Of “Working Ontologists” and “High-Quality Human Components”:

Allhutter, Doris

doi:10.2307/j.ctvc77mp9.25

Cited by 29 publications

(10 citation statements)

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“…Algorithmic bias is a socio-technical phenomenon that may emerge from bias in the training data of systems, their classification or the models that are built from this data (see e.g., Sandvig et al, 2014;Buolamwini and Gebru, 2018). It can also be located in the implicit epistemic norms of computational approaches, concepts, methods and tools (Dobbe et al, 2018;Allhutter, 2019;Selbst et al, 2019).…”

Section: Coproducing Austerity Politics and Algorithmic Classificationsmentioning

confidence: 99%

“…A coproductionist perspective needs to acknowledge that statistical classification co-constitutes the entities that it differentiates between. When a system differentiates between groups of people for a particular purpose based on their gender, ethnicity, age, dis/ability, or socioeconomic status, it re-iterates and coproduces meanings of what these categories imply in a certain context (Allhutter, 2019). While these categories are intersectional and contextually contingent, algorithmic systems frequently model them as dichotomous and essentialist attributes of people (see e.g., Hu and Kohler-Hausmann, 2020).…”

Section: Coproducing Austerity Politics and Algorithmic Classificationsmentioning

confidence: 99%

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Algorithmic Profiling of Job Seekers in Austria: How Austerity Politics Are Made Effective

et al. 2020

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As of 2020, the Public Employment Service Austria (AMS) makes use of algorithmic profiling of job seekers to increase the efficiency of its counseling process and the effectiveness of active labor market programs. Based on a statistical model of job seekers' prospects on the labor market, the system—that has become known as the AMS algorithm—is designed to classify clients of the AMS into three categories: those with high chances to find a job within half a year, those with mediocre prospects on the job market, and those clients with a bad outlook of employment in the next 2 years. Depending on the category a particular job seeker is classified under, they will be offered differing support in (re)entering the labor market. Based in science and technology studies, critical data studies and research on fairness, accountability and transparency of algorithmic systems, this paper examines the inherent politics of the AMS algorithm. An in-depth analysis of relevant technical documentation and policy documents investigates crucial conceptual, technical, and social implications of the system. The analysis shows how the design of the algorithm is influenced by technical affordances, but also by social values, norms, and goals. A discussion of the tensions, challenges and possible biases that the system entails calls into question the objectivity and neutrality of data claims and of high hopes pinned on evidence-based decision-making. In this way, the paper sheds light on the coproduction of (semi)automated managerial practices in employment agencies and the framing of unemployment under austerity politics.

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Section: Coproducing Austerity Politics and Algorithmic Classificationsmentioning

confidence: 99%

Section: Coproducing Austerity Politics and Algorithmic Classificationsmentioning

confidence: 99%

Algorithmic Profiling of Job Seekers in Austria: How Austerity Politics Are Made Effective

et al. 2020

Self Cite

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“…These intertwined structures, materialities, practices, and affects configure a sociomaterial apparatus that is historically contingent. Using examples from an ethnographic study (Allhutter 2019), I show how the apparatus of semantic computing and its infrastructuring practices are materially and discursively performative in their co-emergence with techno-epistemic discourses and politicoeconomic structures.…”

Section: Doris Allhutter Institute Of Technology Assessment Austrian ...mentioning

