Responsible AI | Muhammad Aurangzeb Ahmad

Overview

AI systems trained on historical data inherit historical inequities. When those systems are deployed at clinical scale, predicting which patients need intervention, which populations carry elevated risk, which deterioration alerts should fire, the hidden assumptions scale with them. Responsible AI is the discipline that makes those assumptions visible: auditing who the system fails, why it fails them, and whether we can explain and correct it before the harm compounds.

Working with a large multi-state health system in the United States, we deployed a model to predict care deferral: patients who would cancel or fail to appear for scheduled appointments (Ahmad et al., 2022). The model performed well by standard accuracy measures. But when we disaggregated its predictions by race and socioeconomic status, a pattern emerged — patients flagged as high-risk for deferral were disproportionately racial minorities. The algorithm had correctly identified a disparity. It could not explain one.

What happened next required something no model can do on its own. Working alongside clinicians, care coordinators, and community health workers, we set out to understand why these patients were deferring. What emerged was not a story about disengagement. These patients were primary caregivers — for children, for elderly parents, for partners with chronic illness — who could not take a half-day from work for an appointment. They were navigating scheduling systems built around a workday that assumed flexibility they did not have.

The solution was a mobile clinic: bringing care to the patients rather than requiring patients to come to the clinic. The program achieved a 12% reduction in deferral rate disparity across demographic groups, narrowing a gap that the model had identified but could not close on its own. The lesson was precise: machine learning can identify who is being left behind. Closing that gap requires understanding why — and that requires talking to people.

My research in this area spans three interconnected problems. First, fairness: AI systems trained on historical data inherit historical biases, and I study how those biases manifest across race, gender, age, and socioeconomic status in high-stakes settings, particularly healthcare (Ahmad et al., 2020; Ahmad et al., 2021). Second, explainability: a model that clinicians cannot understand is a model they cannot trust or correct, and I develop methods that make machine learning legible to the people who depend on it (Ahmad et al., 2018; Ahmad et al., 2019). Third, trustworthy LLMs: large language models introduce new failure modes (hallucination, confident error, catastrophic forgetting) that are particularly dangerous in clinical settings, and I am working on frameworks to detect and reduce them (Ahmad et al., 2023).

This work is motivated by a conviction: AI systems do not just reflect the world as it is. They shape the world as it will be. It has shaped regulatory frameworks for clinical AI (Ahmad et al., 2021), model reporting standards (Ahmad & Eckert, 2023), and research into algorithmic fairness in end-of-life decisions (Ahmad, 2025) and robust clinical decision support under uncertainty (Preuett et al., 2025). I am part of the Responsible AI Systems and Experiences (RAISE) initiative at the University of Washington. Building them responsibly is not a constraint on progress: it is what progress requires.

Research Areas

Algorithmic Fairness

Auditing ML models for disparate outcomes across demographic groups. Developing metrics and mitigation strategies for bias in healthcare prediction, criminal justice, and resource allocation.

Explainability & Interpretability

Building ML models that are transparent and understandable to clinicians, regulators, and patients. Identifying failure modes of post-hoc explanation methods, including imputation artifacts in SHAP and LIME.

Trustworthy Large Language Models

Addressing hallucination, confident error, and catastrophic forgetting in LLMs deployed in high-stakes clinical environments. Developing evaluation frameworks for LLM reliability in medicine.

AI Regulation & Governance

Examining how AI-based clinical tools should be classified and regulated as software medical devices. Engaging with FDA frameworks, international standards, and the ethics of algorithmic accountability at national scale.

Open-Source Software

Python · Open Source · KenSci Research

fairMLHealth

An open-source Python toolkit for measuring and reporting algorithmic fairness in clinical AI. Developed at KenSci Research, fairMLHealth provides a comprehensive suite of fairness metrics (statistical parity, equalized odds, calibration parity, predictive parity) together with bias detection utilities and standardized fairness reports designed for healthcare auditing contexts. The toolkit is built to work with common ML frameworks and is intended to make fairness evaluation a routine part of the model development lifecycle, not an afterthought.

