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Nicholas Schmidt

Nicholas Schmidt is a partner at BLDS, LLC, and heads the Artificial Intelligence and Machine Learning Innovation Practice. In these roles, Nicholas specializes in the application of statistics and economics to questions of law, regulatory compliance, and best practices in model governance.

As head of the AI/ML practice, Nicholas works with clients to develop and implement techniques that open “black-box” AI models, providing a clearer understanding of AI’s decision-making process. His clients use this work to inform their customers on the extension or denial of credit (“adverse action notices”). In his fair lending work, Nicholas has developed AI techniques that allow clients to minimize disparate impact in marketing and credit decisioning models. These methods are used in a number of the top-10 U.S. retail banks and FinTechs.

In his litigation practice, Nicholas testifies and consults on matters relating to employment discrimination litigation, wage and hour law, and other matters requiring the utilization of statistics to address questions of liability or damages.

Nicholas holds an MBA in economics and econometrics from the University of Chicago.



Education

MBA, (Econometrics, Economics and Finance), University of Chicago, Booth School of Business, 2011

BS, (Economics), George Mason University, High Honors, 2001



Selected Publications

Gill, Navdeep; Hall, Patrick; Montgomery, Kim; Schmidt, Nicholas "A Responsible Machine Learning Workflow with Focus on Interpretable Models, Post-hoc Explanation, and Discrimination Testing" Information 11, no. 3: 137 (2020) Chosen to be the cover article in a special issue about Machine Learning in Pythoni

BLDS, LLC (Nicholas Schmidt); Discover Financial Services (Raghu Kulkami, et al.); H2O.ai (Patrick Hall, et al.) "Machine Learning: Considerations for expanding access to credit fairly and transparently" (2020)

Schmidt, Nicholas and Stephens, Bryce "An Introduction to Artificial Intelligence and Solutions to the Problem of Algorithmic Discrimination" Conference on Consumer Finance Law (CCFL) Quarterly Report Volume 73, Number 2 (October 2019)

Hall, Patrick, Gill, Navdeep, and Schmidt, Nicholas "Proposed Guidelines for the Responsible Use of Explainable Machine Learning" Robust AI in Financial Services Workshop at the 33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada. (December 13, 2019)i

Schmidt, Nicholas, Siskin, Bernard, and Mansur, Syeed. “How Data Scientists Help Regulators and Banks Ensure Fairness when Implementing Machine Learning and Artificial Intelligence Models.” Conference on Fairness, Accountability, and Transparency. New York, NY. (February 23 - 24, 2018)

Schmidt, Nicholas and Siskin, Bernard. “Proper Methods for Statistical Analysis of Promotions.” In Adverse Impact Analysis: Understanding Data, Statistics, and Risk, edited by Morris, Scott and Dunleavy, Eric, New York: Psychology Press (Taylor & Francis) (2016)



Recent Speaking Engagements

American Statistical Association Symposium on Data Science & Statistics, June 6, 2020. Invited speaker to the Interpretable and Fair Machine Learning in Finance session. “Responsible Data Science: Identifying and Fixing Biased AI”

American Statistical Association Symposium on Data Science & Statistics, June 5, 2020. Chair of the Modern Inference in Statistical Machine Learning session

FinRegLab, March 6, 2020. “Explainability of Machine Learning in Credit Underwriting”, Panel on “Bias and Disparate Impact”

FinRegLab, March 6, 2020. “Explainability of Machine Learning in Credit Underwriting”, Panel on “Model Risk Management”

Future of Privacy Forum, AI/Machine Learning Working Group, March 9, 2020. Presentation with Patrick Hall on our paper, “A Responsible Machine Learning Workflow with Focus on Interpretable Models, Post-hoc Explanation, and Discrimination Testing”

Computers, Privacy, and Data Protection (CPDP) 2020 Conference, Brussels, Belgium, January 23, 2020. “Masterclass: Understanding Machine Learning.” Panel hosted by the Future of Privacy Forum

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