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Data Pollution & Power - White Paper for a Global Sustainable Development Agenda on AI by Gry Hasselbalch with contributions from the Data Pollution & Power (DPP) Group at Bonn Sustainable AI Lab
Abstract
Data Pollution is to the big data age what smog was to the industrial age. Our response to data pollution will develop much like our reaction to traditional forms of pollution—just much faster and hopefully with dedication and great force. This white paper describes a nascent environmental data pollution movement. It frames data pollution in the context of powers and interests exploring eight domains in which data pollution has the greatest impact: Nature, Science & Innovation, Democracy, Human Rights, Infrastructure, Decision-Making, Global Opportunities, and Time. The main objective is to ensure that data pollution of AI in particular is included in the global sustainable development agenda.
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Artificial intelligence (AI) is becoming increasingly important for the infrastructures that support many of society’s functions. Transportation, security, energy, education, the workplace, the government have all incorporated AI into their infrastructures for enhancement and/or protection. In this paper, we argue that not only is AI seen as a tool for augmenting existing infrastructures, but AI itself is becoming an infrastructure that many services of today and tomorrow will depend upon. Considering the vast environmental consequences associated with the development and use of AI, of which the world is only starting to learn, the necessity of addressing AI alongside the concept of infrastructure points toward the phenomenon of carbon lock-in. Carbon lock-in refers to society’s constrained ability to reduce carbon emissions technologically, economically, politically, and socially. These constraints are due to the inherent inertia created by entrenched technological, institutional, and behavioral norms. That is, the drive for AI adoption in virtually every sector of society will create dependencies and interdependencies from which it will be hard to escape. The crux of this paper boils down to this: in conceptualizing AI as infrastructure we can recognize the risk of lock-in, not just carbon lock-in but lock-in as it relates to all the physical needs to achieve the infrastructure of AI. This does not exclude the possibility of solutions arising with the rise of these technologies; however, given these points, it is of the utmost importance that we ask inconvenient questions regarding these environmental costs before becoming locked into this new AI infrastructure.
Climate change is a global priority. In 2015, the United Nations (UN) outlined its Sustainable
Development Goals (SDGs), which stated that taking urgent action to tackle climate change and its
impacts was a key priority. The 2021 World Climate Summit finished with calls for governments
to take tougher measures towards reducing their carbon footprints. However, it is not obvious
how governments can make practical implementations to achieve this goal. One challenge towards
achieving a reduced carbon footprint is gaining awareness of how energy exhaustive a system or
mechanism is. Artificial Intelligence (AI) is increasingly being used to solve global problems, and
its use could potentially solve challenges relating to climate change, but the creation of AI systems
often requires vast amounts of, up front, computing power, and, thereby, it can be a significant
contributor to greenhouse gas emissions. If governments are to take the SDGs and calls to reduce
carbon footprints seriously, they need to find a management and governance mechanism to (i) audit
how much their AI system ‘costs’ in terms of energy consumption and (ii) incentivise individuals to
act based upon the auditing outcomes, in order to avoid or justify politically controversial restrictions
that may be seen as bypassing the creativity of developers. The idea is thus to find a practical solution
that can be implemented in software design that incentivises and rewards and that respects the
autonomy of developers and designers to come up with smart solutions. This paper proposes such a
sustainability management mechanism by introducing the notion of ‘Sustainability Budgets’—akin
to Privacy Budgets used in Differential Privacy—and by using these to introduce a ‘Game’ where
participants are rewarded for designing systems that are ‘energy efficient’. Participants in this game
are, among others, the Machine Learning developers themselves, which is a new focus for this
problem that this text introduces. The paper later expands this notion to sustainability management
in general and outlines how it might fit into a wider governance framework.