GitHub →

Selected Publications

2025

arXiv
Algorithmic Fairness in AI Surrogates for End-of-Life Decision-Making

Muhammad Aurangzeb Ahmad

2025

Abs arXiv Bib HTML PDF

Artificial intelligence surrogates are systems designed to infer preferences when individuals lose decision-making capacity. Fairness in such systems is a domain that has been insufficiently explored. Traditional algorithmic fairness frameworks are insufficient for contexts where decisions are relational, existential, and culturally diverse. This paper explores an ethical framework for algorithmic fairness in AI surrogates by mapping major fairness notions onto potential real-world end-of-life scenarios. It then examines fairness across moral traditions. The authors argue that fairness in this domain extends beyond parity of outcomes to encompass moral representation, fidelity to the patient’s values, relationships, and worldview.
@misc{ahmad2025algorithmic, category = {selected_research}, title = {Algorithmic Fairness in AI Surrogates for End-of-Life Decision-Making}, author = {Ahmad, Muhammad Aurangzeb}, year = {2025}, }
RLC
Reinforcement Learning under State and Outcome Uncertainty: A Foundational Distributional Perspective

Larry Preuett, Qiuyi Zhang, and Muhammad Aurangzeb Ahmad

In Reinforcement Learning Conference (RLC), 2025

Abs arXiv Bib HTML PDF Code

In many real-world planning tasks, agents must tackle uncertainty about the environment’s state and variability in the outcomes of any chosen policy. We address both forms of uncertainty as a first step toward safer algorithms in partially observable settings. Specifically, we extend Distributional Reinforcement Learning (DistRL) — which models the entire return distribution for fully observable domains — to Partially Observable Markov Decision Processes (POMDPs), allowing an agent to learn the distribution of returns for each conditional plan. Concretely, we introduce new distributional Bellman operators for partial observability and prove their convergence under the supremum p-Wasserstein metric. We also propose a finite representation of these return distributions via psi-vectors, generalizing the classical alpha-vectors in POMDP solvers. Building on this, we develop Distributional Point-Based Value Iteration (DPBVI), which integrates psi-vectors into a standard point-based backup procedure — bridging DistRL and POMDP planning. By tracking return distributions, DPBVI naturally enables risk-sensitive control in domains where rare, high-impact events must be carefully managed.
@inproceedings{preuett2025distributional, category = {selected_research}, title = {Reinforcement Learning under State and Outcome Uncertainty: A Foundational Distributional Perspective}, author = {Preuett, Larry and Zhang, Qiuyi and Ahmad, Muhammad Aurangzeb}, booktitle = {Reinforcement Learning Conference (RLC)}, year = {2025}, }

2023

arXiV
Creating trustworthy llms: Dealing with hallucinations in healthcare ai

Muhammad Aurangzeb Ahmad, Ilker Yaramis, and Taposh Dutta Roy

arXiv preprint arXiv:2311.01463, 2023

Abs Bib PDF

Large language models have proliferated across multiple domains in as short period of time. There is however hesitation in the medical and healthcare domain towards their adoption because of issues like factuality, coherence, and hallucinations. Give the high stakes nature of healthcare, many researchers have even cautioned against its usage until these issues are resolved. The key to the implementation and deployment of LLMs in healthcare is to make these models trustworthy, transparent (as much possible) and explainable. In this paper we describe the key elements in creating reliable, trustworthy, and unbiased models as a necessary condition for their adoption in healthcare. Specifically we focus on the quantification, validation, and mitigation of hallucinations in the context in healthcare. Lastly, we discuss how the future of LLMs in healthcare may look like.
@article{ahmad2023creating, title = {Creating trustworthy llms: Dealing with hallucinations in healthcare ai}, author = {Ahmad, Muhammad Aurangzeb and Yaramis, Ilker and Roy, Taposh Dutta}, journal = {arXiv preprint arXiv:2311.01463}, year = {2023}, url = {https://arxiv.org/abs/2311.01463}, }
Surgical Clinics
Show Your Work: Responsible Model Reporting in Health Care Artificial Intelligence.

