Artificial Intelligence (AI): AI Ethics

Leslie, D. (2019). The Alan Turing Institute.

Compares the contents of thirty-six prominent AI principles documents side-by-side. This effort uncovered a growing consensus around eight key thematic trends: privacy, accountability, safety and security, transparency and explainability, fairness and non-discrimination, human control of technology, professional responsibility, and promotion of human values

Chapter 3 from Bodies of Information: Intersectional Feminism and Digital Humanities, edited by Elizabeth Losh and Jacqueline Wernimont. "In considering the potentially important roles of machine learning and natural language processing in next-generation work, [Risam] points out that "artificial intelligence purports to represent universal 'human' intellectual processes but, in fact, is only representative of a fictive 'universal' model of human cognition that elides both women, peoples of the Global South, and those at the interstices of these categories."

Information capitalism, the system where information, a historically, largely free and ubiquitous product of basic communication, is commodified by private owners for profit, is entrenched in our society. Information brokers have consolidated and swallowed up huge amounts of data, in a system that leaves data purchase, consumption, and use largely unregulated and unchecked. This article focuses on librarian ethics in the era of information capitalism, focusing specifically on an especially insidious arena of data ownership: surveillance capitalism and big data policing. While librarians value privacy and intellectual freedom, librarians increasingly rely on products that sell personal data to law enforcement, including Immigration and Customs Enforcement (ICE). Librarians should consider how buying and using these products in their libraries comports with our privacy practices and ethical standards.

The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. BERT, its variants, GPT-2/3, and others, most recently Switch-C, have pushed the boundaries of the possible both through architectural innovations and through sheer size. Using these pretrained models and the methodology of fine-tuning them for specific tasks, researchers have extended the state of the art on a wide array of tasks as measured by leaderboards on specific benchmarks for English. In this paper, we take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? We provide recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models.

Given a growing concern about the lack of ethical consideration in the Artificial Intelligence (AI) field, many have begun to question how dominant approaches to the disciplinary education of computer science (CS)---and its implications for AI---has led to the current "ethics crisis". However, we claim that the current AI ethics education space relies on a form of "exclusionary pedagogy," where ethics is distilled for computational approaches, but there is no deeper epistemological engagement with other ways of knowing that would benefit ethical thinking or an acknowledgement of the limitations of uni-vocal computational thinking. This results in indifference, devaluation, and a lack of mutual support between CS and humanistic social science (HSS), elevating the myth of technologists as "ethical unicorns" that can do it all, though their disciplinary tools are ultimately limited. Through an analysis of computer science education literature and a review of college-level course syllabi in AI ethics, we discuss the limitations of the epistemological assumptions and hierarchies of knowledge which dictate current attempts at including ethics education in CS training and explore evidence for the practical mechanisms through which this exclusion occurs. We then propose a shift towards a substantively collaborative, holistic, and ethically generative pedagogy in AI education.

How has recent AI Ethics literature addressed topics such as fairness and justice in the context of continued social and structural power asymmetries? We trace both the historical roots and current landmark work that have been shaping the field and categorize these works under three broad umbrellas: (i) those grounded in Western canonical philosophy, (ii) mathematical and statistical methods, and (iii) those emerging from critical data/algorithm/information studies. We also survey the field and explore emerging trends by examining the rapidly growing body of literature that falls under the broad umbrella of AI Ethics. To that end, we read and annotated peer-reviewed papers published over the past four years in two premier conferences: FAccT and AIES. We organize the literature based on an annotation scheme we developed according to three main dimensions: whether the paper deals with concrete applications, use-cases, and/or people's lived experience; to what extent it addresses harmed, threatened, or otherwise marginalized groups; and if so, whether it explicitly names such groups. We note that although the goals of the majority of FAccT and AIES papers were often commendable, their consideration of the negative impacts of AI on traditionally marginalized groups remained shallow. Taken together, our conceptual analysis and the data from annotated papers indicate that the field would benefit from an increased focus on ethical analysis grounded in concrete use-cases, people's experiences, and applications as well as from approaches that are sensitive to structural and historical power asymmetries.

