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Mission, Team and Story - The Algorithmic Justice League

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ajl.org·ajl.org/about

AJL is a key organization in the AI fairness and accountability space; relevant for AI safety researchers interested in societal harms, bias mitigation, and responsible deployment of AI systems.

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Summary

The Algorithmic Justice League (AJL) is an organization founded by Joy Buolamwini that combines art and research to illuminate the social implications and harms of AI technologies. AJL focuses on equitable AI through research, advocacy, and policy work, particularly around algorithmic bias and facial recognition disparities. The organization works to amplify the voices of those most impacted by harmful AI systems.

Key Points

  • •Founded by Joy Buolamwini, whose research exposed significant racial and gender bias in commercial facial recognition systems
  • •Combines artistic expression with technical research to raise awareness about algorithmic harms and bias
  • •Advocates for equitable and accountable AI through policy engagement, research publications, and public education
  • •Notable for the 'Gender Shades' study revealing bias in facial analysis tools from major tech companies
  • •Works to center the voices of marginalized communities most affected by biased AI systems

2 FactBase facts citing this source

EntityPropertyValueAs Of
Algorithmic Justice LeagueHeadquartersCambridge, MA—
Algorithmic Justice Leagueresearch-impactOver 70 AI researchers publicly signed letter defending AJL's research following Amazon's challenge to the Gender Shades findings on their Rekognition system2019

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Mission, Team and Story - The Algorithmic Justice League 

 OUR MISSION We’re leading a cultural movement towards EQUITABLE and ACCOUNTABLE AI

 We now live in a world where AI governs access to information, opportunity and freedom. However, AI systems can perpetuate racism, sexism, ableism, and other harmful forms of discrimination, therefore, presenting significant threats to our society - from healthcare, to economic opportunity, to our criminal justice system.

 The Algorithmic Justice League is an organization that combines art and research to illuminate the social implications and harms of artificial intelligence. 

AJL’s mission is to raise public awareness about the impacts of AI, equip advocates with resources to bolster campaigns, build the voice and choice of the most impacted communities, and galvanize researchers, policymakers, and industry practitioners to prevent AI harms.

 We want the world to remember that who codes matters, how we code matters, and that we can code a better future. OUR PRINCIPLES We mitigate the harms and biases of AI by promoting 4 core principles.

 Affirmative consent Everyone should have a real choice in how and whether they interact with AI systems.

 meaningful transparency It is of vital public interest that people are able to understand the processes of creating and deploying AI in a meaningful way, and that we have full understanding of what AI can and cannot do.

 Continuous oversight and accountability Politicians and policymakers need to create robust mechanisms that protect people from the harms of AI and related systems both by continuously monitoring and limiting the worst abuses and holding companies and other institutions accountable when harms occur. Everyone, especially those who are most impacted, must have access to  redress from AI harms. Moreover, institutions and decision makers that utilize AI technologies must be subject to accountability that goes beyond self-regulation.

 Actionable critiquE We aim to end harmful practices in AI, rather than name and shame. We do this by conducting research and translating what we’ve learned into principles, best practices and recommendations that we use as the basis for our advocacy, education and awareness-building efforts. We are focused on shifting industry practices among those creating and commercializing today’s systems.

 4:59 OUR ORIGINS
 The inspiration for the Algorithmic Justice League

 Dr. Joy Buolamwini, Founder of the Algorithmic Justice League, came face to face with discrimination. From a machine. It may sound like a scene from a sci-fi movie, but it carries meaningful real-world consequences.

 While working on a graduate school project, facial analysis software struggled to  detect her face. She suspected this was more than a technical blunder, but rather than surrender, she responded with curiosity. Her MIT peers with lighter skin color didn’t have the same issues, so Joy tried drawing a face on the palm of her hand. The machine 

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