Muhammad Aurangzeb Ahmad, and Carly Marie Eckert

The Surgical Clinics of North America, 2023

Abs Bib HTML

Standardized and thorough model reporting is an integral component in the development and deployment of machine learning models in health care. Model reporting includes sharing multiple model performance metrics and incorporating metadata to provide the necessary context for model evaluation. Thorough model reporting addresses common concerns about artificial intelligence in health care including model explainability, transparency, fairness, and generalizability. All stages in the model development lifecycle, from initial design to data capture to model deployment, can be communicated openly to stakeholders with responsible model reporting. Physician involvement throughout these processes can ensure clinical concerns and potential consequences are considered.
@article{ahmad2023show, category = {selected_research}, title = {Show Your Work: Responsible Model Reporting in Health Care Artificial Intelligence.}, author = {Ahmad, Muhammad Aurangzeb and Eckert, Carly Marie}, journal = {The Surgical Clinics of North America}, volume = {103}, number = {2S}, pages = {e1--e11}, year = {2023}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0039610923000348?via%3Dihub}, }

2022

ArXiV
Machine Learning for Deferral of Care Prediction

Muhammad Aurangzeb Ahmad, Raafia Ahmed, Dr Steve Overman, and 3 more authors

arXiv preprint arXiv:2207.01485, 2022

Abs Bib PDF

Care deferral is the phenomenon where patients defer or are unable to receive healthcare services, such as seeing doctors, medications or planned surgery. Care deferral can be the result of patient decisions, service availability, service limitations, or restrictions due to cost. Continual care deferral in populations may lead to a decline in population health and compound health issues leading to higher social and financial costs in the long term. [1]. Consequently, identification of patients who may be at risk of deferring care is important towards improving population health and reducing care total costs. Additionally, minority and vulnerable populations are at a greater risk of care deferral due to socioeconomic factors. In this paper, we (a) address the problem of predicting care deferral for well-care visits; (b) observe that social determinants of health are relevant explanatory factors towards predicting care deferral, and (c) compute how fair the models are with respect to demographics, socioeconomic factors and selected comorbidities. Many health systems currently use rules-based techniques to retroactively identify patients who previously deferred care. The objective of this model is to identify patients at risk of deferring care and allow the health system to prevent care deferrals through direct outreach or social determinant mediation.
@article{ahmad2022machine, title = {Machine Learning for Deferral of Care Prediction}, author = {Ahmad, Muhammad Aurangzeb and Ahmed, Raafia and Overman, Dr Steve and Campbell, Patrick and Stroum, Corinne and Karunakaran, Bipin}, journal = {arXiv preprint arXiv:2207.01485}, year = {2022}, }

2021

ICHI 2021
Fairness in healthcare AI

Muhammad Aurangzeb Ahmad, Carly Eckert, Christine Allen, and 3 more authors

In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), 2021

Abs Bib

The issue of bias and fairness in healthcare has been around for centuries. With the integration of AI in healthcare the potential to discriminate and perpetuate unfair and biased practices in healthcare increases many folds. The tutorial focuses on the challenges, requirements and opportunities in the area of fairness in healthcare AI and the various nuances associated with it. The problem healthcare as a multi-faceted systems level problem that necessitates careful consideration of different notions of fairness in healthcare to corresponding concepts in machine learning is elucidated via different real world examples.
@inproceedings{ahmad2021fairness, category = {tutorial}, title = {Fairness in healthcare AI}, author = {Ahmad, Muhammad Aurangzeb and Eckert, Carly and Allen, Christine and Kumar, Vikas and Hu, Juhua and Teredesai, Ankur}, booktitle = {2021 IEEE 9th International Conference on Healthcare Informatics (ICHI)}, pages = {554--555}, year = {2021}, organization = {IEEE}, url = {https://ieeexplore.ieee.org/abstract/document/9565788}, }
KDD
Software as a medical device: regulating AI in healthcare via responsible AI

