Is Data Subjective?
Eyebeam is excited to announce the first in our series of Eyebeam Exchange weekend workshops, Learning AI: Creating Emotional Datasets and Deep Learning in the Browser with ML5 on Saturday, April 21st from 10am to 5pm.
Can data be subjective? The influences, experiences and perspectives of those creating datasets that power digital decision making, who tagged them, who funded them and where they were made can attribute to prejudice which may tarnish algorithms.
As our current resident, Stephanie Dinkins, says, “If the information used to process data is embedded with historical biases, the resulting algorithms can perpetuate racist, sexist, or classist ideologies.” While in residence at Eyebeam, Stephanie is working to create a unique culturally attuned AI entity in collaboration with coders, engineers and in close consultation with local communities of color to work toward the goals of its creators.
This education program, is one part of a holistic approach, to understanding and being critical of Artificial Intelligence. In our first Eyebeam Assembly, on April 24th, Stephanie Dinkins in conversation with Bina48, Bruce Duncan will discuss how AI can be a form of representation, community generated storytellers and animated transmitters of culture and Stephanie will start this Exchange on April 21st with a short introduction to frame the day’s learnings.
Join us to be at the forefront of conscientious technology-building. As we grow as an institution, we deem it necessary to inject our core values of Openness, Justice and Invention into programs that invite artists to create curriculum. Within artists leading, we aim to foster collaboration through project-based learning in order to create imaginative and just invention and design of our shared future.
Guided by these ideas, Eyebeam Exchange is a monthly education series open to adults, young and old with different experience levels with technology. Topics vary but are influenced by our current residents, programs and core values at Eyebeam.
We stress curiosity over skill mastery. Students should leave feeling emboldened, asking questions about how they can manipulate their skills to help our communities. Our goal is to create an engaging, comprehensive learning experience between our workshops, talks, exhibitions and events. Workshops will be led by teaching artists and will provide context, tools, resources and examples to learn and master subjects after students leave.
Hannah Davis will be teaching session one of our first Eyebeam Exchange, Emotional / Subjective data and dataset creation. She is the creator of the algorithm TransProse, a mechanism that translates novels and other large texts into music as well as the 2017 AI grant recipient. Session 1 will explore the tools used for deep learning, a field that studies machine learning methods based on learning data representations, and datasets that can be created by them.
This course is for those with basic programming knowledge, particularly in Python, though aimed at beginners who want to explore datasets with a critical eye by learning how to find subjectivity within “objective” datasets. We’ll invite students to explore “artisanal data” which is explicitly subjective data for artistic purposes.
The objective of this workshop is to ask, how can a culture’s values be transformed into bias? How can we avoid bias in computer systems through experimenting with labels and vocabulary? Students will be asked to then, look inside themselves, to see how they may be cognizant during the collection, analysis and usage stages.
In the second session, Deep Learn Web, Cristóbal Valenzuela, a technologist and software developer interested in building digital tools and interactive experiences whose work has been sponsored by Google and the Processing Foundation and Yining Shi, a creative technologist interested in developing novel ways of learning and teaching computational topics will be focus on deep learning in the browser. By using ML5, an experimental library void of complicated frameworks, we invite beginners to embark on critical adventures within machine learning.
With the help of artists, we can foster a community of learners, makers, artists to construct conversation and collaborative learning around AI.