This is the intervention of DataEthics.eu’s Research Director and principal investigator of the Data Pollution & Power initiative Gry Hasselbalch at the first meeting of the DPP Group of the Data Pollution & Power Initiative.
The DPP Group is set up to examine the data of AI as a human and natural resource in a sensitive “eco-system” – and ”data pollution” as the interrelated (big) data of AI adverse effects on the UN sustainable development goals. It is part of the Sustainable AI Lab’s[1] Data Pollution & Power initiative[2]. The DPP Group will in 2021-2022 explore the ”data pollution” of AI as the interrelated adverse effects of the data of AI and the power dynamics that shape the field. The group’s meetings are not accessible to the public. Mini reports from the meetings are made accessible on the DPP website: www.datapollution.eu
The objective of the first DPP group meeting was to make an initial dive into the different expertises and interests in the data pollution of AI represented in the group, map out themes and power dynamics that are particular to the respective fields of expertise and research focus. Ultimately the aim was to understand how and where the various perspectives on and analysis of the data pollution of AI intersect and scoping out the power dynamics of a potential common research field.
Gry Hasselbalch:
Data pollution is to the big data age what smog was to the Industrial age and it requires a similar societal response. Only a few decades ago a nice car was a big car, and its toxic exhaust was something far away in the horizon. Today, a car’s environmental friendliness is a legal requirement and a market demand. We have environmental laws, standards for sustainable business conduct as well as citizen environmental awareness of air, water and land pollution that has been steadily increasing alongside scientific studies of the adverse impacts of harmful pollutants on our natural environments. It is time now for a movement to tackle the data pollution of the age of big data.
Data pollution calls for a proactive approach to the technologies we build, govern and embed in the sociotechnical infrastructures of the big data society. ‘Environmental awareness’ in society and among the companies and institutions responsible for the ‘data pollution’ of this age is lagging greatly behind other forms of environmental concerns. We therefore urgently need to develop our awareness of ‘data pollution’. But to do this, we firstly have to develop a conceptual framework to address the power dynamics that shape one of the big pollution problems of our age. We must recognise that in a big data economy data is the main “currency” and “resource” and therefore also the locus of different societal interests and power dynamics that not always put the human, social or environmental interest first.
Data pollution does not only concern the carbon footprint of AI’s energy consumption, nor is it only a privacy issue or a discriminatory effect of “bad data”. If we look at the entire field, we will see that data pollution is a structural issue integrated in general societal power dynamics; that power actors, hierarchies and asymmetries are interrelated with data pollution.
The core value of the DPP group is the diversity of perspectives and we will in the coming year not necessarily produce traditional “results” or “conclusions” but rather seek to create a common ground for the debates on data pollution with a focus on interrelated structures of power.
We rely on the 17 UN Sustainable development goals in our analysis and combine two traditional usages of the term “data pollution”:
1. As the adverse impacts on our personal and social environments, e.g. on individual rights, such as data protection and privacy, and democratic institutions and balances of power.
2. As the material adverse effects on our natural environment, e.g. the carbon foot print of big data handling.
We also need to consider looking at the power dynamics of data pollution at different scales of time and levels of analysis:
On the micro level, powers and interests in data can be identified in the very design of an AI system. Here, we want to understand data pollution of the very data design of AI. Where is the data pollution in the data eco system of an AI technology? Which interests are embedded in the data design process? How is a data design choice made? Is there an alternative more sustainable data design?
What are the barriers and enablers on a micro design level for tackling data pollution and achieving sustainable data of AI?
On the meso level, institutions, companies, governments and intergovernmental organisations will be negotiating the interests, values and cultural frameworks for their practices in contexts of standards and laws. How are laws and standards implemented within an organisation? Which interests are emphasized in the implementation of an institutional and standardized framework?
What are the barriers and enablers on an institutional, organizational and governmental meso level for tackling data pollution and achieving sustainable data of AI?
We are participating in a social movement on a global scale. Sociotechnical change will also happen on a macro level. In this perspective, we can look at critical moments of social negotiation that constitute “the technological momentum” that a larger socio-technical system needs to evolve (See Thomas P. Hughes). Our increasing awareness of the data pollution of AI is integral to a moment as such. It is a critical ethical reflection and value negotiation that emerge in between a moment of crisis and a when the technological systems are consolidated in society. We will also see on a macro scale conflicts between different systems. This is also when critical problems are exposed, different interests are negotiated, and they are finally gathered around solutions to direct the evolution of a technological development. Here, we want to understand the power dynamics of the geo-political battle between different approaches to data and AI. How do the political and social discourses, legal twists and cultural tensions shape how we tackle data pollution of AI on a macro scale? What are the barriers and enablers on a historical and geo-political level for tackling data pollution and achieving sustainable data of AI?
[1] The Bonn University’s Institute of Science and Ethics Sustainable AI Lab is a new section established in 2021 by Aimee van Wynsberghe https://www.sustainable-ai.eu/
[2] The Data Pollution & Power initiative is set up at the Sustainable AI Labby the independent senior researcher Gry Hasselbalch to explore the power dynamics that shape the data pollution of AI across the UN Sustainable Development Goals. The project examines how power dynamics and interests in the data of AI determine how data resourcesare handled and distributed in our data eco system and considers actions and governance approaches that are intrinsically interrelated in systems of power and interests. In addition to the establishment of the DPP group, a DPP white paper will be published in 2022.
