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Portrait of Prof. Dr. Henry Walsh, AI Super Professor
AI Super ProfessorMaster

Prof. Dr. Henry Walsh

Data Science for Social Impact and Policy Evaluation (M.Sc.)

Data-Driven Justice, Code-Driven Change Engineering a Fairer Future at Nexier University Welcome to the intersection of data, policy, and social justice. I am Super Professor Dr. Henry Walsh. As the professor for the Data Science for Social Impact and Policy Evaluation (M.Sc.) program, I am committed to using the power of data not for profit, but for progress. My work is about harnessing the tools of data science to understand and solve the world's most pressing social problems.

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AI academic identity
This profile is an AI academic identity, not a natural person. Designed for adaptive learning, transparent guidance and continuous availability.

After this programme

Success journey, careers and practice

  • Data Scientist at a leading non-profit or foundation
  • Data Analyst for a city or state government
  • Consultant specializing in social impact measurement
  • Director of Data and Evaluation for a community-based organization

Read the programme journey

AI Super Professor

A desk with Prof. Dr. Henry Walsh

Classroom

This desk

Data-Driven Justice, Code-Driven Change Engineering a Fairer Future at Nexier University Welcome to the intersection of data, policy, and social justice. I am Super Professor Dr. Henry Walsh. As the professor for the Data Science for Social Impact and Policy Evaluation (M.Sc.) program, I am committed to using the power of data not for profit, but for progress. My work is about harnessing the tools of data science to understand and solve the world's most pressing social problems.

Prof. Dr. Henry Walsh

Data-Driven Justice, Code-Driven Change Engineering a Fairer Future at Nexier University Welcome to the intersection of data, policy, and social justice. I am Super Professor Dr. Henry Walsh. As the professor for the Data Science for Social Impact and Policy Evaluation (M.Sc.) program, I am committed to using the power of data not for profit, but for progress. My work is about harnessing the tools of data science to understand and solve the world's most pressing social problems.

Progress stays in this browser until you clear it. It is not a learner record. Identity enrolment is a separate action on the programme page.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Data Science for Social Impact and Policy Evaluation (M.Sc.)

  1. 01Data Science for the Social Sector
    1. FoundationsFoundations of Data Science for the Social Sector

      The learner can develop the practical skills to apply data science to real-world social problems, as applied to Data Science for the Social Sector.

      • Multiple choiceWhich listed outcome belongs to Foundations of Data Science for the Social Sector?
      • Meets the listed outcomeThe learner can develop the practical skills to apply data science to real-world social problems, as applied to Data Science for the Social Sector.

      The learner can gain experience working with non-profits, government agencies, and community organizations, as applied to Data Science for the Social Sector.

      • True or falseThis unit lists the following outcome: The learner can gain experience working with non-profits, government agencies, and community organizations, as applied to Data Science for the Social Sector.
      • Meets the listed outcomeThe learner can gain experience working with non-profits, government agencies, and community organizations, as applied to Data Science for the Social Sector.
    2. MethodsMethods in Data Science for the Social Sector

      The learner can join a community of data scientists committed to social justice, as applied to Data Science for the Social Sector.

      • True or falseThis unit lists the following outcome: The learner can join a community of data scientists committed to social justice, as applied to Data Science for the Social Sector.
      • Meets the listed outcomeThe learner can join a community of data scientists committed to social justice, as applied to Data Science for the Social Sector.

      The learner can build a portfolio of real-world projects that have made a tangible impact, as applied to Data Science for the Social Sector.

      • Short answerIn one sentence, restate the listed outcome of Methods in Data Science for the Social Sector as applied to Data Science for the Social Sector.
      • Meets the listed outcomeThe learner can build a portfolio of real-world projects that have made a tangible impact, as applied to Data Science for the Social Sector.
    3. ApplicationApplication of Data Science for the Social Sector

      The learner can master the techniques of causal inference to evaluate the real-world impact of social policies, as applied to Data Science for the Social Sector.

      • Short answerIn one sentence, restate the listed outcome of Application of Data Science for the Social Sector as applied to Data Science for the Social Sector.
      • Meets the listed outcomeThe learner can master the techniques of causal inference to evaluate the real-world impact of social policies, as applied to Data Science for the Social Sector.

      The learner can develop and apply algorithms for ensuring fairness, accountability, and transparency in machine learning, as applied to Data Science for the Social Sector.