Muhammad Aurangzeb Ahmad, Steve Overman, Christine Allen, and 3 more authors

In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021

Abs Bib

With the increased adoption of AI in healthcare, there is a growing recognition and demand to regulate AI in healthcare to avoid potential harm and unfair bias against vulnerable populations. Around a hundred governmental bodies and commissions as well as leaders in the tech sector have proposed principles to create responsible AI systems. However, most of these proposals are short on specifics which has led to charges of ethics washing. In this tutorial we offer a guide to help navigate through complex governmental regulations and explain the various constituent practical elements of a responsible AI system in healthcare in the light of proposed regulations. Additionally, we breakdown and emphasize that the recommendations from regulatory bodies like FDA or the EU are necessary but not sufficient elements of creating a responsible AI system. We elucidate how regulations and guidelines often focus on epistemic concerns to the detriment of practical concerns e.g., requirement for fairness without explicating what fairness constitutes for a use case. FDA’s Software as a medical device document and EU’s GDPR among other AI governance documents talk about the need for implementing sufficiently good machine learning practices. In this tutorial we elucidate what that would mean from a practical perspective for real world use cases in healthcare throughout the machine learning cycle i.e., Data Management, Data Specification, Feature Engineering, Model Evaluation, Model Specification, Model Explainability, Model Fairness, Reproducibility, checks for data leakage and model leakage. We note that conceptualizing responsible AI as a process rather than an end goal accords well with how AI systems are used in practice. We also discuss how a domain centric stakeholder perspective translates into balancing requirements for multiple competing optimization criteria.
@inproceedings{ahmad2021software, category = {tutorial}, title = {Software as a medical device: regulating AI in healthcare via responsible AI}, author = {Ahmad, Muhammad Aurangzeb and Overman, Steve and Allen, Christine and Kumar, Vikas and Teredesai, Ankur and Eckert, Carly}, booktitle = {Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining}, pages = {4023--4024}, year = {2021}, url = {https://dl.acm.org/doi/abs/10.1145/3447548.3470823}, }

2020

KDD
Fairness in machine learning for healthcare

Muhammad Aurangzeb Ahmad, Arpit Patel, Carly Eckert, and 2 more authors

In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, 2020

Abs Bib

The issue of bias and fairness in healthcare has been around for centuries. With the integration of AI in healthcare the potential to discriminate and perpetuate unfair and biased practices in healthcare increases many folds The tutorial focuses on the challenges, requirements and opportunities in the area of fairness in healthcare AI and the various nuances associated with it. The problem healthcare as a multi-faceted systems level problem that necessitates careful of different notions of fairness in healthcare to corresponding concepts in machine learning is elucidated via different real world examples.
@inproceedings{ahmad2020fairnessKDD, category = {tutorial}, title = {Fairness in machine learning for healthcare}, author = {Ahmad, Muhammad Aurangzeb and Patel, Arpit and Eckert, Carly and Kumar, Vikas and Teredesai, Ankur}, booktitle = {Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery \& data mining}, pages = {3529--3530}, year = {2020}, url = {http://apsychoserver.psych.arizona.edu/JJBAReprints/PSYC621/Ahmad%20et%20al%202020.pdf}, }

2019

IJCAI
The challenge of imputation in explainable artificial intelligence models

Muhammad Aurangzeb Ahmad, Carly Eckert, and Ankur Teredesai

In AISafety Workshop at IJCAI, 2019

Abs Bib PDF

Explainable models in Artificial Intelligence are often employed to ensure transparency and accountability of AI systems. The fidelity of the explanations are dependent upon the algorithms used as well as on the fidelity of the data. Many real world datasets have missing values that can greatly influence explanation fidelity. The standard way to deal with such scenarios is imputation. This can, however, lead to situations where the imputed values may correspond to a setting which refer to counterfactuals. Acting on explanations from AI models with imputed values may lead to unsafe outcomes. In this paper, we explore different settings where AI models with imputation can be problematic and describe ways to address such scenarios.
@inproceedings{ahmad2019challenge, title = {The challenge of imputation in explainable artificial intelligence models}, author = {Ahmad, Muhammad Aurangzeb and Eckert, Carly and Teredesai, Ankur}, booktitle = {AISafety Workshop at IJCAI}, year = {2019}, url = {https://arxiv.org/pdf/1907.12669}, }

