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This anthology was curated from stories of living with data and AI in/from the majority world, narrated at a storytelling workshop in October 2021 organized by Data & Society Research Institute.

We present EKILA; a decentralized framework that enables creatives to receive recognition and reward for their contributions to generative AI (GenAI). EKILA proposes a robust visual attribution technique and combines this with an emerging content provenance standard (C2PA) to address the problem of synthetic image provenance – determining the generative model and training data responsible for an AI-generated image. Furthermore, EKILA extends the non-fungible token (NFT) ecosystem to introduce a tokenized representation for rights, enabling a triangular relationship between the asset’s Ownership, Rights, and Attribution (ORA). Leveraging the ORA relationship enables creators to express agency over training consent and, through our attribution model, to receive apportioned credit, including royalty payments for the use of their assets in GenAI.

Symbolic language can help us grasp the nature and power of what is coming

Nora N. Khan assembles a cast of luminaries to consider the far-reaching implications of AI and computational culture. –$40

Co-authored with Grace Carney A few months ago Fred kicked off a conversation about what the “native” applications of AI technology will be. What are the

Conversation around generative AI tends to focus on whether its development is open or closed. It's more responsible to envision releases along a gradient.

🔬 Research Summary by Shreyasha Paudel, a Ph.D. student at the University of Toronto with an interdisciplinary research focus that combines Human-Computer Interaction with critical theories from…

Stable Diffusion revolutionised image creation from descriptive text. GPT-2, GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of language tasks. ChatGPT introduced such language models to the general public. It is now clear that large language models (LLMs) are here to stay, and will bring about drastic change in the whole ecosystem of online text and images. In this paper we consider what the future might hold. What will happen to GPT-{n} once LLMs contribute much of the language found online? We find that use of model-generated content in training causes irreversible defects in the resulting models, where tails of the original content distribution disappear. We refer to this effect as Model Collapse and show that it can occur in Variational Autoencoders, Gaussian Mixture Models and LLMs. We build theoretical intuition behind the phenomenon and portray its ubiquity amongst all learned generative models. We demonstrate that it has to be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of content generated by LLMs in data crawled from the Internet.

How many humans does it take to make tech seem human? Millions.

Read and download Artificial Intelligence and Post-Capitalism by Thanasis Apostolakoudis on OA.mg

This paper explores the important role of critical science, and in particular of post-colonial and decolonial theories, in understanding and shaping the ongoing advances in artificial intelligence. Artificial Intelligence (AI) is viewed as amongst the technological advances that will reshape modern societies and their relations. Whilst the design and deployment of systems that continually adapt holds the promise of far-reaching positive change, they simultaneously pose significant risks, especially to already vulnerable peoples. Values and power are central to this discussion. Decolonial theories use historical hindsight to explain patterns of power that shape our intellectual, political, economic, and social world. By embedding a decolonial critical approach within its technical practice, AI communities can develop foresight and tactics that can better align research and technology development with established ethical principles, centring vulnerable peoples who continue to bear the brunt of negative impacts of innovation and scientific progress. We highlight problematic applications that are instances of coloniality, and using a decolonial lens, submit three tactics that can form a decolonial field of artificial intelligence: creating a critical technical practice of AI, seeking reverse tutelage and reverse pedagogies, and the renewal of affective and political communities. The years ahead will usher in a wave of new scientific breakthroughs and technologies driven by AI research, making it incumbent upon AI communities to strengthen the social contract through ethical foresight and the multiplicity of intellectual perspectives available to us; ultimately supporting future technologies that enable greater well-being, with the goal of beneficence and justice for all.

Artificial Intelligence (AI) is transforming the world as we know it, implying that it is up to the current generation to use the technology for ''good.'' We argue that making good use of AI constitutes aligning it with the wellbeing of conscious creatures. However, designing wellbeing-aligned AI systems is difficult. In this article, we investigate a total of twelve challenges that can be categorized as related to a lack of knowledge (how to contextualize, operationalize, optimize, and design AI for wellbeing), and lack of motivation (designing AI for wellbeing is seen as risky and unrewarding). Our discussion can be summarized into three key takeaways: 1) our understanding of the impact of systems on wellbeing should be advanced, 2) systems should be designed to promote and sustain wellbeing intentionally, and 3), above all, Positive AI starts with believing that we can change the world for the better and that it is profitable.

A lot is required to achieve those shareable trophies of taming ChatGPT into producing what you want: tinkering, rejected drafts, invocations of the right spells (I mean prompts!), and learning from observing Twitter threads and Reddit forums. But, those early efforts remain hidden, a kind of surviv

🔬 Research Summary by Elizabeth Seger, PhD, a researcher at the Centre for the Governance of AI (GovAI) in Oxford, UK, investigating beneficial AI model-sharing norms and practices.

Ethical Theory and Moral Practice - The idea that technologies can change moral beliefs and practices is an old one. But how, exactly, does this happen? This paper builds on an emerging field of...

The Vera List Center for Art and Politics is a research center and a public forum for art, culture, and politics.

For decades, the conventional wisdom about Silicon Valley was that it leaned progressive. And by many measures (like donations by Big Tech employees to political candidates), the industry has been…

Lessons from young people on AI’s thin line between productivity and interference.

My first computer was gifted to me by my mother. A talented computer programmer herself, she preferred I learn programming than memorising multiplication tables at school. Almost two decades later, as I researched and wrote my undergraduate thesis on the history of information systems and the effective role of computing, I realised something. All the literature that I was reading around the advent of AI, communication theory and cybernetics came from a limited demographic; most of whom were priv

By asking language models to fill in the blank, we can probe their understanding of the world.

“Piercing the corporate veil” is when there is a legal decision made to hold a company’s shareholders or directors responsible for the…

Writer James Bridle explores advancements in AI and how humankind's collaboration with machines could make for a fascinating creative future

AI Takeoff refers to the process of an artificial general intelligence going from a certain threshold of capability (often discussed as "human level") to being super-intelligent and capable enough to control the fate of civilization. There has been much debate about whether AI takeoff is more likely to be "slow" or "fast". AI takeoff is sometimes casually referred to as AI FOOM.

An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible. Using GPT-3 175B as an example -- deploying independent instances of fine-tuned models, each with 175B parameters, is prohibitively expensive. We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared to GPT-3 175B fine-tuned with Adam, LoRA can reduce the number of trainable parameters by 10,000 times and the GPU memory requirement by 3 times. LoRA performs on-par or better than fine-tuning in model quality on RoBERTa, DeBERTa, GPT-2, and GPT-3, despite having fewer trainable parameters, a higher training throughput, and, unlike adapters, no additional inference latency. We also provide an empirical investigation into rank-deficiency in language model adaptation, which sheds light on the efficacy of LoRA. We release a package that facilitates the integration of LoRA with PyTorch models and provide our implementations and model checkpoints for RoBERTa, DeBERTa, and GPT-2 at https://github.com/microsoft/LoRA.