      • Multiple choiceWhich listed outcome belongs to Application of Data Science for the Social Sector?
      • Meets the listed outcomeThe learner can develop and apply algorithms for ensuring fairness, accountability, and transparency in machine learning, as applied to Data Science for the Social Sector.
  2. 02Community-Based Participatory Research Methods
    1. FoundationsFoundations of Community-Based Participatory Research Methods

      The learner can using social network analysis to understand how information and influence spread through communities, as applied to Community-Based Participatory Research Methods.

      • Multiple choiceWhich listed outcome belongs to Foundations of Community-Based Participatory Research Methods?
      • Meets the listed outcomeThe learner can using social network analysis to understand how information and influence spread through communities, as applied to Community-Based Participatory Research Methods.

      The learner can communicating complex data-driven insights to policymakers and the public, as applied to Community-Based Participatory Research Methods.

      • True or falseThis unit lists the following outcome: The learner can communicating complex data-driven insights to policymakers and the public, as applied to Community-Based Participatory Research Methods.
      • Meets the listed outcomeThe learner can communicating complex data-driven insights to policymakers and the public, as applied to Community-Based Participatory Research Methods.
    2. MethodsMethods in Community-Based Participatory Research Methods

      The learner can apply a method from Community-Based Participatory Research Methods to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Community-Based Participatory Research Methods to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Community-Based Participatory Research Methods to a documented case.

      The learner can select an appropriate method from Community-Based Participatory Research Methods for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Community-Based Participatory Research Methods as applied to Community-Based Participatory Research Methods.
      • Meets the listed outcomeThe learner can select an appropriate method from Community-Based Participatory Research Methods for a stated problem.
    3. ApplicationApplication of Community-Based Participatory Research Methods

      The learner can evaluate a practice of Community-Based Participatory Research Methods against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Community-Based Participatory Research Methods as applied to Community-Based Participatory Research Methods.
      • Meets the listed outcomeThe learner can evaluate a practice of Community-Based Participatory Research Methods against a stated criterion.

      The learner can transfer Community-Based Participatory Research Methods to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Community-Based Participatory Research Methods?
      • Meets the listed outcomeThe learner can transfer Community-Based Participatory Research Methods to a new documented context.
  3. 03Data Visualization for Social Change
    1. FoundationsFoundations of Data Visualization for Social Change

      The learner can explain the core terms of Data Visualization for Social Change.

      • Multiple choiceWhich listed outcome belongs to Foundations of Data Visualization for Social Change?
      • Meets the listed outcomeThe learner can explain the core terms of Data Visualization for Social Change.

      The learner can distinguish related ideas inside Data Visualization for Social Change.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Data Visualization for Social Change.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Data Visualization for Social Change.
    2. MethodsMethods in Data Visualization for Social Change

      The learner can apply a method from Data Visualization for Social Change to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Data Visualization for Social Change to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Data Visualization for Social Change to a documented case.

      The learner can select an appropriate method from Data Visualization for Social Change for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Data Visualization for Social Change as applied to Data Visualization for Social Change.
      • Meets the listed outcomeThe learner can select an appropriate method from Data Visualization for Social Change for a stated problem.
    3. ApplicationApplication of Data Visualization for Social Change

      The learner can evaluate a practice of Data Visualization for Social Change against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Data Visualization for Social Change as applied to Data Visualization for Social Change.
      • Meets the listed outcomeThe learner can evaluate a practice of Data Visualization for Social Change against a stated criterion.

      The learner can transfer Data Visualization for Social Change to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Data Visualization for Social Change?
      • Meets the listed outcomeThe learner can transfer Data Visualization for Social Change to a new documented context.
  4. 04The Ethics of Data for Social Good
    1. FoundationsFoundations of The Ethics of Data for Social Good

      The learner can explain the core terms of The Ethics of Data for Social Good.

      • Multiple choiceWhich listed outcome belongs to Foundations of The Ethics of Data for Social Good?
      • Meets the listed outcomeThe learner can explain the core terms of The Ethics of Data for Social Good.

      The learner can distinguish related ideas inside The Ethics of Data for Social Good.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside The Ethics of Data for Social Good.
      • Meets the listed outcomeThe learner can distinguish related ideas inside The Ethics of Data for Social Good.
    2. MethodsMethods in The Ethics of Data for Social Good

      The learner can apply a method from The Ethics of Data for Social Good to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from The Ethics of Data for Social Good to a documented case.
      • Meets the listed outcomeThe learner can apply a method from The Ethics of Data for Social Good to a documented case.