confidence: 99%

Semantic Media: Political Economy Perspectives on Platformized Fact Production

Iliadis

Ford

Allhutter

et al. 2023

SPIR

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Large media platforms are now in the habit of providing facts in their products and representing knowledge to various publics. For example, Google’s Knowledge Graph is a database of facts that Google uses to provide quick answers to publics who use their products, while Wikipedia has a product called Wikidata that similarly stores facts about the world in data formats through which various apps can retrieve the data. Microsoft, Amazon, and IBM use similar fact storing and retrieval techniques in their products. This panel introduces papers that take a political economy perspective on such platformaized versions of fact production and examines the underlying infrastructures, histories, and modeling techniques used in such knowledge representation systems. Knowledge representation, long a central topic in archiving work in library and information sciences, is a key feature of platforms and practiced by internet companies more broadly. Much of this work has historically centered on metadata models that seek to organize and describe information in standardized ways. In the context of expanding this data organizing and labeling work into the wider web, one of the main facilitators was the “Semantic Web” project proposed by Tim-Berners Lee and the World Wide Web Consortium (W3C). Today, many of the same principles, technologies, and standards that were proposed by those early projects in metadata modeling from groups like W3C are found at companies like Google and Facebook, organizations like Wikipedia, government portals, and beyond. These platform metadata models are typically produced by industry professionals (e.g., taxonomists, ontologists, knowledge engineers, etc.) who help structure information for algorithmic processing on platforms and their recommender systems. Such structured information is supposed to add a layer of contextual expressivity to web data that would otherwise be more difficult to parse, though the issue of context control is not unproblematic in relation to statements of facts. In many of these automated systems, metadata models contribute to articulating ready-made facts that then travel through these systems and eventually reach the products that are engaged by everyday web users. This panel connects scholars working in information, media studies, and science and technology studies to discuss these semantic technologies. The first paper presents data gathered from interviews with semantic web practitioners who build or have built metadata models at large internet and platform companies. It presents results from a qualitative study of these platform data management professionals (collectively referred to as “metadata modelers”) and draws from unstructured interviews (n=10) and archival research. The paper describes the image of a metadata ecology along with selected work-related contestations expressed by interview subjects regarding some of the difficulties and intractable problems in metadata modeling work. The paper includes a discussion of the political economy of platform semantics through an examination of critical semantic web literature and ends with some policy concerns. The second paper translates the method of tracing “traveling facts” from science studies to the context of online knowledge about evolving, historic events. The goal is to understand the socio-political impact of the semantic web as it has been implemented by monopolistic digital platforms and how such practices intersect in the context of Wikipedia, where the majority of knowledge graph entities are sourced from. The paper describes how the adoption (and domination) by platform companies of linked data has catalyzed a re-shaping of web content to accord with the question and answer linked data formats, weakening the power of open content licenses to support local knowledge and consolidating the power of algorithmic knowledge systems that favor knowledge monopolies. The third paper discusses building a semantic foundation for machine learning and examines how information infrastructures that convey meaning are intimately tied to colonial labor relations. It traces the practice of building a digital infrastructure that enables machines to learn from human language. The paper describes examples from an ethnographic study of semantic computing and its infrastructuring practices to show how such techniques are materially and discursively performative in their co-emergence with techno-epistemic discourses and politico-economic structures. It examines sociomaterial process in which classifications, standards, metadata, and methods co-emerge with processes of signification that reconstitute and/or shift hegemonic ecologies of knowledge. The fourth paper evaluates and examines the ethics of “free” data (CC-0) in Wikidata by evaluating the sources and usage of data from and within Wikidata. From knowledge graphs to AI training, Wikidata is the semantic web platform that is being used across the Internet to power new platforms. Through a consideration of the ways in which Wikidata scrapes Wikipedia’s “share alike” knowledge through scraping metadata and the significant donations and partnerships from large technology firms (Google in particular), this paper addresses ethical concerns within the largest semantic web platform, how these transformations of knowledge alienate donated volunteer labor, and offers some ways in which these issues might be mitigated.

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“…Further information about Google's Knowledge Graph is provided in a coauthored research paper by engineers at competing platform companies (such publications are a relatively rare occurrence in the corporate world), where Google researchers describe the Knowledge Graph as "a long-term, stable source of class and entity identity that many Google products and features use behind the scenes" and that it "helps Google products interpret user requests as references to concepts in the world of the user" (Noy et al, 2019). The researchers at these companies (Allhutter [2019] describes such people as "working ontologists") further describe how knowledge graphs help with actions; they recognize "that certain kinds of interactions can take place with different entities"-for example, a search for "'Russian Tea Room' provides a button to make a reservation, while a query for 'Rita Ora' provides links to her music on various music services" (Noy et al, 2019). The Google Knowledge Graph currently contains roughly 1 billion entities and 70 billion assertions related to facts about the world-in comparison, Microsoft's contains 2 billion entities and 55 billion facts, and Facebook's has 50 million entities and 500 million assertions (Noy et al, 2019).…”

Section: Knowledge Graphsmentioning

confidence: 99%

Fast Facts: Platforms From Personalization to Centralization

Iliadis,

Ford

2023

Social Media + Society

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In recent years, there has been a noticeable shift in the economies of search and information retrieval concerning how the products of giant platform companies consolidate and represent facts directly in their search results. Concomitantly, media, communication, and information scholars have recently refocused on how media technology companies variably create, collect, connect, and commercialize data related to facts about the world and how such processes have implications for how we know the world. Such approaches often counter popular narratives that seek to frame the problems of platforms in terms of personalization and personalized content. While research on the personalization afforded by media is widespread, platforms also engage in the centralization of facts by merging web data representing factual claims and offering answers directly in search engines and virtual assistants’ results and responses (what we refer to as “fast facts”). These processes considerably affect how knowledge is constructed and shared in a networked society. This special issue collects empirical investigative research on the platformization, exploitation, and centralization of facts while offering a variety of perspectives from which to study these developments, including semantic and infrastructural techniques. This article provides an overview of this field and contextualizes recent media studies on search and information retrieval in broader debates around facts and truth claims.

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Of “Working Ontologists” and “High-Quality Human Components”:

Cited by 29 publications

References 0 publications

Algorithmic Profiling of Job Seekers in Austria: How Austerity Politics Are Made Effective

Algorithmic Profiling of Job Seekers in Austria: How Austerity Politics Are Made Effective

Semantic Media: Political Economy Perspectives on Platformized Fact Production

Fast Facts: Platforms From Personalization to Centralization

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