14 1541-1672/11/$26.00 © 2011 IEEE Ieee INTeLLIGeNT SYSTemSPublished by the IEEE Computer Society

A I A N D S U S T A I N A B I L I T YEditor: Doug Fisher, Vanderbilt University, douglas.h.fi sher@vanderbilt.edu

Computing and AI for a Sustainable Future

Douglas H. Fisher, Vanderbilt University

and sustainability. My search was not exhaustive, largely based on keywords, but it wasn’t trivial ei-ther. Still, little turned up in the intersection of AI and sustainability in early 2007, and most of what did, as I recall, was in environmental science pub-lications and appeared to be dominated by Euro-pean researchers using evolutionary computation for the purposes of optimization.1

AI and sustainability has grown substantially in the last few years. To some extent, this tracks with increasing interest in sustainability and comput-ing more generally. However, AI is helping to drive this larger movement, rather than simply riding along. Indeed, it’s hard to imagine that AI would not be central to understanding and managing the great complexity of maintaining a healthy planet in the face of pervasive and transformative human activity.

A visible and scientifi cally signifi cant landmark in this growth of AI and sustainability is the es-tablishment of the Computational Sustainability Institute,2 with its focus on AI and many sustain-ability areas, such as biodiversity and alternative energy. The institute grew from a 2008 Expedi-tion in Computing Award from the NSF to Cor-nell University, Oregon State University, Bowdoin College, Howard University, and other partners, quickly attracting other researchers, educators, government, and industry. The fi rst conference on computational sustainability took place in 2009, followed by a second in 2010 and leading in 2011 to a special track on Computational Sustainability at the Association for the Advancement of Artifi -cial Intelligence (AAAI) conference.

Coinciding with the institute’s founding was a groundswell of activity to include sustainability

tracks at other AI-related conferences. Machine learning and data mining have been strong among these, and in 2010, a second sustainability-focused Expedition in Computing award was given to the University of Minnesota and its partners for data-driven understanding of climate change and re-lated phenomena.

Forthcoming articles in this new IEEE Intelli-gent Systems AI and Sustainability Department will elaborate on AI’s deployment in many areas of sustainability as well as the challenges and op-portunities that sustainability issues bring to AI research, education, and practice. This opening article will touch upon the main themes at the in-tersection of AI and sustainability, but it will pri-marily concentrate on the larger contexts of sus-tainability, and on computing and sustainability, thereby setting the stage for articles to come.

SustainabilityThe United Nations’ Bruntland report contains a popular and succinct defi nition of sustainability: “Sustainable development is development that meets the needs of the present without compromis-ing the ability of future generations to meet their own needs.”3

To many, the phrase “sustainable development” is an oxymoron, but the “needs” spoken of in the Bruntland report are not about the luxuries of the materially wealthy, but rather about the survival needs of the poor and starving. As contextualized in the report, “development” is about bringing all those on the planet up to a reasonable standard of living, rather than on those who are already us-ing plenty. Indeed, there is a nascent AI for De-velopment Group4 actively exploring AI’s role in advancing social equity, together with a larger computing for development group.

More generally, the reference to “needs” begs the question as to exactly what these are, both now and in the future. Achieving and then maintaining

When preparing for a March 2007 talk

at the US National Science Foundation

(NSF), I searched the Web for scholarly work on

AI and climate change, the natural environment,

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November/December 2011 www.computer.org/intelligent 15

safe and adequate water, food, air, and other health-related criteria for all people are high-level goals. When we work backward from these ulti-mate human-centric goals, we arrive at a large number of sustainability desiderata relating to biodiversity, en-ergy, toxins, climate change, disease, community planning, agriculture, emergency response, transportation, garbage, materials, economics, pol-icy, and human behavior, among oth-ers. An expansion of what is a many-node and densely connected graph will define the purview of the IEEE Intelligent Systems’ AI and Sustain-ability area. Readers are encouraged to trace out what they imagine this graph looks like from their own per-spective and to ask students at all lev-els to do so as well. What we desire to be sustained shouldn’t be simply enu-merated for people; it should be a fo-cus of deep, ongoing conversation.

