Diversity, Inclusion and Leadership in Tech

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This is a reading seminar on issues of diversity and inclusion, focusing on how to take leadership in creating more equitable and just communities in tech.

Spring 2022 Diversity, Inclusion and Leadership in Tech Reading Group

This is a reading group for IACS students and affliates on issues of diversity and inclusion, focusing on how to take leadership in creating more equitable and just communities in tech. The reading group is supported and facilitated by members of the IACS Graduate Advisory Committee as well as by IACS faculty and staff.

This reading group is a not-for-credit continuation of the Fall Diversity, Inclusion and Leadership Seminar. The reading group will be bimonthly and discuss papers on topics that are chosen by both group facilitators as well as participants.

We welcome folks of all backgrounds in the wider IACS community to sign-up for the reading group!

Location and Time

We will meet bi-monthly on Fridays from 3.30pm-4.30pm in SEC 6.301.

Erin Erhart will host a DIB coffee hour on alternate Fridays (3.30pm-4.30pm) in their office SEC 1.312.09 (see Schedule).

Prerequisite Readings:

In order to establish a foundation of shared language and references, we ask that participants complete the following list of reading prior to our first group meeting. These readings are selected from the syllabus of the DIL Seminar.

  1. Five Years of Tech Diversity Reports—and Little Progress
  2. WHY IS SILICON VALLEY SO AWFUL TO WOMEN?
  3. Introduction and Chapter 2 from Race After Technology
  4. Chapters 1 and 8 from Invisible Women
  5. Data Science as Political Action: Grounding Data Science in a Politics of Justice
  6. “Those invisible barriers are real”: The Progression of First-Generation Students Through Doctoral Education
  7. Fourteen Recommendations to Create a More Inclusive Environment for LGBTQ+ Individuals in Academic Biology
  8. Awareness in Practice: Tensions in Access to Sensitive Attribute Data for Antidiscrimination
  9. Introduction and Chapter 1 of Ghost Work
  10. Algorithms are Not Neutral: Bias in Collaborative Filtering

Schedule

February 4th, 3.30pm-4.30pm Introductions

February 11th, 3.30pm-4.30pm DIB Coffee Hour with Erin Erhart

February 18th, 3.30pm-4.30pm Building Trust Across Differences & Tackling Difficult Subjects

February 25th, 3.30pm-4.30pm DIB Coffee Hour with Erin Erhart

March 4th, 3.30pm-4.30pm Situating the Scientist

March 25th, 3.30pm-4.30pm Case Study I: The Data Nutrient Project

In this session, we look at ways that we can instantiate princples/frameworks from our readings to evaluate the broader social impact of technology.

April 8th, 3.30pm-4.30pm Roles of Data Science in Social Change

This week we examine another case study of the broader socal impact of technology. In particular, we examine frameworks and examples for applying data science for social change and advocacy.

April 22th, 3.30pm-5pm Data Labeling Party with the Data Nutrition Project

This week the Harvard Data Nutrition Project is visiting our reading group. We will engage in another short data labeling exercise and discuss research in ML transparency, fairness and policy.