The collection, processing, storage and circulation of data are fundamental element of contemporary societies. While the positivistic literature on ‘data revolution’ finds it essential for improving development delivery, critical data studies stress the threats of datafication. In this article, we demonstrate that datafication has been happening continuously through history, driven by political and economic pressures. We use historical examples to show how resource and personal data were extracted, accumulated and commodified by colonial empires, national governments and trade organizations, and argue that similar extractive processes are a present-day threat in the Global South. We argue that the decoupling of earlier and current datafication processes obscures the underlying, complex power dynamics of datafication. Our historical perspective shows how, once aggregated, data may become imperishable and can be appropriated for problematic purposes in the long run by both public and private entities. Using historical case studies, we challenge the current regulatory approaches that view data as a commodity and frame it instead as a mobile, non-perishable, yet ideally inalienable right of people.
This work examines the evolution of Latin American Civil Society Organizations’ (CSOs) resistance practices in the context of datafication and how these relate with the overall notions of symbolic domination denounced by the Latin American School of Communication. Although CSOs in Latin America are still exploring the problems surrounding datafication, signs of vitality are already showing in broader debates around human rights, community development, and media policies. The study identifies the main themes underlying datafication work by Latin American CSOs and assesses how they shape resistance practices and CSOs’ perceptions of asymmetrical power relations. While some patterns can fall into existing conceptualisations surrounding resistance practices and data activism, this paper identifies new conceptual and empirical approaches to face the challenges posed by a datafied society.
While there is a growing effort towards AI for Sustainability (e.g. towards the sustainable development goals) it is time to move beyond that and to address the sustainability of developing and using AI systems. In this paper I propose a definition of Sustainable AI; Sustainable AI is a movement to foster change in the entire lifecycle of AI products (i.e. idea generation, training, re-tuning, implementation, governance) towards greater ecological integrity and social justice. As such, Sustainable AI is focused on more than AI applications; rather, it addresses the whole sociotechnical system of AI. I have suggested here that Sustainable AI is not about how to sustain the development of AI per say but it is about how to develop AI that is compatible with sustaining environmental resources for current and future generations; economic models for societies; and societal values that are fundamental to a given society. I have articulated that the phrase Sustainable AI be understood as having two branches; AI for sustainability and sustainability of AI (e.g. reduction of carbon emissions and computing power). I propose that Sustainable AI take sustainable development at the core of its definition with three accompanying tensions between AI innovation and equitable resource distribution; inter and intra-generational justice; and, between environment, society, and economy. This paper is not meant to engage with each of the three pillars of sustainability (i.e. social, economic, environment), and as such the pillars of sustainable AI. Rather, this paper is meant to inspire the reader, the policy maker, the AI ethicist, the AI developer to connect with the environment—to remember that there are environmental costs to AI. Further, to direct funding towards sustainable methods of AI.
This article investigates a moment of the big data age in which artificial intelligence became a fixed point of global negotiations between different interests in data. In particular, it traces and explicates cultural positioning as an interest in the artificial intelligence momentum with an investigation of the unfolding of a European AI policy agenda on trustworthy AI in the period 2018–2019.
There is great interest in how the growth of artificial intelligence and machine learning may affect global GHG emissions. However, such emissions impacts remain uncertain, owing in part to the diverse mechanisms through which they occur, posing difficulties for measurement and forecasting. Here we introduce a systematic framework for describing the effects of machine learning (ML) on GHG emissions, encompassing three categories: computing-related impacts, immediate impacts of applying ML and system-level impacts. Using this framework, we identify priorities for impact assessment and scenario analysis, and suggest policy levers for better understanding and shaping the effects of ML on climate change mitigation. The rapid growth of artificial intelligence (AI) is reshaping our society in many ways, and climate change is no exception. This Perspective presents a framework to assess how AI affects GHG emissions and proposes approaches to align the technology with climate change mitigation.
This essay responds to the prevailing political environment of estrangement that can be seen in the growing distrust of public institutions, intensifying levels of political polarization, and rising support for populism, particularly in the United States. These trends have contributed to a diminished sense of publicness in public administration, including an erosion of public values and political legitimacy, and an increasingly cynical view of the value, role, and purpose of public service in the modern polity. We argue that public administration must respond actively to this estrangement and seek to repair and strengthen the links between democracy, public administration, and public values through scholarship, connections to practice and the public, and education.