Delft AI Lab Lunch with AI Futures Lab & IRIS Lab

About AI Futures Lab

AI for Rights & Justice

AI is increasingly widespread, but poses a challenge for designers: it is complex, entangled, plural and indeterminate, and it changes over time. Harms that can result from poorly designed AI systems are both subtle and large scale, and socio-legal contexts evolve in response to the actions of computational systems. New understandings of rights and justice and flexible responses to a changing world are needed, which at the same time support human agency, human rights, wellbeing and justice.

The AI Futures Lab will address the current knowledge gap by combining Industrial Design Engineering (IDE) post-industrial design research and methodologies. These will be applied to machine ethnography, to experiential AI and to in-the-wild AI prototyping using Technology, Policy and Management (TPM) methodologies of comprehensive engineering and design for values. We will explore configurations of people and AI around rights and justice, aiming to expand both scientific knowledge and public understanding of AI capabilities. The empirical research focus of our lab extends from remote work to robotics and security. Our goal is a tangible and vibrant set of prototypes, experiences and theories that map out ways in which design can be engaged to deploy AI and machine learning in support of rights and justice. By prototyping new relationships ‘WITH AI’ that are respectful of agency, rights, and justice, we will open up spaces for new developments ‘IN AI’.

About IRIS

AI for quantitative bioimaging

The IRIS Lab develops AI-based technology that improves microscopy methods for biomedical use. The technology will be demonstrated on electron, optical, and ultrasound imaging but is applicable much wider. It will be able to unravel biological processes, from a molecular level up to a much larger scale. AI is currently propelling nearly all computer vision applications in life science. Neural networks are trained to perform a certain task using very large sets of data, but which decisions these networks take, and why, is essentially a ‘black box’. For scientific applications this black box causes a serious dilemma: the learned knowledge is not transparent and cannot be reliably reused. Furthermore, how well a method performs its task is strongly tied to the specific data it was trained on and therefore cannot be easily used across different modalities of biological imaging that all produce different image data.

The aim of the IRIS lab is therefore to open the black box of AI. This can be accomplished by having the neural network approximate physical and system operations separately to enable the incorporation of known physical principles of image formation. This approach has the potential to outperform more classical methods in core image processing tasks. The three goals are: efficient detection and tracking of objects, accurate labeling and classification of the objects and meaningful segmentation of the objects. IRIS Lab will demonstrate their AI-methodology in electron- and fluorescence-microscopy and ultrasound imaging, but aims to become a campus-wide resource for researchers in any quantitative imaging- and visualization-intensive field.