The Turing Lectures series features influential figures from the world of data science and artificial intelligence. The latest lecture, which took place in October, was given by Dr Abeba Birhane, Senior Fellow in Trustworthy AI at Mozilla Foundation, and Adjunct Lecturer/Assistant Professor at the School of Computer Science and Statistics at Trinity College Dublin, Ireland. In her talk, Abeba covers the topic of biases in data and the downstream impact on AI systems, and shows how this can lead to unfair outcomes in our daily lives. She explains why it's such a big issue and how researchers, activists and policy makers are tackling the problem. To watch the other lectures in the series, head to the The Turing Lectures webpage.

Foundation model development attracts a rapidly expanding body of contributors, scientists, and applications. To help shape responsible development practices, we introduce the Foundation Model Development Cheatsheet: a growing collection of 250+ tools and resources spanning text, vision, and speech modalities. We draw on a large body of prior work to survey resources (e.g. software, documentation, frameworks, guides, and practical tools) that support informed data selection, processing, and understanding, precise and limitation-aware artifact documentation, efficient model training, advance awareness of the environmental impact from training, careful model evaluation of capabilities, risks, and claims, as well as responsible model release, licensing and deployment practices. We hope this curated collection of resources helps guide more responsible development. The process of curating this list, enabled us to review the AI development ecosystem, revealing what tools are critically missing, misused, or over-used in existing practices. We find that (i) tools for data sourcing, model evaluation, and monitoring are critically under-serving ethical and real-world needs, (ii) evaluations for model safety, capabilities, and environmental impact all lack reproducibility and transparency, (iii) text and particularly English-centric analyses continue to dominate over multilingual and multi-modal analyses, and (iv) evaluation of systems, rather than just models, is needed so that capabilities and impact are assessed in context.

This is Atlantic Intelligence, an eight-week series in which The Atlantic's leading thinkers on AI will help you understand the complexity and opportunities of this groundbreaking technology. The bedrock of the AI revolution is the internet, or more specifically, the ever-expanding bounty of data that the web makes available to train algorithms. ChatGPT, Midjourney, and other generative-AI models "learn" by detecting patterns in massive amounts of text, images, and videos scraped from the internet. The process entails hoovering up huge quantities of books, art, memes, and, inevitably, the troves of racist, sexist, and illicit material distributed across the web. Earlier this week, Stanford researchers found a particularly alarming example of that toxicity: The largest publicly available image data set used to train AIs, LAION-5B, reportedly contains more than 1,000 images depicting the sexual abuse of children, out of more than 5 billion in total.

Hosted by Eleanor Drage and Kerry Mackereth, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. In this episode Eleanor and Kerry speak to Abeba Birhane, senior research fellow at Mozilla, about how cognition extends beyond the brain, why why we need to turn questions like "why aren't there enough black women in computing" on their head and actually transform computing cultures, and why human behaviour is a complex adaptive system that can't always be modelled computationally. Abeba Birhane is a cognitive scientist researching human behaviour, social systems, and responsible and ethical Artificial Intelligence (AI). She recently finished her PhD, where she explored the challenges and pitfalls of automating human behaviour through critical examination of existing computational models and audits of large scale datasets. She is currently a Senior Fellow in Trustworthy AI at Mozilla Foundation.

Two Stanford heavyweights have weighed in on the fiery AI sentience debate -- and the duo is firmly in the "BS" corner. The wrangle recently rose to a crescendo over arguments about Google's LaMDA system. Developer Blake Lemoine sparked the controversy. Lemoine, who worked for Google's Responsible AI team, had been testing whether the large-language model (LLM) used harmful speech. The 41-year-old told The Washington Post that his conversations with the AI convinced him that it had a sentient mind.

Hosted by Dylan Doyle-Burke and Jessie J Smith, Radical AI is a podcast featuring the voices of the future in the field of artificial intelligence ethics. In this episode Jess and Dylan chat to Abeba Birhane about "Robot Rights? Should we grant robots rights? What is moral relationality and how can it be useful for designing machine learning algorithms? What is the algorithmic colonization of Africa and why is it harmful?

A UCD student's research has resulted in the withdrawal of an 80-million image library used to train artificial intelligence systems. The research by PhD student Abeba Birhane found that hundreds of millions of images in academic datasets that are used to develop AI systems and applications are partly based on racist and misogynistic labels and slurs, according to the Irish Software Research Centre (Lero) and University College Dublin's Complex Software Lab. "Already, MIT has deleted its much-cited '80 Million Tiny Images' dataset, asking researchers and developers to cease using the library to train AI and ML system," said the software research centre in a statement. "MIT's decision came as a direct result of the research carried out by University College Dublin based Lero researcher Abeba Birhane and Vinay Prabhu, chief scientist at UnifyID, a privacy start-up in Silicon Valley." In the course of the work, the Lero statement says, Ms Birhane found the MIT database contained thousands of images labelled with racist and misogynistic insults and derogatory terms.