Sustainability has received mixed attention from academics, govern-ments, and industries over the past few decades. As the Brundtland re-port indicates, many have sounded the alarm for a good long time. Silent Spring,5 Rachel Carson’s book on environmental poisoning through pesticides and the like, was published in 1962. It is often credited for an en-vironmental awakening, but one that has waxed and waned over the years. The first scientific reports on rising CO2 levels and the implications for warming the planet were published by the early 1960s, reaching levels of scientific consensus by the 1980s.6

Nevertheless, a significant (but mi-nority) proportion of Americans, to take but one nation, aren’t simply skeptics, but are dismissive7 and eas-ily shifted—the materially wealthy world in general has been slow to re-act. So we have to wonder whether the new initiatives will have stay-ing power, and having staying power

through this and all subsequent gen-erations is critical, at least if we view the planet from the perspective of the human time scales of decades, centu-ries, and millennia.

In the US, a new NSF initiative—at fully 10 percent of the NSF’s pro-posed budget for the upcoming fiscal year—will support science, engineer-ing, and education for sustainability. The SEES initiative follows a host of discipline-focused programs, but now the emphasis is squarely on the need for strong interdisciplinary partner-ships leading to a science of sustain-ability.8 The Proceedings of the Na-tional Academy of Sciences launched a sustainability science section and academic departments and schools of sustainability are springing up.9

It is striking, however, that com-puting is typically not a component in these sustainability curricula, per-haps in part, because computer sci-entists themselves do not actively recognize its relevance to sustain-ability. Yet computing is pervasive and transformative, potentially af-fecting human behavior in disruptive ways, so it seems wise to consider it a core part of the emerging science of sustainability.

Computing and SustainabilitySustainability science can be reason-ably viewed as a new and vitally im-portant discipline, but sustainability concerns should not be stove-piped. If we are designing a planet that sus-tains humanity for millennia (or even centuries and decades) at anything like current levels, with wealth ac-ceptably distributed, sustainability motivated thought and action must be at the core of everything we do and must permeate the societal mi-lieu. Considering that computing is already embedded in much of society, the prescription that sustainability

should be so embedded would result in a frequent and necessary align-ment of sustainability and comput-ing. These realizations have only re-cently started to take center stage.

In May 2008, the Organization for Economic Cooperation and De-velopment (OECD) hosted the Inter-national Workshop on Information and Communications Technology (ICT) and Environmental Challenges in Copenhagen, following projec-tions on ICT’s growing environmen-tal footprint.10,11 The workshop was convened to share strategies on miti-gating these footprints, to include en-ergy, greenhouse gasses (GHG), and waste. These direct or first-order ef-fects of ICT—during the use, manu-facture, and disposal phases—are typically detrimental.

Importantly, the speakers and national delegations also discussed ICT’s higher-order effects, many of which lead to decreasing ecologi-cal footprints in other sectors such as travel and transportation. Exam-ples of computing’s second-order ef-fects include more accurate and rapid identification of species’ populations through image and audio recording and processing, static and dynamic routing of vehicles to eliminate con-gestion and the idle time associated with it, the use of video-conferencing systems instead of travel for meetings, and proposed smart grid applications such as electricity load balancing.

In turn, third-order effects of ICT alter the ways that people and other processes operate, in quality and/or quantity, and these third-order ef-fects can have profound effects, both positive and negative, on ecological footprints. For example, rebound ef-fects occur when efficiency improve-ments in the per unit costs (such as energy) of a process result in the in-creased use of that process so that the collective costs (notably energy used)

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becomes even greater than the collec-tive costs before the “improvements.” These rebound effects are but one ex-ample of unanticipated (though not necessarily unforeseeable) effects that are detrimental to the environment. Conversely, ICT is responsible for im-proved data collection and evidence-based decision making, which itself might increase these behaviors.8,12 The Oberlin dorm energy monitor-ing project, for example, used com-puting technology to visualize energy and water usage in an attempt to alter their human behavior.13

