# Tian Zheng (Workshop in Applied Statistics) #### calendar\_today Date and Time **November 9, 2022** 12:00PM - 01:30PM EST #### pin\_drop Location **CGIS Knafel Building, room K354 or Online via Zoom** ### **Today's Speaker** Tian Zheng (Columbia University) "Toward a Taxonomy of Trust for Probabilistic Machine Learning" ### **Abstract** Probabilistic machine learning increasingly informs critical decisions in all sectors. To aid the development of trust in these decisions, we develop a taxonomy delineating where trust in an analysis can break down: (1) in the translation of real-world goals to goals on a particular set of available training data, (2) in the translation of abstract goals on the training data to a concrete mathematical problem, (3) in the use of an algorithm to solve the stated mathematical problem, and (4) in the use of a particular code implementation of the chosen algorithm. Our taxonomy highlights steps where existing research work on trust tends to concentrate and also steps where establishing trust is particularly challenging. In this talk, I will detail how trust can fail at each step and illustrate our taxonomy with examples from my recent research. *The Applied Statistics Workshop (Gov 3009) meets all academic year, Wednesdays, 12pm-1:30pm, in CGIS K354. This workshop is a forum for advanced graduate students, faculty, and visiting scholars to present and discuss methodological or empirical work in progress in an interdisciplinary setting. The workshop features a tour of Harvard's statistical innovations and applications with weekly stops in different fields and disciplines and includes occasional presentations by invited speakers.* *More information is available at the Gov 3009 website: * *All interested Harvard affiliates are invited to attend.* See also:- [ Workshops, Seminars & Classes ](/event-type/workshops-seminars-classes) Share on:- [ Facebook ](#) - [ Twitter ](#) - [ Linkedin ](#) Save: [ Add to calendar calendar\_today ](https://www.iq.harvard.edu/node/1650473/event-feed.ics) Copy link link