June 21-24 2022 In-person event (the exact day is still unknown).
SEDL 3.0 is divided into three parts:
Artistic narratives as a form of communication
Art served scientific purposes in the attempt to explain the events of life. With the increasing complexity of society, artworks, specifically paintings and orally transmitted plays, also became educational tools, teaching the illiterate population about religious doctrine. This process wasn't of course only meant to educate the public, it also played a major role in sharing and maintaining specific ideas and concepts, usually those officially recognised by the ruling classes throughout history. As a form of resistance towards this phenomenon, creative work became a way to oppose such ideologies too. Thus Art became an effective way to expose the public to often controversial new ideas and perspectives, often opposing or deviating from what’s accepted and acceptable.
In this part, we would like to discuss the potential influences of artistic communications as a tool of cross-disciplinary knowledge transfer. We would also like to discuss the impact of machine learning in the art community and how this cross-disciplinary communications could create potential harms and benefits. We aim to bring together artists, including computational artists who use machine learning as a tool of creation and communication with researchers who work on the intersection of art and society. Discussion points include
Care and ethical considerations in ML-oriented creation and artistic storytelling
Emphasizing marginalized narratives and representation issues in ML
Critique of narratives around large scale ML techniques potentially accelerating harms
We believe that a great degree of care and research should be put on this way of communication to prevent further marginalizing of the communities that are already marginalized. We would like to learn how art serves as a point of interdisciplinary communications and what we, as the people at the intersection of Theoretical and Applied ML, FAccT, and Social Science, could implement in our communities.
Building an inclusive lexicon for ML
Communication across communities through various forms of narratives necessarily rely on shared vocabularies whose content may diverge depending on which community uses it. Concepts in machine learning via this shared vocabulary are being used by many communities, however, diverging meanings arise as a result of disciplinary differences. In this part, we aim to build an inclusive lexicon of machine learning. We believe that building a shared vocabulary will help in cross disciplinary communication and will complement the other parts of this proposal.
We plan to work towards an inclusive lexicon of machine learning with particular attention to the social impact of AI (starting with a description of what AI is). The concepts will not only be descriptive but also prescriptive, especially for concepts with potential social impact. The lexicon will be built communally and the preliminary work will be done before the event. During the 90 minute discussion the key topics will include:
How to build an inclusive lexicon focusing on diverging vocabulary
Usefulness for descriptions and the need for prescriptive entries in the lexicon
Baseline definitions, building a map of machine learning, and its editorial structure
Clarifying fuzzy concepts with prioritizing their social impact
We will include a discussion of related references and find ways to make this effort public.
ML evaluations, standards and critiques
Earlier parts of the proposed session explore how individual context shapes interpretation of our shared vocabulary, and the remarkable ability of art(ists) to communicate with people across contexts. However, a crucial perspective remains absent from our discussion, that of real people whose lives are affected by our research, systems, and practices.
This final part will be a semi-structured panel-style discussion that will consider practical problems surrounding participatory ML evaluation. From data collection, to algorithm design and implementation, to application and deployment in society, ML requires careful consideration of its impacts. Core questions discussed in this breakout will include how (often dynamic) values are encoded in our evaluation practices; the participation and role of broad and inclusive stakeholder groups; and the conflict between “standardized” evaluation practices and sensitivity to a system’s eventual context and use.
Additionally, this part will engage with participatory design literature from the human-computer interaction (HCI) and social computing community. Such scholarship historically has been of focus within these communities. Especially as it interfaces with scholarship, it explores the intersection of gender and participatory design, participatory design and health and participatory design for community focused research. Drawing from community-focused design practices that have been explored within HCI, we hope to explore the ways in which participatory design can support critical research within the FAccT community.
This part will engage participants in interrogating if it is possible to develop standard practices for evaluation, how this might be approached, and how different contexts require different solutions. Prior to the event, participants will be invited to complete an online survey whose results will frame a starting point for discussion.