ICML 2021 Affinity Events

Queer in AI Social: AI for Biodiversity
Vishakha Agrawal, Sara Beery

Talk on AI for biodiversity and closing the gap between academic research and real-world impact. Nontraditional paths to research and interdisciplinary education. Register for the socials here.

LatinX in AI (LXAI) Research at ICML 2021
Maria Luisa Santiago, Miguel Alonso Jr, Laura Montoya, William Berrios, Fiorela Manco Fernández, Diana Diaz, Vinicius Caridá, LOURDES RAMIREZ CERNA, Pedro Braga, Gabriel Ramos, Leonel Rozo, Walter Mayor, Vanessa Gilede, Dennis Hernando Núñez Fernández, Erick Mendez Guzman, Paola Cascante-Bonilla

Launched in January 2018, leaders from academia and industry in Artificial Intelligence, Education, Research, Engineering, and Social Impact banded together to create a group that would be focused on “Creating Opportunity for LatinX in AI.”

Indigenous in AI Social
MrWolf Running Wolf

Papa Reo (papareo.nz) is a multilingual language initiative grounded in Indigenous knowledge and ways of thinking and powered by cutting edge data science. We will present our work on the importance of indigenous sovereignty over data, platforms, and technologies used in the preservation, promotion, and revitalisation of language and culture. We will also cover some of our latest tools such as a real time pronunciation model which aims to decolonise the sound of te reo Māori. Papa Reo is led by Te Reo Irirangi o Te Hiku o Te Ika (Te Hiku Media), a charitable media organisation, collectively belonging to the Far North iwi of Ngāti Kuri, Te Aupouri, Ngai Takoto, Te Rārawa and Ngāti Kahu. Te Hiku Media is an iwi communications hub for radio, online video and media services. Māori language revitalisation is a core focus, and Te Hiku Media’s vision and mission were confirmed by a meeting of kaumātua (elders) and other native speakers of Te Reo Māori.

A QA and informal social will follow this talk

Queer in AI Workshop
Arjun Subramonian, Sharvani Jha, Vishakha Agrawal, Umut Pajaro Velasquez, MaryLena Bleile, Michelle Julia Ng

Queer in AI’s demographic survey reveals that most queer scientists in our community do not feel completely welcome in conferences and their work environments, with the main reasons being a lack of queer community and role models. Over the past years, Queer in AI has worked towards these goals, yet we have observed that the voices of marginalized queer communities - especially transgender, non-binary folks and queer BIPOC folks - have been neglected. The purpose of this workshop is to highlight issues that these communities face by featuring talks and panel discussions on the inclusion of non-Western non-binary identities; and Black, Indigenous, and Pacific Islander non-cis folks.

Women in Machine Learning (WiML) Un-Workshop
Wenshuo Guo, Beliz Gokkaya, Arushi G K Majha, Vaidheeswaran Archana, Berivan Isik, Olivia Choudhury, Liyue Shen, Hadia Samil, Tatjana Chavdarova

The Women in Machine Learning (WiML) workshop was founded in 2006 to forge connections within the relatively small community of women working in machine learning, to encourage mentorship, exchange of ideas, and promote communication. The program features 4 invited talks, 4 breakout sessions each having 2-8 parallel webinars, a panel with discussions on “industry/academic research, how to choose your path” and “post-pandemic adjustment and tips”, a mentoring social and 4 sponsor expo talks. Please refer to https://wimlworkshop.org/icml2021/program/ for more information.

The workshop attracts representatives from both academia and industry, whose contributed talks showcase some of the cutting-edge research done by women. In addition to technical presentations and discussion, the workshop aims to incite debate on promising research avenues and career choices for machine learning professionals. Details about WiML’s history and past events can be found at www.wimlworkshop.org. WiML workshops are overseen by the WiML Board of Directors, who select and oversee the organizing committee for each year’s workshop.

Black in AI Social
charlescearl Earl, Victor Silva N Silva

For over four years, Black in AI has been a place for sharing ideas, fostering collaborations, and discussing initiatives to increase the presence of Black people in the field of Artificial Intelligence. If you are in AI and either self-identify as Black, African, Diaspora or an ally, please join us at ICML21 to discuss interests, challenges, opportunities, collaborations, and other related issues. We plan to gather for a one-hour town hall and Q&A session. We'll then continue with informal socializing for the remaining hour.

Queer in AI Social: Storytelling: Intersectional Queer Experiences Around the World
Vishakha Agrawal, Shubha Chacko

Share stories with and listen to anecdotes from fellow queer + trans folks from around the world. Register for the socials here

LatinX in AI Social
Maria Luisa Santiago, Miguel Alonso Jr, William Berrios

Launched in January 2018, leaders from academia and industry in Artificial Intelligence, Education, Research, Finance, Community and Social Impact Nonprofits banded together to create a group that would be focused on “Creating Opportunity for LatinX in AI.”

Artificial Intelligence has the potential to displace workers of marginalized populations including those of Latinx origin. AI is already perpetuating social bias and prejudice because it lacks representation of LatinX professionals in the AI industry. Machine learning algorithms can encode a discriminative bias during training with real-world data in which underrepresented groups are not properly characterized or represented. A question quickly emerges: how can we make sure Machine Learning does not discriminate against people from minority groups because of the color of their skin, gender, ethnicity, or historically unbalanced power structures in society?

Even more, as the tech industry does not represent the entire population, underrepresented populations in computing such as Hispanics, women, African-Americans, and Native Americans have limited control over the direction of machine learning breakthroughs. As an ethnicity, the Latinx population is an interesting case study for this research as members are comprised of all skin tones with a wide regional distribution across the world.

In this session, we claim that …