using memories to collectively explore implicit assumptions, values and context in practices of debiasing and discrimination-awareness
This workshop is part of the Critiquing and Rethinking Accountability, Fairness and Transparency (CRAFT) track of the ACM Conference on Fairness, Accountability, and Transparency (FAT*2020) taking place from January 27 to January 30 in Barcelona.
The workshop uses deconstruction to explore the implicit assumptions, values and beliefs that FAT researchers mobilize as part of their epistemic practices. It is for FAT researchers of all disciplines and aims at making visible how practices of computing are entrenched in power relations in complex and multi-layered ways.
Trying to disentangle the way in which structural discrimination, mundane ways-of-doing, and normative computational concepts and methods are intertwined, frequently raises the question of WHO are the actors that shape technologies and research agendas — who gets to speak and to define bias, (un)fairness, and discrimination in sociotechnical systems? This workshop aims at complicating this question by asking how “we” as researchers in FAT (often unknowingly) mobilize assumptions, values and beliefs that reflect our own embeddedness in power relations, our disciplinary ways of thinking, and our historically, locally, and culturally-informed ways of solving computational problems or approaching our research.
During the workshop a curated panel of FAT researchers engages in a deconstruction exercise which serves as a vantage point to make visible and analyze the normativity of technical approaches, methods, concepts and tools that are part of the repertoire of FAT research. The deconstruction traces the following issues:
1) FAT research frequently speaks of social bias that is amplified by algorithmic systems, of the problem of discriminatory consequences that is to be solved, and of underprivileged or vulnerable groups that need to be protected. What does this perspectivity imply in terms of the approaches, concepts, methods and metrics that are being applied? How do methods of debiasing and discrimination-awareness enact a perspective of privilege as their norm? What if designing systems for fairness starts from removing the normative epistemic power of a perspective of privilege instead?
2) FAT research has emphasized the need for multi- or interdisciplinary approaches to get a grip on the complex intertwining of social power relations and the normativity of computational methods, norms and practices. Clearly, multi- and interdisciplinary research includes different normative frameworks and ways of thinking that need to be negotiated. This is complicated by the fact that these frameworks are not fully transparent and ready for reflection. What are the normative implications of interdisciplinary collaboration in FAT research?
3) While many problems of discrimination, bias, unfairness, marginalization, exploitation can be similar across places, they can also have specific local shapes that we must take into account both scientifically and practically for real-world relevance to materialize, and for us to talk to real on-the-ground challenges. How can FAT research e.g. consider historically grown specifics (such as the effects of different colonial histories)? Do these specifics make patterns of discrimination have different and more nuanced dimensions than clear-cut ‘redlining’, and what does this imply?
In order to explore these questions, we use a deconstructive method called ‘mind scripting’. The method is based in theories of discourse, ideology, memory and affect and aims at investigating hidden patterns of meaning making in written memories of the panelists. While the above higher level questions are quite complex, the method manages to break down this complexity by using a set of questions that will guide us through the deconstruction in an easy way. In this manner, the workshop strives to challenge some of the implicit norms and tensions in FAT research and to creatively and collectively trigger future directions that were difficult to see previously.