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Mutual learning has been explicitly stated as a core tenet in the canon of participatory design, socially engaged art and other community-oriented and -driven “making” processes. Yet, what is mutual learning and how does it concretize in the world?

Every dataset communicates a different perspective. When you shift your perspective, your conclusions can shift, too.

The world of Artificial-Intelligence generated art has exploded over the last twelve months. In January 2021, OpenAI released two models that changed the game: DALL-E and CLIP. These models showed what might be possible by generating visual art from text-based prompts.

Demanding inclusive and radically diverse digital futures

We analyze how advice from an AI affects complementarities between humans and AI, in particular what humans know that an AI does not know: “unique human knowled

The aim to transcend that which makes us human is not the path to serving humanity.

A year in workshops

The Universal Hip Hop Museum is coming to New York City, and an MIT team led by D. Fox Harrell has designed for it unique creative experiences at the intersection of art, learning, and technology.

I was on a panel tonight discussing Ethics in Design Research. I’m on a lot of panels about design and ethics because I’ve worked in AI…

Data-Driven Exploitation in the Gig Economy

The role of a designer is to intentionally shape the world around us with the resources we have available. That world is increasingly shaped by data sets, algorithms, and pre-trained models.

Learn about bias and variance in our second animated data visualization.

When it was first published in 1972, Hubert Dreyfus's manifesto on the inherent inability of disembodied machines to mimic higher mental functions caused an uproar in the artificial intelligence community. The world has changed since then. Today it is clear that "good old-fashioned AI," based on the idea of using symbolic representations to produce general intelligence, is in decline (although several believers still pursue its pot of gold), and the focus of the Al community has shifted to more complex models of the mind. It has also become more common for AI researchers to seek out and study philosophy. For this edition of his now classic book, Dreyfus has added a lengthy new introduction outlining these changes and assessing the paradigms of connectionism and neural networks that have transformed the field.At a time when researchers were proposing grand plans for general problem solvers and automatic translation machines, Dreyfus predicted that they would fail because their conception of mental functioning was naive, and he suggested that they would do well to acquaint themselves with modern philosophical approaches to human beings. What Computers Can't Do was widely attacked but quietly studied. Dreyfus's arguments are still provocative and focus our attention once again on what it is that makes human beings unique.

Pointing out the spaces & practices emerging at this intersection.

Understand design heuristics for products with AI-driven user experiences. Learn more about the process at Adobe XD Ideas.

By Julian Posada via AI Now Institute | An investigation on the experiences of people who annotate data for AI and what the implications are when society integrates a heavily designed AI labor system.

How data science is woven into the fabric of Stitch Fix.

A method you can follow to build XAI applications, developed through our research and collaboration with IBM Design for AI

The ethics, data, privacy and complexity are not only presenting the constraints for the development and design of the intelligent systems but also open up a space for new modes of thinking. Through real-life examples, I’ll discuss how designers can reimagine the goals and focus on the unique attributes, potentials of the evolving nature of the emerging intelligent systems as well as its implications on the society and the individuals.

By Tom Simonite (WIRED) | Privacy concerns about AI systems are growing. So researchers are testing whether they can remove sensitive data without retraining the system from scratch.

With the No-Code revolution around the corner, and the coming of new-age technologies like GPT-3 we may see a stark difference between the…

By Martin Wattenberg, Fernanda Viégas, and Moritz Hardt | As machine learning is increasingly used to make important decisions across core social domains, the work of ensuring that these decisions aren't discriminatory becomes crucial. Here we discuss "threshold classifiers," a part of some machine learning systems that is critical to issues of discrimination.

Hyper-intelligent algorithms are not going to take over the world for these five reasons.