Understanding power positions in a new digital landscape: perceptions of Syrian refugees and data experts on relocation algorithm

Abstract

This study explores the differences and similarities between the perceptions of data experts and refugees as data subjects, in the context of a refugee relocation algorithm. The study conducted in-depth interviews with data experts and Syrian refugees in Estonia and Turkey. The results indicate that both refugees and data experts acknowledge the algorithms' potential power for structuring the everyday life experiences of people. Whereas refugees mainly focused on cultural and social concerns, the data experts underlined the importance of refugees' agency and the potential drawbacks of algorithms in terms of transparency and accountability. While both groups of interviewees thought the relocation algorithm could be useful especially in economic terms, the study demonstrates that algorithms create complex power relations and place extra pressure on both refugees and data experts. The new digital landscapes produced by algorithms entail a 'triple agency'an agency of experts developing and using these datafied solutions, an agency of data subjects being targets of those calculations, and an agency of algorithms. For solving the issue of 'false authority', where the modelling of spatial choice cannot grasp the socio-cultural reality, it is necessary to consider the socio-cultural context of the calculative devices. A paradigm shift in machine learning is necessary from learning machines as autonomous subjects to machines learning from social contexts and individuals' experiences. Rather than experimenting with algorithmic solutions to speed up decisions about human lives, migration policies and relevant datafied solutions should consider the diversity of human experiences expressed in individuals' everyday life.

(Non)negotiable spaces of algorithmic governance: Perceptions on the Ubenwa health app as a ‘relocated’ solution

“…These socio-cultural issues inform and are informed by the use of these same ‘relocated’ solutions. Accordingly, our study confirms other research, particularly research that advocates a paradigm shift in how academic understand learning machines (Bengio et al, 2019; Masso and Kasapoğlu, 2020). In other words, we ought to think of machines as capable of learning from social contexts and individual experiences, rather than as autonomous subjects.…”

“…We applied a purposeful sampling technique (Suri, 2011) to collect interview data. As indicated in previous theoretical (Kitchin and Dodge, 2014) and empirical studies (Masso and Kasapoğlu, 2020), the application of the relocated algorithmic solutions assumes mutual negotiations of algorithms and space through multiple, individual agencies.…”

“…Algorithmic governance, as a form of management by and based on technology, still tends to overlook social context-specificity because the idea of ‘data on the move’ instead of ‘people on the move’ clashes with the current, formal understanding of diversity. Our study contributes to the emerging paradigm of contextual research (Loukissas, 2019; Masso and Kasapoğlu, 2020; Kasapoğlu et al, 2021; Petrović et al, 2020) by assessing individuals’ perceptions about the algorithmic governance and the tensions that arise in a local context. Because algorithms do not determine in absolute, non-negotiable ways how one space is converted into another (Kitchin and Dodge, 2014), this study assesses the mutual dependencies in algorithmic contexts, as expressed by individual end users in the process of algorithmic governance.…”

Co-designing algorithms for governance: Ensuring responsible and accountable algorithmic management of refugee camp supplies

“…For example in case of this study, knowledge of availability of spaces in refugee camps could support migration decision-making. However, similar to other studies in which refugees are usually data subjects rather than data experts (Masso and Kasapoglu, 2020), we experienced barriers in involving this group. It proved difficult and unethical to ask vulnerable populations to invest their time in a demanding long-term pilot project with yet uncertain outcomes for their personal situation.…”

Intersectional Inquiry, on the Ground and in the Algorithm

“…Researchers and activists have long pursued ways of including those who are affected by technology into the design and evaluation of these systems. Recent work by researchers working with Syrian refugees on algorithms (Kasapoglu et al, 2021; Masso & Kasapoglu, 2020) argues for a reconsideration of algorithmic governance that would involve more collaborative and flexible feedback loops between various data “agents”—authorities, experts and users—to improve technocratic means of “caring for the self.” Arguably work by large technology firms is already at least performing feedback loops through funded initiatives like AI for Accessibility (Microsoft), PAIR (People + AI Research at Google), and Responsible AI (Meta). A challenge for such approaches in AI is simply the scale at which language models are developed and trained—nuanced user feedback, particularly in relation to groups that represent intersectional minorities, has to be integrated into systems that take months and millions of dollars to train.…”