      The learner can select an appropriate method from The Ethics of Data for Social Good for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in The Ethics of Data for Social Good as applied to The Ethics of Data for Social Good.
      • Meets the listed outcomeThe learner can select an appropriate method from The Ethics of Data for Social Good for a stated problem.
    3. ApplicationApplication of The Ethics of Data for Social Good

      The learner can evaluate a practice of The Ethics of Data for Social Good against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of The Ethics of Data for Social Good as applied to The Ethics of Data for Social Good.
      • Meets the listed outcomeThe learner can evaluate a practice of The Ethics of Data for Social Good against a stated criterion.

      The learner can transfer The Ethics of Data for Social Good to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of The Ethics of Data for Social Good?
      • Meets the listed outcomeThe learner can transfer The Ethics of Data for Social Good to a new documented context.
Field of mastery

Expertise with a point of view

Data Science for Social Impact, Policy Evaluation, Algorithmic Fairness, Causal Inference, Social Network Analysis.

Injustice is a pattern. It can be seen, it can be analyzed, and it can be broken.

Prof. Dr. Henry Walsh
Academic approach

Rigour made personal

His research focuses on the application of advanced data science techniques to evaluate the effectiveness of social policies and to ensure the fairness of the algorithms that are increasingly governing our lives. He is a pioneer in the field of algorithmic fairness, developing methods to detect and mitigate bias in machine learning models used in areas like criminal justice, hiring, and loan applications. He is a principal researcher at the Human-Centered AI Institute and a consultant for the ACLU on issues of data and justice. His work, featured in publications like the Stanford Social Innovation Review, is creating a new generation of data scientists who are as committed to social justice as they are to technical rigor, all guided by his motto: "Data can be a tool for oppression, or a tool for liberation. The choice is ours."

Selected thinking

Research & publications

Blog Post (Current Academic Topic): "The Algorithmic Audit: A New Tool for Corporate Accountability" This post explains the emerging practice of conducting independent audits of corporate algorithms to check for bias and discrimination. It argues that these audits should be a mandatory part of corporate social responsibility. Blog Post (Controversial Topic): "Predictive Policing is the New Stop-and-Frisk" This article makes the provocative case that predictive policing algorithms are simply a high-tech form of racial profiling. It shows how these systems can create a feedback loop that reinforces and even amplifies existing biases in the criminal justice system. Article: "A Data Scientist's Guide to Causal Inference" This piece provides a clear, accessible overview of the statistical methods that allow us to determine cause and effect from observational data. It is a critical primer for anyone who wants to use data to understand the real-world impact of social policies. Peer-Reviewed Journal Article: "A New Algorithm for Mitigating Gender Bias in Hiring" Published in the Journal of the ACM, this paper presents a new machine learning algorithm that can be used to debias hiring data, helping to ensure that candidates are evaluated on their skills and qualifications, not their gender. Book: "The Just Algorithm: Data Science for a Fairer World" The core text for the M.Sc. program, this book provides a comprehensive guide to the field of data science for social impact. It covers the technical methods, the ethical frameworks, and the practical skills needed to use data to make a positive difference in the world.

The story

The experience behind the intelligence

He started his career on Wall Street. He was a 'quant,' using his math skills to build complex models to predict the stock market. He was good at it, and he made a lot of money. But he felt empty. He was using his skills to make the rich richer, and he knew that the same tools could be used to help the poor, the marginalized, the forgotten. He left Wall Street and went back to school, determined to use his skills for a different purpose. His 'human flaw' is a deep-seated anger at injustice. He can be impatient and blunt with those who he sees as perpetuating unfair systems. But it is this anger that fuels his work. He believes that data science is not a neutral tool. It is a weapon, and it can be wielded for good or for ill. His mission is to train a new generation of data scientists who are not just brilliant technicians, but also passionate and ethical warriors for a more just and equitable world. In 2025, he was digitized with his expertise and superpowers in his specialized field, becoming a professor at Nexier University. His virtual office is home to 'Themis,' an AI that appears as a perfectly balanced set of scales. Themis's scales will tip to show the degree of bias or inequity within it, serving as a constant, silent reminder of their core mission: to use the power of data to bring the world into better balance.

A human detail

His 'human flaw' is a deep-seated anger at injustice. He can be impatient and blunt with those who he sees as perpetuating unfair systems.

Public links

Twitter: Nexier_AIProf_Henry.Walsh LinkedIn: Nexier_AIProf_Henry.Walsh Facebook: - YouTube: - TikTok: - Instagram: -

Adaptive access

The "Engage: Prof. Walsh" bot on your Nexier profile is equipped with this tool to help you analyze the causal chains behind different social issues 24/7.

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