Matthew McQuilla and I am delighted to announce the publication of "The algorithmic law of business and human rights: constructing private transnational law of ratings, social credit and accountability measures" International Journal of Law in Context as FirstView (2022).
Abstract: This paper examines the rise of algorithmic systems – that is, systems of data-driven governance (and social-credit-type) systems – in the form of ratings systems of business respecting human rights responsibilities. The specific context is rating or algorithmic systems emerging around national efforts to combat human trafficking through so-called Modern Slavery and Supply Chain Due Diligence legal. Section 2 provides a brief contextualisation of the problems and challenges of managing compliance with emerging law and norms against forced labour and, in its most extreme forms, modern slavery. Section 3 examines the landscape of such algorithmic private legal systems as it has developed to date in the context of forced labour ratings systems. There is a focus on the connection between the power to impose the normative basis of data analytics and the increasingly tightly woven-in connection between principal actors in this endeavour.
The article has published as Open Access (OA), you can access it on this link.
What started out as an exploration of the mechanics and narratives of ratings systems as a sort of informal regulatory space with potential bite evolved, over the course of the study, into a more focused consideration of what appears to be a species of outsourced Leninism at the core of the process of liberal democracy. What the movement toward non-state based soft regulation appears to begin to tease us with is the structures, and differences, between vanguardism in liberal democracy and that of Marxist Leninist political systems. In the later, vanguardism is aligned with political and state power directly, and centralized. It is administrative and bureaucratic, driven by the discourse of administrative discretion. In liberal democratic sphere vanguardism is aligned wit markets and social power networks, It is private or privatized and indirectly aligned with state power, or in which state power is an instrument of private power organized and led through vanguards. It is driven by the discourse of accountability and assessment and of conformity to social-market narratives curated for the purposes of vanguard objectives as they morph from time to time. Leninist vanguards are centralized. Liberal democratic vanguards are not; they are the stuff of networked governance one has been taught is a good thing (eg here and here) by an academic discourse that itself swerves as an instrument of vanguard authority. Technology merely makes this more efficient and changes its manifestations. Global liberal democracy's New World Order may, like Marxist Leninism, merely reflect another aspect of Enlightenment managerialism to suit the times. And the times are quantitative, accountability based, and modeled; important but revealing. We are just at the start of this project.
The Introduction follows.
The algorithmic law of business and human rights: constructing private transnational law of ratings, social credit and accountability measures
Published online by Cambridge University Press: 16 November 2022
Abstract
This paper examines the rise of algorithmic systems – that is, systems of data-driven governance (and social-credit-type) systems – in the form of ratings systems of business respecting human rights responsibilities. The specific context is rating or algorithmic systems emerging around national efforts to combat human trafficking through so-called Modern Slavery and Supply Chain Due Diligence legal. Section 2 provides a brief contextualisation of the problems and challenges of managing compliance with emerging law and norms against forced labour and, in its most extreme forms, modern slavery. Section 3 examines the landscape of such algorithmic private legal systems as it has developed to date in the context of forced labour ratings systems. There is a focus on the connection between the power to impose the normative basis of data analytics and the increasingly tightly woven-in connection between principal actors in this endeavour.
- Type
- Special Issue Article
- Information
- International Journal of Law in Context , First View , pp. 1 - 19
- Creative Commons
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.- Copyright
- Copyright © The Author(s), 2022. Published by Cambridge University Press
1 Introduction
Human systems, qualitative or quantitative, require two principal elements to make them viable as collective regulatory measures: they require a normative super structure and they require an institutional structure for the interruption and application of this normative superstructure. While this is commonly understood within traditional systems of regulatory governance (Aguirre, Reference Aguirre2011), its application to quantitative methods of ordering collective behaviour is less well explored (Backer, Reference Backer2022a). This paper considers the use of algorithmic systems – that is, systems of data-driven governance (and social-credit-type) systems – in the context of the regulation of the human rights effects of economic activity. It then considers the way that algorithmic systems, like ratings systems, may be impacted by the interlinking networks of human and in institutional ratings systems builders.
The object is to advance the discourse of algorithmic law between two distinct lines of scholarship that have only recently emerged. The first is a line of scholarship that focuses its inquiry of algorithmic law and data-driven governance – by positioning the conversation around pragmatic issues, including the potential social harms and/or gains that could be had from algorithmic law and more generally data-driven governance (Campbell-Verduyn et al., Reference Campbell-Verduyn, Goguen and Porter2017; Smith, Reference Smith2020; Katzenbach and Ulbricht, Reference Katzenbach and Ulbricht2019; Robinson, Reference Robinson2017; Brown, Reference Brown2020). In that vein, some important scholarship has focused on issues of definition (algorithmic law) and, quite influentially, others developed proposals on how to tame, contain and regulate their manifestation (Pasquale, Reference Pasquale2017; Alang, Reference Alang2019). Another line of scholarly development considered the ramifications of the rise of platforms used to support the structures and operations of algorithmic law (Barns, Reference Barns2018; Martin, Reference Martin2019). Of particular utility has been scholarship of domestic governance policies supported using algorithmic law and especially of the use of predictive analytics within multiple fields including governance, health care, economics, etc. (Van Calster et al., Reference Van Calster2019; Lena and Delen, Reference Lena and Delen2020; Curran and Smart, Reference Curran and Smart2021). This discussion foregrounds ethics and the threat of algorithmic governance to established political values (e.g. privacy, autonomy, equal treatment; Pasquale, Reference Pasquale2015; Sandvig et al., Reference Sandvig2014).
