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The Digital Humanities Institute (EPFL) in collaboration with the Equal Opportunities Office will host a 3 hour interactive workshop for EPFL students via Zoom on March 26.

How can a human rights based approach be applied to computer science, engineering and machine learning algorithms?

Using gender as a prism to understand a human rights framework that underscores AI, the interactive workshop will focus on what a human rights approach is, how a sociological perspective can contribute to developing practices, and the journey of data and model design, using a Jupyter notebook to work through the Pre-Processing, In-Processing and Post-Processing steps of the data life cycle. Through the workshop we will foster reflection on the stereotypes, biases and gendered roles of both women and men, with the intention of understanding what real-life constraints hinder equality in the working environments and the output of computer scientists and engineers. The workshop will increase participant awareness of the relevance of gender and equality in their work and to their workplace. It provides a unique opportunity to develop, deepen, and apply gender equality learnings, putting learning into action, ultimately leading to better decision-making, excellence in science, and improved practices.
After completing the workshop, the participant will receive a certificate.

Who is this workshop for?

EPFL undergraduate and graduate students.

Learning outcomes

Upon completion EPFL students will have the knowledge and skills to:

  • Explain a human rights-based approach to AI;
  • Identify relevance of different biases and importance of intersectionality, gender equality and bias to computer science and engineering / institutional objectives;
  • Analyze how gender or inequality bias has occurred or can occur in the research, design and development of AI;
  • Apply how and when to use gender & equality inclusive tools and techniques to mitigate bias in AI;
  • Evaluate concrete methods to integrate gender and equality into design, planning and implementation of AI projects.

Instructors

Caitlin Kraft-Buchman is the CEO/Founder of Women at the Table, a growing global Swiss civil society organization based in Geneva, and the first organization to focus on systems change by helping women gain influence in sectors that have key structural impact: technology; the economy; sustainability; and democracy and governance. She is a founder of the A+ Alliance for Inclusive Algorithms which was one of Fast Company’s 2020 World Changing Ideas in AI & Data, and leadership of the UN Women Generation Equality Action Coalition for Technology and Innovation.

Asako Hattori serves as a human rights officer at the Women’s Human Rights and Gender Section, the Office of the United Nations High Commissioner for Human Rights (UN Human Rights– – OHCHR). Previously, she served at the Secretariat of the United Nations human rights treaty bodies, including the Committee on the Rights of the Child, the Committee on the Elimination of Discrimination against Women and the Committee on the Rights of Persons with Disabilities. At UN Human Rights, she has also worked on economic, social and cultural rights, land and human rights, gender stereotyping, digital technologies and women’s rights.

Jessica Pidoux is a doctoral assistant at the Ecole Polytechnique Fédérale de Lausanne, holds a master’s degree in sociology of communication and culture from the University of Lausanne and is finalising her thesis on dating applications programming and user practices.

Sofia Kypraiou is a final-year master’s student in Data Science at EPFL who will work on her master’s thesis exploring and enhancing a practical toolbox for AI and Human rights in 2021. She holds a degree in Informatics & Telecommunications from the University of Athens.

Hosts

Daniel Gatica-Perez leads the Social Computing Group at Idiap and is a professor at EPFL, affiliated with the School of Engineering and the College of Humanities.

Helene Füger is the head of EPFL’s Equality Office.

 

More information and registration here.

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