Get your news from a source that's not owned and controlled by oligarchs. The trees are now considered invasive, and their bark contributes to wildfire risk. In 2023, author and poet Victoria Chang watched as the massive eucalyptus tree across the street from her home in Los Angeles was cut down. As the men lopped off the tree's limbs, Chang realized she hadn't spent much time really looking at it. She reflected that the tree had probably taken years to grow and was so easily cut down in just a few days. Chang felt compelled to write poems about this feeling that would later evolve into her latest poetry collection, which asks what it means to be human in the face of nature.

Rep. Ayanna Pressley and Democratic lawmakers reintroduced the AI Civil Rights Act aiming to combat algorithmic bias affecting Black, Brown and marginalized communities.

This survey article assesses and compares existing critiques of current fairness-enhancing technical interventions in machine learning (ML) that draw from a range of non-computing disciplines, including philosophy, feminist studies, critical race and ethnic studies, legal studies, anthropology, and science and technology studies. It bridges epistemic divides in order to offer an interdisciplinary understanding of the possibilities and limits of hegemonic computational approaches to ML fairness for producing just outcomes for society's most marginalized. The article is organized according to nine major themes of critique wherein these different fields intersect: 1) how "fairness" in AI fairness research gets defined; 2) how problems for AI systems to address get formulated; 3) the impacts of abstraction on how AI tools function and its propensity to lead to technological solutionism; 4) how racial classification operates within AI fairness research; 5) the use of AI fairness measures to avoid regulation and engage in ethics washing; 6) an absence of participatory design and democratic deliberation in AI fairness considerations; 7) data collection practices that entrench "bias," are non-consensual, and lack transparency; 8) the predatory inclusion of marginalized groups into AI systems; and 9) a lack of engagement with AI's long-term social and ethical outcomes. Drawing from these critiques, the article concludes by imagining future ML fairness research directions that actively disrupt entrenched power dynamics and structural injustices in society.

With there being many technical and ethical issues with artificial intelligence (AI) that involve marginalized communities, there is a growing interest for design methods used with marginalized people that may be transferable to the design of AI technologies. Participatory design (PD) is a design method that is often used with marginalized communities for the design of social development, policy, IT and other matters and solutions. However, there are issues with the current PD, raising concerns when it is applied to the design of technologies, including AI technologies. This paper argues for the use of PD for the design of AI technologies, and introduces and proposes a new PD, which we call agile participatory design, that not only can could be used for the design of AI and data-driven technologies, but also overcomes issues surrounding current PD and its use in the design of such technologies.

This paper unapologetically reflects on the critical role that Black feminism can and should play in abolishing algorithmic oppression. Positioning algorithmic oppression in the broader field of feminist science and technology studies, I draw upon feminist philosophical critiques of science and technology and discuss histories and continuities of scientific oppression against historically marginalized people. Moreover, I examine the concepts of invisibility and hypervisibility in oppressive technologies a l\'a the canonical double bind. Furthermore, I discuss what it means to call for diversity as a solution to algorithmic violence, and I critique dialectics of the fairness, accountability, and transparency community. I end by inviting you to envision and imagine the struggle to abolish algorithmic oppression by abolishing oppressive systems and shifting algorithmic development practices, including engaging our communities in scientific processes, centering marginalized communities in design, and consensual data and algorithmic practices.

Abstract: Marginalized People are "Expert" (not "Other") in Design The technologies we build often have unanticipated consequences. These can be particularly deleterious for people from marginalized populations––people considered non-normative "others." A conspicuous wearable device that helps navigate a person who is blind may make them a target for street robbery or harassment. In this talk, I draw from my research with the blind and transgender communities to argue that members of marginalized populations are experts. For example, a blind person is an expert in voice interaction; imagine what their expertise might lend the design of voice interfaces like Amazon Echo.

Digital technologies like the internet and smartphones are transforming our lives and society. They are proving to be powerful tools for liberating individuals' creative and entrepreneurial potential, as well as providing new educational opportunities and higher wages for marginalized people, both in the U.S. and around the globe. Unfortunately, in the U.S., outdated government regulations and weak consumer protections are undermining these opportunities. What's more, the Trump administration has not yet made significant moves to address this growing crisis: As of this writing, five key White House positions are vacant, without even acting directors or interim leaders to help the executive branch formulate U.S. science and technology policy. As the founder of both the Open Technology Institute and the X-Lab policy and innovation organization, I have spent years at the heart of many Washington, D.C. battles over technology policy, fighting for ideas that would best serve American workers and the general public.