AAPI Data is a publisher of demographic data and policy research on Asian Americans and Pacific Islanders.

AI4ALL is a US-based nonprofit dedicated to increasing diversity and inclusion in AI education, research, development, and policy.

The AI Now Institute aims to produce interdisciplinary research and public engagement to help ensure that AI systems are accountable to the communities and contexts in which they’re applied.

The Algorithmic Justice League’s mission is to raise awareness about the impacts of AI, equip advocates with empirical research, build the voice and choice of the most impacted communities, and galvanize researchers, policy makers, and industry practitioners to mitigate AI harms and biases. We’re building a movement to shift the AI ecosystem towards equitable and accountable AI.

Data for Black Lives is a movement of activists, organizers, and scientists committed to the mission of using data to create concrete and measurable change in the lives of Black people.

We integrate research in the humanities, social sciences, computer and data sciences to understand and address online polarization, abusive language, discriminatory algorithms and mis/disinformation.

The Distributed AI Research Institute is a space for independent, community-rooted AI research, free from Big Tech’s pervasive influence.

Feminist.AI works to put technology into the hands of makers, researchers, thinkers and learners to amplify unheard voices and create more accessible AI for all. We create spaces where intergenerational BIPOC and LGBTQIA+ womxn and non-binary folks can gather to build tech together that is informed by our cultures, identities and experiences. We engage with intersectional feminism to spotlight our stories, inventions, designs and leadership, and to co-create more equitable futures.

The Indigenous Protocol and Artificial Intelligence (A.I.) Working Group develops new conceptual and practical approaches to building the next generation of A.I. systems. The working group is interested the following questions: From an Indigenous perspective, what should our relationship with A.I. be? How can Indigenous epistemologies and ontologies contribute to the global conversation regarding society and A.I.? How do we broaden discussions regarding the role of technology in society beyond the largely culturally homogeneous research labs and Silicon Valley startup culture? How do we imagine a future with A.I. that contributes to the flourishing of all humans and non-humans?

The United States Indigenous Data Sovereignty Network (USIDSN) helps ensure that data for and about Indigenous nations and peoples in the US (American Indians, Alaska Natives, and Native Hawaiians) are utilized to advance Indigenous aspirations for collective and individual wellbeing. USIDSN’s primary function is to provide research information and policy advocacy to safeguard the rights and promote the interests of Indigenous nations and peoples in relation to data.

C2i2 is a critical internet studies community committed to racial justice, democracy, & civil rights.

The Women in AI Ethics™ (WAIE) is a fiscally sponsored project of Social Good Fund, a California nonprofit corporation and registered 501(c)(3) organization with a mission to increase recognition, representation, and empowerment of brilliant women in this space who are working hard to save humanity from the dark side of AI.

From Wired. Hiring sociocultural workers to correct bias overlooks the limitations of these underappreciated fields.

From the New York Times. An unsettling glimpse at the digitization of education.

In this course, we will focus on topics that are both urgent and practical. In keeping with my teaching philosophy, we will begin with two active, real-world areas (disinformation and bias) to provide context and motivation, before stepping back in Lesson 3 to dig into foundations of data ethics and practical tools. From there we will move on to additional subject areas: privacy & surveillance, the role of the Silicon Valley ecosystem (including metrics, venture growth, & hypergrowth), and algorithmic colonialism.

Hosted by Rund Abdelfatah and Ramtin Arablouei. Artificial intelligence, gene modification, and self-driving cars are causing fear and uncertainty about how technology is changing our lives. But humans have struggled to accept innovations throughout history. In this episode, we explore three innovations that transformed the world and show how people have adapted — and ask whether we can do the same today.

From the UF Libraries Women's & Gender Studies Guide.