How might we address challenges around equity diversity and inclusion in computing

  • less representation in equity in leadership
  • Efforts have been focused on tree aspects?
    • Helping getting a degree
    • Helping getting a person hired
  • Help design “fair” systems/projects (focus on ml?)
    • Who reviews the code
      • Men get recommended more
        • Code review automation system?
  • There are issues with unfair citing in research papers

Questions

Su- Question 1: How can we ensure that computing education and training programs are accessible and inclusive for individuals from diverse socioeconomic backgrounds and underrepresented groups?

Su- Question 2: What strategies or initiatives have been effective in promoting diversity and inclusion within computing organizations and academic departments, and how can these be scaled or adapted for broader impact?

  • One of rthe most successful is K2I
    • it helps them gain confidence
  • There have been some great clubs (I imagine culture clubs?)
  • In her four hers no failures out.

J- Question 3: In your view, what are the most significant barriers to entry and advancement for underrepresented groups in computing fields, and how can we dismantle those barriers through education and policy interventions

J - Question 3.5: How can we create a more inclusive and welcoming environment for students from diverse backgrounds in computing and computing education programs?

  • They don’t have access to the best teachers/schools
  • ensure they feel welcome
    • They have challenges finding teams to work on projects
    • They need to feel involved and welcome
  • Build communities around said students/minorities
    • without support systems they feel less motivated and may quit.

J - Question 4: How can we ensure that AI and machine learning technologies are developed and deployed in ways that mitigate bias and promote fairness? 

diff ways to achieve fairness

  • analyze data before input to see bias
  • measure ml output to detect bias (In-proccesing techniques/out-processing.)

Su- Question 5: What strategies can we implement to identify and stop biases in recruitment?

  • Automated systems are good-
    • With good training data
  • Profiling of students-

Su- Question 6: What are some support networks and communities for underrepresented groups in computing?

Interviewers may be lacking

  • DEI training

translations framework?
accessibility in design

framework to preprocess data?
framework to post train ml models

issues are not being able to get as many opportunities
less welcoming environments
more challenges for equity seeking groups to land positions
it can take up to 6 months to land a position. or even more.

not enough mentors for equity groups.

smaller networks- more difficult for higher salaries

if we make sure there are more mentors, it’s better

2nd barrier is the environment.

  • has to defy stereotypes
  • has to work harder to prove they are has good as everyone else.