2018

ACM-BCB
Interpretable machine learning in healthcare

Muhammad Aurangzeb Ahmad, Carly Eckert, and Ankur Teredesai

Proceedings of the 2018 ACM international conference on bioinformatics, computational biology, and health informatics, 2018

Abs Bib PDF

This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the landscape of recent advances to address the challenges model interpretability in healthcare and also describe how one would go about choosing the right interpretable machine learnig algorithm for a given problem in healthcare.
@article{ahmad2018interpretable, category = {tutorial}, title = {Interpretable machine learning in healthcare}, author = {Ahmad, Muhammad Aurangzeb and Eckert, Carly and Teredesai, Ankur}, year = {2018}, journal = {Proceedings of the 2018 ACM international conference on bioinformatics, computational biology, and health informatics}, pages = {559--560}, url = {https://dl.acm.org/doi/abs/10.1145/3233547.3233667}, }

Tutorials

2021

ICHI 2021
Fairness in healthcare AI

Muhammad Aurangzeb Ahmad, Carly Eckert, Christine Allen, and 3 more authors

In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), 2021

Abs Bib

The issue of bias and fairness in healthcare has been around for centuries. With the integration of AI in healthcare the potential to discriminate and perpetuate unfair and biased practices in healthcare increases many folds. The tutorial focuses on the challenges, requirements and opportunities in the area of fairness in healthcare AI and the various nuances associated with it. The problem healthcare as a multi-faceted systems level problem that necessitates careful consideration of different notions of fairness in healthcare to corresponding concepts in machine learning is elucidated via different real world examples.
@inproceedings{ahmad2021fairness, category = {tutorial}, title = {Fairness in healthcare AI}, author = {Ahmad, Muhammad Aurangzeb and Eckert, Carly and Allen, Christine and Kumar, Vikas and Hu, Juhua and Teredesai, Ankur}, booktitle = {2021 IEEE 9th International Conference on Healthcare Informatics (ICHI)}, pages = {554--555}, year = {2021}, organization = {IEEE}, url = {https://ieeexplore.ieee.org/abstract/document/9565788}, }
KDD
Software as a medical device: regulating AI in healthcare via responsible AI

Muhammad Aurangzeb Ahmad, Steve Overman, Christine Allen, and 3 more authors

In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021

Abs Bib

With the increased adoption of AI in healthcare, there is a growing recognition and demand to regulate AI in healthcare to avoid potential harm and unfair bias against vulnerable populations. Around a hundred governmental bodies and commissions as well as leaders in the tech sector have proposed principles to create responsible AI systems. However, most of these proposals are short on specifics which has led to charges of ethics washing. In this tutorial we offer a guide to help navigate through complex governmental regulations and explain the various constituent practical elements of a responsible AI system in healthcare in the light of proposed regulations. Additionally, we breakdown and emphasize that the recommendations from regulatory bodies like FDA or the EU are necessary but not sufficient elements of creating a responsible AI system. We elucidate how regulations and guidelines often focus on epistemic concerns to the detriment of practical concerns e.g., requirement for fairness without explicating what fairness constitutes for a use case. FDA’s Software as a medical device document and EU’s GDPR among other AI governance documents talk about the need for implementing sufficiently good machine learning practices. In this tutorial we elucidate what that would mean from a practical perspective for real world use cases in healthcare throughout the machine learning cycle i.e., Data Management, Data Specification, Feature Engineering, Model Evaluation, Model Specification, Model Explainability, Model Fairness, Reproducibility, checks for data leakage and model leakage. We note that conceptualizing responsible AI as a process rather than an end goal accords well with how AI systems are used in practice. We also discuss how a domain centric stakeholder perspective translates into balancing requirements for multiple competing optimization criteria.
@inproceedings{ahmad2021software, category = {tutorial}, title = {Software as a medical device: regulating AI in healthcare via responsible AI}, author = {Ahmad, Muhammad Aurangzeb and Overman, Steve and Allen, Christine and Kumar, Vikas and Teredesai, Ankur and Eckert, Carly}, booktitle = {Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining}, pages = {4023--4024}, year = {2021}, url = {https://dl.acm.org/doi/abs/10.1145/3447548.3470823}, }

