Solution Overview

Solution Name:

Anti-bias ML for computer vision

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One-line solution summary:

Detect bias in computer vision solutions for people, such as bias against people of color, disabled people, women, and LGBTQIA+ people.

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Pitch your solution.

Current facial recognition and pose detection computer vision solutions have bias against certain people groups. This affects people worldwide, as these technologies are used in more settings, from law enforcement to health care, and affect people all over the world. As advanced technologies become more affordable in cheap smart phones with Internet access, the number of people who are affected by this keeps increasing.  Current facial recognition and pose detection computer vision solutions have bias against certain people groups. This affects people worldwide, as these technologies are used in more settings, from law enforcement to health care, and affect people all over the world. As advanced technologies become more affordable in cheap smart phones with Internet access, the number of people who are affected by this keeps increasing.  


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What specific problem are you solving?

Current facial recognition and pose detection computer vision solutions have bias against certain people groups. This affects people worldwide, as these technologies are used in more settings, from law enforcement to health care, and affect people all over the world. As advanced technologies become more affordable in cheap smart phones with Internet access, the number of people who are affected by this keeps increasing.  Current facial recognition and pose detection computer vision solutions have bias against certain people groups. This affects people worldwide, as these technologies are used in more settings, from law enforcement to health care, and affect people all over the world. As advanced technologies become more affordable in cheap smart phones with Internet access, the number of people who are affected by this keeps increasing.  

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What is your solution?

My solution would initially use nonlinear regression analysis and deep learning to detect if there is any significant bias in current computer vision solutions for facial recognition and pose detection. Secondly, I would augment and develop alternate solutions for facial recognition and pose detection by analyzing data sets with weighted/geometric averages to account for small samples from groups that the technologies show bias against as well as by using worst-case analysis instead of average-case analysis to improve the fairness and performance of these computer vision solutions for more people groups.

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Who does your solution serve, and in what ways will the solution impact their lives?

My solution would initially use nonlinear regression analysis and deep learning to detect if there is any significant bias in current computer vision solutions for facial recognition and pose detection. Secondly, I would augment and develop alternate solutions for facial recognition and pose detection by analyzing data sets with weighted/geometric averages to account for small samples from groups that the technologies show bias against as well as by using worst-case analysis instead of average-case analysis to improve the fairness and performance of these computer vision solutions for more people groups.

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Which dimension of the Challenge does your solution most closely address?

Actively minimize human and algorithmic biases, particularly in healthcare, education, and workplace settings.
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Explain how the problem you are addressing, the solution you have designed, and the population you are serving align with the Challenge.

My solution would initially use nonlinear regression analysis and deep learning to detect if there is any significant bias in current computer vision solutions for facial recognition and pose detection. Secondly, I would augment and develop alternate solutions for facial recognition and pose detection by analyzing data sets with weighted/geometric averages to account for small samples from groups that the technologies show bias against as well as by using worst-case analysis instead of average-case analysis to improve the fairness and performance of these computer vision solutions for more people groups.

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In what city, town, or region is your solution team headquartered?

College Station, TX, USA
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What is your solution’s stage of development?

Prototype: A venture or organization building and testing its product, service, or business model.
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Explain why you selected this stage of development for your solution.

Prototype. I am testing the prototype that I have developed.

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Who is the Team Lead for your solution?

Zhiyang Ong

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More About Your Solution

Which of the following categories best describes your solution?

A new technology
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What makes your solution innovative?

Yes, it uses transfer learning and reinforcement learning to concurrently detect and mitigate bias in machine learning -based solutions for computer vision tasks of facial recognition and pose detection.

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Please select the technologies currently used in your solution:

  • Artificial Intelligence / Machine Learning
  • Software and Mobile Applications
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Select the key characteristics of your target population.

  • Women & Girls
  • Pregnant Women
  • LGBTQ+
  • Infants
  • Children & Adolescents
  • Elderly
  • Rural
  • Peri-Urban
  • Urban
  • Poor
  • Low-Income
  • Middle-Income
  • Refugees & Internally Displaced Persons
  • Minorities & Previously Excluded Populations
  • Persons with Disabilities
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Which of the UN Sustainable Development Goals does your solution address?

  • 3. Good Health and Well-being
  • 5. Gender Equality
  • 10. Reduced Inequality
  • 16. Peace and Justice Strong Institutions
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How are you measuring your progress toward your impact goals?

By measuring the differences in scores for each metric in facial recognition and pose detection, and report their relative difference.
By report the difference for the average and worst-case score for each set of people group (or combination of identities), we can track if these scores are comparable/fair.

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About Your Team

What type of organization is your solution team?

Not registered as any organization

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How many people work on your solution team?

1

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How long have you been working on your solution?

