COVID-19 has worsened disparities found in workplaces worldwide. In the US alone, 80% of pandemic job loss has affected women, and 25% of women might reduce their hours or leave altogether. Compounding on this, minority women have had the highest rates of unemployment throughout the beginning of the pandemic. Sexism is a Goliath to tackle and can make a solution seem pretty impossible. That is where AI comes in. Simply put- our project will screen for bias and incentivize companies to do better.
Women, in particular mothers and minorities, have unequally born the economic weight of Covid-19, including higher unemployment rates than their male counterparts. Even without a pandemic, minority employees are often held to different standards. There have been countless PSAs and calls to action on the issue but the truth is, bias is human and eradicating its negative effects is hard. That's where AI and machine learning tools can help- and we need YOUR help to prove it.
Artificial Intelligence (AI) can reduce discrimination and increase equity by analyzing corporate hiring and promotion practices. The machine-generated results will be turned into a Public Scorecard, giving you the knowledge and power to support companies that align with your value system. You can also help us create best practices that define "bias" and eliminate discriminatory computer programs. Using technology in this way is new and we believe that our approach will work with YOUR help.
As our economy continues to move online following the pandemic, it will be too easy for diversity and inclusion priorities to fall through the cracks. YOUR support will make our launch possible and continuing support will be vital to see this project through to a global launch. A small donation may seem like it will do nothing with the weight of this issue and the expense of tech projects. But YOUR support shows that you stand with us and believe in the potential AI has to be a force for good!
This project has provided additional documentation in a PDF file (projdoc.pdf).