Workshops & seminars

Data mining with an eye on algorithmic bias

DATE & TIME
Tuesday, February 1, 2022
11 a.m. – 12:30 p.m.
SPEAKER(S)

Francisco Berrizbeitia. Eng, M.Sc, Developer at Concordia Library

COST

Free

ORGANIZATION

Concordia University Library

WHERE

Online

For graduate students only. Register through GradProSkills.

This workshop will guide participants through the first steps for doing data analysis, specifically text mining with Weka. Weka is an open source machine-learning tool. We will be replicating the work of Mike Thelwall in his paper on Gender bias in machine learning for sentiment analysis (https://wlv.openrepository.com/handle/2436/620690)
Before getting into the hands-on text mining exercise, we will present a brief introduction to AI and machine learning, as well as the notion of algorithmic bias; what it is, how is introduced and it’s repercussions.
By the end of the workshop participants will have applied a sentiment analysis technique to a gender segregated data set and be able to determine its effect on the resulting predictive model.

IMPORTANT NOTE

Before the workshop, students are strongly encouraged to install Weka and download the data.

Learning Objectives

Participants of this workshop will:

  • Understand basic notions of AI: machine learning and predictive models.
  • Understand what is algorithmic bias, how is introduced in machine learning models and its possible repercussions.
  • Transform text into word vectors (Bag of Words Approach) as a technique to perform text-mining tasks.
  • Create a model for sentiment prediction using a machine learning approach based on a training corpus of real-life textual data (Tripadvisor comments on hotels and restaurants).
  • Evaluate the model and compare the performance with different gender biased training corpuses.

Event URL: https://concordia-ca.zoom.us/j/84130014603?pwd=M0VMWERLTkVSc3VRT2xVakVaa3Z5QT09

graduate-study-room Graduate student study space at the Webster Library (SGW campus). Lockers can be reserved in this room.
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