1 year

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How are you and your team well-positioned to deliver this solution?

I am a Ph.D. student at Texas A&M University's Department of Electrical and Computer Engineering, working on domain-specific computing for computer vision, via hardware/software co-design.

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What is your approach to building a diverse, equitable, and inclusive leadership team?

I plan to recruit people from Latinx and Black American student organizations that I am part of.

I aim to emphasize discussing inclusive diversity, equity, and accessibility while recruiting people.

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Your Business Model & Partnerships

Do you primarily provide products or services directly to individuals, to other organizations, or to the government?

Organizations (B2B)
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Partnership & Prize Funding Opportunities

Why are you applying to Solve?

To get access to advice from experts and partners of MIT Solve to address U.N. sustainable development goals in computer vision and machine learning solutions that I develop.

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In which of the following areas do you most need partners or support?

  • Human Capital (e.g. sourcing talent, board development, etc.)
  • Financial (e.g. improving accounting practices, pitching to investors)
  • Monitoring & Evaluation (e.g. collecting/using data, measuring impact)
  • Technology (e.g. software or hardware, web development/design, data analysis, etc.)
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Please explain in more detail here.

I need help to acquire a large data sets of videos and photos of people from different marginalized people groups to adequately validate and test my machine learning models for facial recognition and pose detection.

Also, I need help to refine my solutions with automated machine learning to efficient perform design space exploration of computer vision solutions.

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What organizations would you like to partner with, and how would you like to partner with them?

MIT Faculty from CSAIL who work on bias, fairness, accountability, and transparency in machine learning. They can provide technical advice when I get stuck on local minimals in solution refinement, need to explore other techniques to address the problem.

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Do you qualify for and would you like to be considered for the Robert Wood Johnson Foundation Prize? If you select Yes, explain how you are qualified for the prize in the additional question that appears.

Yes, I wish to apply for this prize

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Explain how you are qualified for this prize. How will your team use Robert Wood Johnson Foundation Prize to advance your solution?

The solution that we are prototyping and testing is an anti-racist technology for computer vision, with applications in health care; hence, it address health care inequity as a consequence.

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Do you qualify for and would you like to be considered for The ASA Prize for Equitable Education? If you select Yes, explain how you are qualified for the prize in the additional question that appears.

No, I do not wish to be considered for this prize, even if the prize funder is specifically interested in my solution

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Do you qualify for and would you like to be considered for The Elevate Prize for Antiracist Technology? If you select Yes, explain how you are qualified for the prize in the additional question that appears.

Yes, I wish to apply for this prize

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Explain how you are qualified for this prize. How will your team use The Elevate Prize for Antiracist Technology to advance your solution?

It addresses racial bias in computer vision solutions for facial recognition and pose detection.

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Do you qualify for and would you like to be considered for The GM Prize? If you select Yes, explain how you are qualified for the prize in the additional question that appears.

Yes, I wish to apply for this prize

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Explain how you are qualified for this prize. How will your team use The GM Prize for Innovation in Refugee Inclusion to advance your solution?

The technology that we are developing is anti-racist, and can mitigate racist policies in law enforcement, social services, and health care. Hence, by mitigating inequities in these areas, the technology contributes to smart, safe, and sustainable communities by improving fairness in arrests and sentencing, medical diagnosis and treatment, and allowing more healthy people to live in communion with family and friends for longer lifespans.

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Do you qualify for and would you like to be considered for The HP Prize for Advancing Digital Equity? If you select Yes, explain how you are qualified for the prize in the additional question that appears.

Yes, I wish to apply for this prize

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Explain how you are qualified for this prize. How will your team use The HP Prize for Advancing Digital Equity to advance your solution?

Our anti-racist technology in computer vision improves inclusive diversity and economic opportunity in communities in the U.S. and around the world, since a more fair law enforcement process and more equitable health care allows people to access more educational, employment, and investment opportunities.

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Do you qualify for and would you like to be considered for the Innovation for Women Prize? If you select Yes, explain how you are qualified for the prize in the additional question that appears.

Yes, I wish to apply for this prize

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Explain how you are qualified for this prize. How will your team use the Innovation for Women Prize to advance your solution?

The technology detects and mitigates bias for multiple people groups, including disabled women of color. Hence, it tests for bias against different sets of intersectional identities, such as the aforementioned category.


Hence, this technology is feminist.

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Do you qualify for and would you like to be considered for The AI for Humanity Prize? If you select Yes, explain how you are qualified for the prize in the additional question that appears.

Yes, I wish to apply for this prize

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Explain how you are qualified for this prize. How will your team use The AI for Humanity Prize to advance your solution?

The anti-racist, feminist, and anti-ableist computer vision technology realizes applied machine learning for all in applications such as law enforcement and health care.

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Solution Team

 
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