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<big>2021 - 2022 Cohort | <big> | ||
=== 2021 - 2022 Cohort === | |||
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As we seek to support the MozFest community in building more tools and technology that promote Trustworthy Artificial Intelligence (AI), we would like expert AI Builders to work alongside highly engaged Civil Society Actors in envisioning a more equitable automated future. | As we seek to support the MozFest community in building more tools and technology that promote Trustworthy Artificial Intelligence (AI), we would like expert AI Builders to work alongside highly engaged Civil Society Actors in envisioning a more equitable automated future. | ||
====Building Trustworthy AI Working Group==== <br /> | ==== Building Trustworthy AI Working Group ==== <br /> | ||
The Building Trustworthy AI Working Group for AI builders is an engaged community of over 250 global members who aim to help our technical community build more Trustworthy AI. We invite developers, funders, and policy-makers expressly concerned with technical products and standards to this group. <br /> | The Building Trustworthy AI Working Group for AI builders is an engaged community of over 250 global members who aim to help our technical community build more Trustworthy AI. We invite developers, funders, and policy-makers expressly concerned with technical products and standards to this group. <br /> | ||
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A '''feminist dictionary in AI''' will create a reference for desinging gender-inclusive algorithms. The dictionary will help AI Builders understand feminist theory and gender equality, as well as describe the kinds of classification, analysis, and prediction-making necessary for developing a gender-inclusive algorithm. Join us to work towards a more equitable and inclusive era in AI. <br /> | A '''feminist dictionary in AI''' will create a reference for desinging gender-inclusive algorithms. The dictionary will help AI Builders understand feminist theory and gender equality, as well as describe the kinds of classification, analysis, and prediction-making necessary for developing a gender-inclusive algorithm. Join us to work towards a more equitable and inclusive era in AI. <br /> | ||
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Accountability Case Labs''' will build community and common strategic goals across the full range of technical and social experts, researchers, builders, and advocates who care about AI accountability and auditing. Our cross-disciplinary project will also develop a body of collaborative case studies examining real world examples across groups — from policy-making and regulation to design, development, procurement, deployment and security. Join us for a collaborative approach to increasing AI accountability.<br /> | '''Accountability Case Labs''' will build community and common strategic goals across the full range of technical and social experts, researchers, builders, and advocates who care about AI accountability and auditing. Our cross-disciplinary project will also develop a body of collaborative case studies examining real world examples across groups — from policy-making and regulation to design, development, procurement, deployment and security. Join us for a collaborative approach to increasing AI accountability.<br /> | ||
'''AI Governance in Africa''' will create an open and inclusive space for discussion and research into the development, application, and governance of AI in Africa. We will draft a white paper that tackles issues of societal concern like bias in AI. Join us to help create this resource for more open and inclusive AI in Africa.<br /> | '''AI Governance in Africa''' will create an open and inclusive space for discussion and research into the development, application, and governance of AI in Africa. We will draft a white paper that tackles issues of societal concern like bias in AI. Join us to help create this resource for more open and inclusive AI in Africa.<br /> | ||
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As part of Mozilla’s HBCU Collaborative, students at '''Spelman College''' will work with academics and industry experts on a white paper that centers Black women in the conversation on Trustworthy AI and approaches it from an intersectional lens. Interested in contributing to this research project? Join the working group to learn more.<br /> | As part of Mozilla’s HBCU Collaborative, students at '''Spelman College''' will work with academics and industry experts on a white paper that centers Black women in the conversation on Trustworthy AI and approaches it from an intersectional lens. Interested in contributing to this research project? Join the working group to learn more.<br /> | ||
<big>2020 - 2021 Cohort | <big> | ||
=== 2020 - 2021 Cohort === | |||
</big>Click any link below to find out more about our Trustworthy AI projects, the working group process, outputs, and how some teams used MozFest as platform to expand their work.<br /> | </big>Click any link below to find out more about our Trustworthy AI projects, the working group process, outputs, and how some teams used MozFest as platform to expand their work.<br /> | ||