- 3public datasets on Hugging Face
The next five years
Questions that remain open
- Q1
Can deployment failure be diagnosed before deployment?
TrustShift shows shift type is diagnosable from unlabeled data in four domains. The next step is a general theory: which probe families predict which failure axes, with what guarantees, and where the mapping breaks.
- Q2
What does runtime auditing look like for generative systems?
Classifier audits assume fixed outputs. Free-form generation needs live instrumentation — calibration of verbalized confidence, faithfulness, counterfactual equity — cheap enough to run beside every response on consumer hardware.
- Q3
Can evaluation itself become infrastructure?
Fairness, calibration, and external validity are still bolted on paper by paper. The goal is evaluation as a first-class system: pre-registered, subgroup-aware, shift-aware — as standard as a test suite in software.
About
I am Rajveer Singh Pall, an AI researcher studying trustworthy machine learning under real-world deployment. B.Tech Computer Science & Business Systems, GGITS, expected 2027. Current work: cross-domain deployment-shift auditing (TrustShift) and runtime audit infrastructure for locally deployed LLMs. I am applying to graduate research programs for Fall 2027.