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The purpose of this study is to propose a bearer service, which generates and maintains a “digital doppelgänger” for every financial contract in the form of a dynamic transaction document that is a standardised “data facility” automatically making important contract data from the transaction counterparties available to relevant authorities mandated by law to request and process such data. This would be achieved by sharing certain elements of the dynamic transaction document on a bearer service, based on a federation of distribution ledgers; such a quasi-simultaneous sharing of risk data becomes possible because the dynamic transaction document maintain a record of state in semi-real time, and this state can be verified by anybody with access to the distribution ledgers, also in semi-real time.

In this paper, the authors propose a novel, regular technology (RegTech) cum automated legal text approach for financial transaction as well as financial risk reporting that is based on cutting-edge distributed computing and decentralised data management technologies such as distributed ledger (Swanson, 2015), distributed storage (Arner et al., 2016; Chandra et al., 2013; Caron et al., 2014), algorithmic financial contract standards (Brammertz and Mendelowitz, 2014; Breymann and Mendelowitz, 2015; Braswell, 2016), automated legal text (Hazard and Haapio, 2017) and document engineering methods and techniques (Glushko and McGrath, 2005). This approach is equally inspired by the concept of the “bearer service” and its capacity to span over existing and future technological systems and substrates (Kavassalis et al., 2000; Clark, 1988).

The result is a transformation of supervisors’ capacity to monitor risk in the financial system based on data which preserve informational content of financial instruments at the most granular level, in combination with a mathematically robust time stamping approach using blockchain technology.

The RegTech approach has the potential to contain operational risk linked to inadequate handling of risk data and to rein in compliance cost of supervisory reporting.

The present RegTech approach to financial risk monitoring and supervisory reporting is the first integration of algorithmic financial data standards with blockchain functionality.

The purpose is to present proposals to foster what we call a socially meaningful transparency practice that aims to enhance public understanding of data-based systems through the production of accounts that are relevant and useful to diverse publics, and society more broadly.

The authors’ proposals emerge from reflections on challenges they experienced producing written and visual accounts of specific public sector data-based systems for research purposes. Following Ananny and Crawford's call to see limits to transparency practice as “openings”, the authors put their experience into dialogue with the literature to think about how we might chart a way through the challenges. Based on these reflections, the authors outline seven proposals for fostering socially meaningful transparency.

The authors identify three transparency challenges from their practice: information asymmetry, uncertainty and resourcing. The authors also present seven proposals related to reduction of information asymmetries between organisations and non-commercial external actors, enhanced legal rights to access information, shared decision making about what gets made transparent, making visible social impacts and uncertainties of data-systems, clear and accessible communication, timing of transparency practices and adequate resourcing.

Socially meaningful transparency aims to enhance public understanding of data-based systems. It is therefore a necessary condition not only for informed use of data-based products, but crucially for democratic engagement in the development of datafied societies.

The paper contributes to existing debates on meaningful transparency by arguing for a more social, rather than individual, approach to imagining how to make transparency practice more meaningful. The authors do this through their empirical reflection on our experience of doing transparency, conceptually through our notion of socially meaningful transparency, and practically through our seven proposals.

Algorithmic monitoring has been widely applied to the practice of platform economy as a management means. Despite its benefits, negative effects of algorithmic monitoring are gradually emerging.

Based on moral disengagement theory, this research aims to investigate how algorithmic monitoring might affect gig workers’ attitudes and behaviors. Specifically, we explored the effect of algorithmic monitoring on gig workers’ unethical behavior. A three-wave survey was conducted online, and the sample consisted of 318 responses from Chinese gig workers.

The results revealed that algorithmic monitoring positively affected unethical behavior through displacement of responsibility, and the individualistic orientation of gig workers moderated this relationship. However, the relationship between moral justification and algorithmic monitoring was not significant.

This research contributes to the algorithmic monitoring literature and examines its impact on gig workers’ unethical behavior. By revealing the underlying mechanism and boundary conditions, this research furthers our understanding of the negative influences of algorithmic monitoring and provides practical implications.

Research shows that postsecondary students are largely unaware of the impact of algorithms on their everyday lives. Also, most noncomputer science students are not being taught about algorithms as part of the regular curriculum. This exploratory, qualitative study aims to explore subject-matter experts’ insights and perceptions of the knowledge components, coping behaviors and pedagogical considerations to aid faculty in teaching algorithmic literacy to postsecondary students.

Eleven semistructured interviews and one focus group were conducted with scholars and teachers of critical algorithm studies and related fields. A content analysis was manually performed on the transcripts using a mixture of deductive and inductive coding. Data analysis was aided by the coding software program Dedoose (2021) to determine frequency totals for occurrences of a code across all participants along with how many times specific participants mentioned a code. Then, findings were organized around the three themes of knowledge components, coping behaviors and pedagogy.