Ratings-based regulatory structures serve as a gateway for developing predictive analytics that has regulatory potential in ways that may serve liberal democratic values to the same extent as it appears now to serve Marxist Leninist values (Curran and Smart, Reference Curran and Smart2021). In this sense, these mechanisms advance a discussion on algorithmic law's role in international human rights law (McGregor et al., Reference McGregor, Murray and Ng2019).
Ratings systems serve as a useful entry point for the examination of emerging structures of algorithmic law and governance (Backer, Reference Backer2018). Ratings systems advance the best intentions and objectives of a human rights and sustainability-based governance order. It merges the power of markets, with the accountability measures of analytics, and it transforms the normative principles of business, human rights and accountability to easy-to-understand-and-apply ratings’ (ibid).
All that is required is a set of normative ideals that can be reduced to a set of measurable inputs. Relevant data are then identified and harvested. These are then consumed in a system of analytics from which an entity's performance can be measured against the ideal – and against the performance of other entities. On that basis, normatively infused judgments can be attached to the measures (e.g. ‘excellent’, ‘good’, ‘fair’, ‘unacceptable’ performance). The intersection of traditional law and algorithmic analytics occurs in the context of norm identification, data accessibility and integrity, and the consequences (in law and in markets) of the ratings and of the judgments derived therefrom. While traditional law serves as a constituting and quality-control superstructure, the regulatory-administrative operation is situated within the ratings systems, in conformity with the measures of which an entity seeking a higher rating will have to conform its behaviour. Beyond that, markets for ratings systems may also drive ratings structural and operational integrity (Nguyen and Altan, Reference Nguyen and Altan2011).
The core focus of this contribution is on the ratings-based regulation of human trafficking. The reason is simple. This is one of the areas in which states and public international bodies have already sought to legislate and around which there is consensus on core normative principles. Over the last decade, states have started mandating disclosure regimes intended to change behaviour (UK Modern Slavery Act 2015; Backer, Reference Backer2008a; Modern Slavery Act 2018 (Australia)). Others have sought through disclosure and response regimes to mandate the construction of more comprehensive administrative regulatory regimes by entities now responsible for conduct across their production chains (e.g. Rünz and Herrmann, Reference Rünz and Herrmann2021; Lavite, Reference Lavite2020). In the process, what some have identified as ‘hard soft law’ (Linsay et al., Reference Linsay, Kirkpatrick and Low2017) has been created, in the sense that soft law regimes developed at the international level (Landman and Silverman, Reference Landman and Silverman2019; Mende, Reference Mende2019) are then hardened in the private rule systems of the international regulation of enterprises when commanded by provisions of national legislation. The issue, then, has become centred on the transposition of international norms and principles into the regulatory systems of enterprise internal governance by operation of domestic legislation, rather than into a domestic legal order (the customary mode of domestication of international public norms) (Van Schaack, Reference Van Schaack2014).
Sections 2 and 3 examine the landscape of such algorithmic private legal systems as it has developed to date in the context of forced labour ratings systems. The ratings systems provide insight into the way that division of labour, property regimes and the principles of markets play a role in the translation of theoretical structures of algorithmic governance to concrete measures. In the process, it also points to the very human and institutional issues of the structures of power within which these ratings systems are now driven and controlled. The goal is to dissect these rating systems and their methodologies in a fashion that makes their make-up understandable even to those who have no prior knowledge of this style of rating. This section systematically discusses three separate rating systems (Financial Times Stock Exchange (FTSE) 100, KnowTheChain (KTC) and Green America Chocolate scorecards). The similarities and differences between the three should be made apparent during the exhaustive dialogue that is used to remove any veil acting as a barrier to understanding these systems. This section's importance is based on its ability to expose the mechanisms involved in the construction of a prototypical rating system, as well as their effectiveness. What at first blush may appear to be big data and ratings-based ‘hypernudges’ (Thaler and Sunstein, Reference Thaler and Sunstein2008), as data-based mechanics for guiding decisions, can become substantially less of a nudge (Bovens, Reference Bovens, Grune-Yanoff and Hansson S2008) and more of a complex interlinking of stakeholders producing a regulatory framework from out of interrelated ratings focused (as we will see in this section) on a specific objective (Yeung, Reference Yeung and Beer2018). In understanding its construction and effectiveness, the conceptual make-up of other data-driven governance systems should be more digestible.
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