Artificial intelligence and gender equality
Temas
Framing de landscape of gender equality and AI
Gender equality and AI principles
Recommendations for integrating get into AI principles
Gender transformative operationalization of AI principles
Action plan and next steps
Recommendations UNESCO
Notas generales
Voice assistants con voz de mujer como Alexa y Siri con personalidad uniforme
Claves: reportes de periódicos, discusiones suscitadas dentro del web summit en Portugal,
I'd blush if I could has helped spark a global conversation on the gendering of AI technology and the importance of education to develop the digital skills of women and girls.
Gender biases and sexism displayed by digital voices assistants
Conferencia de UNESCO del 8M "Gender Equality and the Ethics of Artificial Intelligence: What solutions from the private sector".
Since rescheduling the conference was not possible, we decided to reorient our work and launch a Global Dialogue on Gender Equality and Artificial Intelligence (the Dialogue) with leaders in AI, digital technology and gender equality from academia, civil society and the private sector. We structured the Dialogue around eight questions that participants could answer either in writing or through a virtual interview session that was recorded. The present report shares the main findings from experts’ contributions to UNESCO’s Dialogue on Gender Equality and AI, as well as additional research and analysis conducted by an external consultant, Jennifer Breslin1. The report provides recommendations on how to address gender equality considerations in AI principles. It also offers guidance to the public and private sectors, as well as to civil society and other stakeholders, regarding how to operationalize gender equality and AI principles.
AI sustituirá trabajos femeninos /. AI tiene el potencial de hacer cambios positivos en la sociedad al retar normas previas de género por ejemplo, un programa de reclutamiento de AI discriminaba mujeres, ai powered gender decoders ayudan a empleadores a usar lenguaje inclusivo y a escribir perfiles de puestos más inclusivos
UNESCO: Identificar: issues, challenges, and good practices to help:f Overcome the built-in gender biases found in AI devices, data sets and algorithms; f Improve the global representation of women in technical roles and in boardrooms in the technology sector; and f Create robust and gender-inclusive AI principles, guidelines and codes of ethics within the industry.
UNESCO, for example, is advocating for a humanistic approach to AI that would ensure that AI contributes to the realization of the Sustainable Development Goals (SDGs) and of human rights frameworks. Others are asking for the decolonization of AI6, arguing that AI has become a tool of digital colonialism, whereby ‘poorer countries are overwhelmed by readily available services and technology, and cannot develop their own industries and products that compete with Western corporations’.7 Others still are calling for a use of AI that protects data rights and sovereignty, expands collective as well as individual choices, dismantles patriarchy, the neo-liberal order and late stage/extractive capitalism, and promotes human flourishing over relentless economic growth.8 Gender equality is necessary for the realization of any and all of the goals above, as well as being an objective in and of itself. It thus needs to be mainstreamed and considered at the highest level of outcomes and societal imperatives
Summaries of the meta analyses
A feminist viewpoint
Gender dimensions
Content of principles
Process of principle development
Where gender is located
Whole society, systems and lifecycle approach
Addressing gender equality in AI
Provision for participation of women girls and gender equality experts
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Awareness, education and skills
Private sector industry
The bigger picture
Data
Standards and policies
Internal mitigation at company level
Corporate governance
Educate, promote, contextualize
Diverse development teams and design process
Ethics panels and impact mapping
Consultation with affected end users
Technical approaches
External mitigation
Diversity in AI Industry - women in tech
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