"Data/Governance with Ideological Characteristics: China in (and as) the Shadow of the US"--Summary and PPT of Presentation at the Carter Center (Atlanta)--Data Governance and Its Impact on US-China Relations (26 September 2023)

 

I was delighted to have been asked to participate in the SYMPOSIUM EVENT: China’s Data Governance and its Impact on US-China Relations. I am grateful to the event sponsors: The Carter Center China Focus, Emory Unversity, Georgia State University, Spellman College, and the China Research Center. Special appreciation to our convenors-- Dr. Yawei Liu, Senior Advisor on China at The Carter Center and Dr. Keren Wang of Emory University Department of Russian and East Asian Languages and Cultures. Especially delighted to have been able to exchange views and hear the brilliant presentations of an extraordinary group of people:

Obse Ababiya, Associate Director, Office of Global Strategy and Initiatives at Emory University.

Larry Catá Backer, Professor of Law and International Affairs, Penn State Law School.

Jamie Horsley, Senior Fellow, Paul Tsai China Center at Yale Law School | John L. Thornton China Center at Brookings Institution.

Aynne Kokas, C.K. Yen Professor at the Miller Center and Associate Professor of Media Studies, University of Virginia.

Maria Repnikova, Associate Professor in Global Communication, Georgia State University.

Keren Wang, ACLS Emerging Voices Fellow, Emory University Department of Russian and East Asian Languages and Cultures.

For my presentation and remarks, I focused on the basics. Entitled "Data/Governance with Ideological Characteristics: China in (and as) the Shadow of the US" my object was to carefully unpack the key elements around which the debates about data, data governance, and the governance of data have been elaborated, and from which those debates have then been transposed onto the equally important trajectories of U.S.-China relations. My starting and end points are the same: currently, data, data machines, and the systems through which these interact and become useful (to someone) are all built along the same lines.  There is no magical liberal democratic, U.S., E.U. Marxist-Leninist, or Chinese  unique variant of any of these core components of data driven systems of human-data interaction focused on the curation of social relations and the construction of better behaved individuals and the systems in which they are placed.  Where the differences arise are in the development and operationalization of rules, principles, systems, and policing around the governance of data (and data machines). That distinction between the commonality of data and data machine structures on the one hand, and the variegation of governance of data (and data machine) systems on the other, is an essential element of analysis (and the development of sound policy) that tends to be overlooked in a sometimes mad rush to conflate systems and components of data based operations with the ideologically embedded systems of rules and instrumentalization which then animate, weaponize, direct, and activate in specific ways these ecologies of data and data-machine systems. That is not to suggest that ideology ought to be purged from the systematization and pragmatic structuring of these data and data-machine systems.  Quite the reverse. What it does suggest is that one ought to keep the two quite separate in order not to sacrifice the utility of data and data-machine systems on the altar of geo-political contests over ideological dominance.  On the process one is able to de-mystify data and data-machine systems while appropriately focusing on the ideological principles and objectives that then bend these machines to specific political, political, social, cultural, or economic ends dictated by the presumptions and world views that drive the ideological basis of seeing the world backed by public  and cultural power.

To those ends one starts with data. Data is the basic building block of systems and their governance. Anything that can be observed, experienced, created, or undertaken AND recorded can be data. Data has no essence other than “being” data. Everything else is strategic and instrumental: Identifying/choosing data; Choosing the subjective center; Organizing/activating data (platforms); Connecting platforms (neural networks and circulatory systems. Of course, there are governance issues around each of these, and governance issues around their interconnection, and the relationship of producer/consumers of data. But at its source, data must be identified (given significance as data), and then organized (e.g., data lifeworlds (Lebenswelt) - imaginaries; its inter-subjectivity; and activated (through the introduction of data analytics; ratings, modelling, compliance, social/economic discipline). 

One then distinguishes between data governance (the data-machine system) and the governance of data (the political-cultural project). One starts with a fundamental distinction between (1) data (input): Objects and their identification; investing objects with value; (2) data machines/platforms (rationalization, analytics, connections) (the process of aggravating (in semiotics from significance to signification)—the conscious tool; (3) neural pathways/circulatory systems among data machines (connecting data machines; building more complex structures of interactive signification and collective meaning making, that move from descriptive to predictive machines; self-referencing iterative sentience; autonomy; to the (4) governance of data (imposed imaginaries /worldviews) coercively shaping the structure and operation of data machines. 

The governance of data and data-machine systems produces it own complex problem fields.  The first and most critical is what I call the inside/outside problem of governance:  that is of placing governance INSIDE or OUTSIDE the DATA and DATA-MACHINE SYSTEMS that are its object. Outside references traditional law, norms, rules, exogenous structures of meaning making, control and discipline. Inside focuses on insertions of governance (like a virus) into the guts of the machine: coding; analytic parameters; the “inorganic” structuring of iterative non-carbon-based sentience: endogenous. Governance of data has significant effects which tend to be the focus of much current debate.  These revolve around what is aptly termed the ideological mirror and governance instrumentalization of data machines. Prominent among these are bias; and bias privileging (“Social justice” versus “development-stability-prosperity” models); rationalizing modalities of management: markets, regulatory property; governance and quality control issues (integrity issues); and data/systemic integrity; the question of narrative.

Once this superstructure is exposed, it is an easy matter to quite clearly distill the "issues" around "data governance" and U.S.-China relations.  That distilling brings us away from the machine to the ideology that weaponizes the machine toward (no doubt worthy) political-cultural-economic-cultural ends. 
These ends are then the subject of the last part of the presentation.  Along with the consequences of this ideological variegation of machine function and data identification: (1) warring narratives (signification of good and evil systems premises; fairness and trustworthiness); (2) power through projection of “effects” of system operation (e.g., global CSR compliance regimes versus state secrets rule ; domestication of data and analytics; analytic black boxes);  (3) the impenetrability of language and control (coders versus lawyers versus public officials; accountability across languages); and (4) human versus Non-Carbon intelligence (cente3red on backdoors; sabotage; bias drift; autonomy producing a system in which machines drive human culture and politics irrespective of ideological starting point).

The PPT follow below.  ACCESS PPT HERE: Backer-Demystifying-China-Data-Gov (very large file); PDF VERSION OF PPT HERE: Backer-Demystifying-China-Data-Gov.