call-for-code / embrace-diverse-representation Goto Github PK

Theme:
Black employees do not advance at the rate and to the levels of influence in the workplace they should due to a lack of transparency around opportunities and implicit biases in recruiting, evaluation, mentorship and promotion processes.

Brief description of your idea:

A platform/application that educates and tests bias in people who are involved in hiring within a company. The platform has a number of learning pathways for employees to learn about racial bias. This should focus on recommending educational material and promoting a learning pathway for people so that they can understand their bias and educate themselves.

The company can track racial bias (of the whole company) using inbuilt tests. These tests could be based on an existing framework (e.g https://implicit.harvard.edu/implicit/user/agg/blindspot/indexrk.htm). They should be carried out in conjunction with a learning pathway for reducing racial bias within the hiring process and focus on the overall company performance rather than specific individuals.

Further, as much bias is subconscious, it could highlight issues where intervention methods might be needed (e.g more data-driven hiring, masking racial information, etc).

What makes your idea unique?:
I am aware of racial bias tests existing, but perhaps not as part of a company education pathway. Further, companies may not be aware of how much subconscious bias is present in their hiring process.

Understanding how much of an issue this might be could help companies plan to mitigate issues that may arise and ensure that their employees are continuing to educate themselves in this area.

What would be the impact of your idea if implemented?:

• More structured education for those responsible for hiring
• Performance analysis for the company to understand how they can support and educate employees who are involved in hiring decisions.
• Data that can drive change and methods to improve the recruitment process to eliminate the potential for subconscious bias.

Theme:
Problem Statement 1 - data driven recruiting
(secondary) Problem Statement - remove bias from Machine Learning models

Brief description of your idea:
Create a tool that scans a resume to automatically mask words that enable recruiters to be susceptible to bias, such as:
• Name
• Gender-related words
• College
• Age
• Address
• Hobbies

This tool can also be run before a resume is inputted into common machine learning algorithms, so such bias isn't introduced into a company's recruitment models.

Performing this action before a resume is seen by a human can help reduce the impact of implicit bias from a recruiter (or at least, get it past that recruiter to a next step.)

Companies using this technology can also advertise they have bias-free recruitment tooling to help ensure the most qualified candidates are hired.

What makes your idea unique?:
A cursory search didn't find an existing tool specific to scanning resumes to strip bias, but there may be one out there.

What would be the impact of your idea if implemented?:
Recruit the best talent by removing bias from the decisions, which then increases opportunities for underrepresented minorities.

Skills to contribute (e.g. development, architecture, research, design or anything else):
architecture, design, possibly development (pending tooling setup/language)

Theme:

Problem Statement 3: "Bias is learned and perpetuated in different ways (e.g. societal beliefs, misrepresentation, ignorance) that consequently create inequitable outcomes across all spheres of life."

Idea:

Check (draft) news content for statements that could be inherently biased or could cause bias in the public opinion. This could be used by people producing media content for news sites. Use some sort of NLP to scan content. If an article contains a statement that could be taken as fact or where racial bias may be present, the application would highlight it and suggest some additional resources to check and perhaps prompt to add in some statistical context to verify this has been done.

It could also be used by people reading the content (perhaps through a browser plug-in) in order to highlight areas of an article that may reinforce racism.

Uniqueness:

Something like this probably exists already (not sure) but potentially not for racial bias.

Impact if implemented:

• Prompt creators of news content to be thorough in their analysis of whether the content they are producing is factually accurate and make them aware of the racial bias that is present in their writing.

• Reduce reinforced racism from biased media content.