2020

KDD
Fairness in machine learning for healthcare

Muhammad Aurangzeb Ahmad, Arpit Patel, Carly Eckert, and 2 more authors

In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, 2020

Abs Bib

The issue of bias and fairness in healthcare has been around for centuries. With the integration of AI in healthcare the potential to discriminate and perpetuate unfair and biased practices in healthcare increases many folds The tutorial focuses on the challenges, requirements and opportunities in the area of fairness in healthcare AI and the various nuances associated with it. The problem healthcare as a multi-faceted systems level problem that necessitates careful of different notions of fairness in healthcare to corresponding concepts in machine learning is elucidated via different real world examples.
@inproceedings{ahmad2020fairnessKDD, category = {tutorial}, title = {Fairness in machine learning for healthcare}, author = {Ahmad, Muhammad Aurangzeb and Patel, Arpit and Eckert, Carly and Kumar, Vikas and Teredesai, Ankur}, booktitle = {Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery \& data mining}, pages = {3529--3530}, year = {2020}, url = {http://apsychoserver.psych.arizona.edu/JJBAReprints/PSYC621/Ahmad%20et%20al%202020.pdf}, }

2018

ACM-BCB
Interpretable machine learning in healthcare

Muhammad Aurangzeb Ahmad, Carly Eckert, and Ankur Teredesai

Proceedings of the 2018 ACM international conference on bioinformatics, computational biology, and health informatics, 2018

Abs Bib PDF

This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the landscape of recent advances to address the challenges model interpretability in healthcare and also describe how one would go about choosing the right interpretable machine learnig algorithm for a given problem in healthcare.
@article{ahmad2018interpretable, category = {tutorial}, title = {Interpretable machine learning in healthcare}, author = {Ahmad, Muhammad Aurangzeb and Eckert, Carly and Teredesai, Ankur}, year = {2018}, journal = {Proceedings of the 2018 ACM international conference on bioinformatics, computational biology, and health informatics}, pages = {559--560}, url = {https://dl.acm.org/doi/abs/10.1145/3233547.3233667}, }

Service & Leadership

AAAI · 2019–2020

Co-Chair, Symposium on AI for Social Good

AAAI Spring Symposium Series, Stanford University. Organized and led two consecutive years of invited talks, workshops, and panel discussions on AI applications in social good domains, with a focus on healthcare equity, criminal justice, and algorithmic accountability.
ACM FAccT

Panel Organizer, Healthcare AI and Algorithmic Fairness

ACM Conference on Fairness, Accountability, and Transparency. Organized a panel examining the specific fairness challenges of AI systems deployed in clinical and health system settings, including competing fairness criteria, disparate impact in prediction, and regulatory accountability.

Public Writing

3 Quarks Daily · May 2026

Monoculture of the Mind

Large language models are trained on the same data, optimized against the same benchmarks, and deployed to billions of people. I examine what it means for cognitive diversity when the infrastructure of thinking becomes a monoculture.

In the Press

Forbes · August 2019

What's Next in AI as the Debate around Race and Criminal Justice Surges

Featured in this piece examining the next frontier of AI fairness as the national conversation on race and criminal justice intensified. Interviewed about algorithmic bias, the limits of current fairness metrics, and what responsible AI in high-stakes decision systems actually requires.

Read →

Spark Dialog · August 2021

The Problems and Promise of AI in Healthcare

I discuss what AI can and cannot do in clinical settings, where the evidence is strong, where it is overhyped, and what equitable healthcare AI looks like in practice, including the fairness pitfalls that arise when models trained on historical data are deployed in clinical care.

Listen →

Digethics · August 2021

Using Data to Address Inequities in Healthcare

I examine how data science and machine learning can identify and reduce health disparities, and the risks of perpetuating bias through poorly designed algorithms, including what responsible design looks like from data collection through deployment.

Listen →

Contact

For research collaboration, reach me at maahmad@uw.edu. For advisory engagements and responsible AI governance, reach me at vonaurum@gmail.com.