The findings suggested a set of 10 knowledge components that would contribute to students’ algorithmic literacy along with seven behaviors that students could use to help them better cope with algorithmic systems. A set of five teaching strategies also surfaced to help improve students’ algorithmic literacy.

This study contributes to improved pedagogy surrounding algorithmic literacy and validates existing multi-faceted conceptualizations and measurements of algorithmic literacy.

This paper aims to demonstrate the importance of a cash flow generating standard for individual financial contract level data and the ability to create such a standard.

The authors analyze the importance for such a standard of software that turns natural language contracts into cash flow generating algorithms; a data dictionary that standardizes contract terms; and access to variables that represent the state of the world (e.g. market risk, counterparty risk, etc.) that affect contractual obligations.

The ability to realize benefits from the use of such a contract level algorithmic standard depends on the following: making the standard’s software open source; fully testing the software to have complete confidence in its accuracy; and enabling the software to use of a wide range of models of various sources of risk (market, credit and behavior risk) to support forward-looking analysis. Such a standard would solve the disconnect that exists in financial firms between the representation of financial contracts for transaction processing and analysis. The ACTUS Financial Research Foundation is building, testing and making available such a standard that represents almost all financial contracts extant in markets.

The adoption of such a standard would reduce the costs of operations of financial firms, provide the computational infrastructure for more effective regulatory oversight, reduce regulatory reporting costs and improve financial market transparency. It would also enable the assessment of systemic risk by directly quantifying the interconnectedness of firms.

This is a new approach to financial analytics that clearly separates the deterministic components of finance, which can be standardized from the stochastic elements that cannot be standardized.

The defective coin problem involves the identification of a defective coin, if any, and ascertaining the nature of the defect (heavier/lighter) from a set of coins containing at the most one defective coin, using an equal‐arm‐pan‐balance. An algorithmic analysis of the problem is considered. The solution strategy to minimise the number of weighings required to detect the defective coin is based on a problem reduction approach involving successive decomposition of the problem into subproblems until it is trivially solved. The algorithm is capable of generating all possible optimal solutions.

The chapter explores the developments in work on the history of quantification and sport, explaining how quantification in sport is generally understood, and then establishing what a sociological approach offers to scholars interested in exploring new expressions of these developments in biometrics and Big Data. It then outlines some potential directions scholars might pursue to further develop knowledge of these developments in the context of sport.

The chapter synthesizes existing literature from the sociology of quantification, sport sociology and quantification, and Big Data to provide historical, contemporary, and future oriented assessments of sport and datafication.

By situating a discussion of Big Data and biometrics in the context of sport, this chapter argues for the value of a sociological approach to these areas. The chapter engages prior work as a way to move scholars to challenge the assumed epistemological and political power of numbers for the way we engage sport.

The chapter argues for a number of future areas of study that may push the boundaries of existing research in the area.

The chapter provides a survey of the literature on sport, analytics, and Big Data as an impetus for future research into the importance of a sociological approach to these areas in the context of sport.

This essay demonstrates how information systems — collections of documents, data, or other information-bearing objects — function internally as sites for creative manipulation of genre resources. In the information systems context, these textual activities are not clearly traced to the purposeful actions of specific writers.

Genre development for information systems can result from actions that may appear individually to be rote, repetitive, passive, and uninteresting. But as these actions are aggregated at increasing scales, genre components interact and shift, even if change is limited to one element of the larger assemblage. Although these changes may not be initiated by writers in accordance with targeted work activities and associated rhetorical goals, the composite texts thus produced are nonetheless powerful documents that come to partially constitute the broader activities they appear to merely support.

In demonstrating “writerless” phenomena of genre change in distributed, regulated systems, this essay complements and extends the strong body of existing work in genre studies that emphasizes the writer’s perspective and agency in its accounts of genre development. By showing how continually evolving compound documents such as digital libraries constitute such sites of unacknowledged genre change, this essay demonstrates how the social actions that these composite documents facilitate for their users also change.

The purpose of this paper is to study the optimal sequential information acquisition process of a rational decision maker (DM) when allowed to acquire n pieces of information from a set of bi-dimensional products whose characteristics vary in a continuum set.

The authors incorporate a heuristic mechanism that makes the n-observation scenario faced by a DM tractable. This heuristic allows the DM to assimilate substantial amounts of information and define an acquisition strategy within a coherent analytical framework. Numerical simulations are introduced to illustrate the main results obtained.

The information acquisition behavior modeled in this paper corresponds to that of a perfectly rational DM, i.e. endowed with complete and transitive preferences, whose objective is to choose optimally among the products available subject to a heuristic assimilation constraint. The current paper opens the way for additional research on heuristic information acquisition and choice processes when considered from a satisficing perspective that accounts for cognitive limits in the information processing capacities of DMs.

The proposed information acquisition algorithm does not allow for the use of standard dynamic programming techniques. That is, after each observation is gathered, a rational DM must modify his information acquisition strategy and recalculate his or her expected payoffs in terms of the observations already acquired and the information still to be gathered.