PUBLICATIONS
Four Priorities for Datasets, Models, and Metrics to Recalibrate Machine Learning for Social Biases
Author: Lucy Havens, Melissa Terras, Benjamin Bach, Beatrice Alex
(Under submission to Big Data & Society)
Classifying Bias to Communicate Bias: A Case Study with Gender Bias in British English Text from a Scottish Archives' Catalog
Author: Lucy Havens, Rachel Hosker, Melissa Terras, Benjamin Bach, Beatrice Alex
(Under submission to the ACM Conference on Fairness, Accountability, and Transparency)
Data Bias and Natural Language Processing of Metadata
Author: Lucy Havens
In: Library Catalogues as Data: Research, Practice, and Usage (forthcoming)
Confronting Gender Biases in Heritage Catalogues: A Natural Language Processing Approach to Revisiting Descriptive Metadata
Author: Lucy Havens, Beatrice Alex, Melissa Terras
In: The Routledge Handbook on Heritage and Gender (forthcoming 2024)
Tutorial on Text Classification of Gender Biased Language in Archival Documentation
Author: Lucy Havens
In: SAGE Research Methods Datasets: Decolonising and Diversifying Datasets (March 2024)

Author: Lucy Havens, Rachel Hosker, Melissa Terras, Benjamin Bach, Beatrice Alex
Digital Humanities 2023: Book of Abstracts (pp. 267-270)
Presented at the 2023 Digital Humanities Conference

Author: Lucy Havens
Publication in the Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing
North American Chapter of the Association for Computational Linguistics 2022
Towards Gender Biased Language Classification: A Case Study with British English Archival Metadata Descriptions
Author: Lucy Havens
Student Research Workshop
North American Chapter of the Association for Computational Linguistics 2022
Authors: Lucy Havens, Benjamin Bach, Melissa Terras, and Beatrice Alex
Publication in the Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022
Language Resources and Evaluation Conference 2022

Exploring National Library of Scotland Datasets with Jupyter Notebooks
Authors: Sarah Ames, Lucy Havens
IFLA Journal 2022

Author: Lucy Havens
Installation at The Association for Computers and the Humanities 2021 Conference

Authors: Lucy Havens, Melissa Terras, Beatrice Alex
Presentation at The Association for Computers and the Humanities 2021 Conference

Authors: Lucy Havens, Melissa Terras, Benjamin Bach, and Beatrice Alex
Publication in the Proceedings of the Second Workshop on Gender Bias in Natural Language Processing
28th International Conference on Computational Linguistics (COLING) 2020

Authors: Lucy Havens, Paul Gooding, Kirsty Lingstadt, Alex Forrest, Alasdair MacDonald, Melissa Terras
Digital Humanities Congress 2020 (postponed to 2022)

Authors: Lucy Havens, Mengting Bao, Larissa Pschetz, Benjamin Bach, Christine Bell
Publication in CHI EA '19: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems
Invited Talks
Panel on Gender Bias in Data and Technology
Discussed gender biases in cultural heritage documentation and machine learning systems
Scotsman Data Conference: Data for Diversity - September 29, 2022
Research Software Engineer (RSE) Story
Talk about my career path doing computational work in the humanities
The Alan Turing Institute's DH-RSE Summer School - July 28, 2021
Introduction to Text Mining in the Digital Humanities
Provided a theoretical introduction to text mining and an overview of practical approaches to text mining for humanities students interested in digital methods
Guest Lecture at the University of Buckingham - February 26, 2021
Designing for Data Exploration
Described three keys to designing platforms that allow people to explore data, drawing on the PaxVis project as an illustrative case study
Design Informatics Research Talk - January 24, 2019
Data Stories presentation - July 9, 2019
TEACHING
Tutorial on Text Classification of Gender Biased Language in Archival Documentation
Wrote a teaching guide and student guide, with accompanying code and data files, for creating a document classification model from archival documentation that has been annotated for gender bias in the form of omissions and stereotypes 
SAGE Research Methods, Decolonizing and Diversifying Research: Datasets 
Introduction to Python
Designed curriculum for and taught a two-week course on how to program in Python for Digital Humanities researchers without previous programming experience 
Centre for Data, Culture & Society, University of Edinburgh, February 2022
Designed curriculum (available on GitHub) for and taught three, two-week, virtual courses on select Python libraries relevant to Digital Humanities researchers
  - Course 1: how to analyze structured data with Pandas and ElementTree
  - Course 2: how to analyze unstructured data (text) with NLTK
  - Course 3: how to analyze networks in NetworkX and visualize data in Altair
Centre for Data, Culture & Society, University of Edinburgh, October-December 2020 and March-April 2022

Designed curriculum and taught a one-day workshop introducing data visualization with D3.js
University of Edinburgh, March 16, 2018

Visual Communication Design
Developed and taught curriculum for corporate training in visual communication design for project proposals and reports, in collaboration with coworkers at West Monroe Partners
Presented to West Monroe Partners employees in New York City, NY and Chicago, IL in 2016 and 2017
BlogGING
Train in the Distance (personal blog)
Documenting reflections on my research and studies, and my travels through Europe

Introduction to my area of work on the Living with Machines project
Explains the process of analyzing text from a digitized collection at the National Library of Scotland, one of five collections analyzed during an internship at the Library

Documents concept sketches and project progress for the PaxVis platform

Exquisite Design Blog Series
Complements corporate training in visual communication design for project proposals and reports at West Monroe Partners

Credit Card Marketing Blog Series
Explains the potential of Marketing Technology for business operations and customer experience to West Monroe Partners' clients

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