The OECD 2008 and 2009 meet-ings resulted in an important con-ceptual framework for expressing re-lationships between computing and the environment.11 Coincident with this, mathematicians14 and computer scientists were hosting workshops on sustainability themes. The NSF awarded the Computer Science and

Telecommunications Board (CSTB) of the US National Academy of Sci-ences a grant to explore, organize, and report on the opportunities for computer science research contribu-tions to sustainability. More recently, in February 2011, the Computing Community Consortium (CCC) of the Computer Research Association (CRA) convened a similarly intended meeting, issuing a report on the broad swath of challenges at the in-tersection of computing and sustain-ability, requiring truly interdisciplin-ary partnerships between computing researchers and domain scientists.15

Taken from and nicely abstracting the CCC report, Figure 1 highlights the critical role that observational data and computational models play in sustainability science. Chal-lenges in the future of modeling in-clude downscaling global mod-els, say of climate, to better inform

regional planning and policy as well as the integration of various sources of information, from social and phys-ical, to plan for the human burden on the natural environment. Some of these challenges might be met by agent-based modeling approaches,16 where “agents” correspond to small regions and/or particular information sources.

The CCC report makes recommen-dations for computing subdisciplines, a few of which we can highlight here. For example, social computing is changing the way that humans com-municate, collaborate, compete, and play.17 Yet, we have not substantially tapped into the possibilities of social computing for advancing a sustain-ability agenda. Encouraging new, sus-tainable behaviors and growing col-lective intelligence through social networking is a goal, though find-ing the incentives that will motivate people to act is a challenge. Green IT refers to mitigating the first-order en-ergy and material effects of comput-ing due to its manufacture, use, re-cycling, and disposal. Advances in energy efficiency and energy harvest-ing through GHG-neutral means are relevant. Software is also relevant in areas such as server virtualization and all forms of intelligent control.

In addition to research and prac-tice, the report also stressed the im-portance of education, in particular the infusing of computing curricula with sustainability, and inversely the infusion of computation into sustain-ability curricula. Finally, we can ex-trapolate beyond the US context in which the CCC report was prepared and emphasize that government fund-ing provides incentive for the interdis-ciplinary and international research collaborations necessary to advance sustainability desiderata. These col-laborations would not simply be be-tween environmental scientists and

Figure 1. Many subdisciplines of computing will contribute to and will be challenged by sustainability objectives. Achieving sustainability goals will require that computer scientists enter into interdisciplinary collaborations with other scientists, and vice versa, and that researchers across fields integrate their efforts with education, development, and practice. (Based on a figure in From Science, Engineering, and Education of Sustainability: The Role of Information Sciences and Engineering.15 Used with permission.)

“Big data”

Area

s of

dis

cove

ry a

nd in

nova

tion

Modeling andsimulation

Human-centered andsocial computing

Cyberphysical systems

OptimizationIntelligent systems

Privacy and security

Systems engineering

Collaborative, use-inspiredfundamental research

Energy Transportation Environment and climate

fundamental researchCore

“Green IT”• Cradle-to-cradle design• “Power-aware computing”• Energy complexity analysis of

algorithms• Energy harvesting

Sustainability: meeting the needs of present and future generations

Stakeholders

Coordinated federal investment

ResearchersComputer scientists,systems engineers,

social scientists

ResearchersComputer scientists

Educators

Domain expertsIT manufacturers

IT operators

Domain expertsElectrical engineers, transportationengineers, environmental scientists,

biologists, climatologists

Stakeholders

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computer scientists, but because hu-mans are key to sustainability solu-tions, there is a great need for “socio-technical sciences that anticipate, evaluate and design cognizant of re-bound effects”8 and other infl uences of technology on human behavior.

AI and SustainabilityFinally, we come to the area that this article inaugurates: AI and sustain-ability. The computing areas that I highlighted earlier all invite AI meth-ods to facilitate progress. In green IT, for example, there are intelligent controls during use phases, and plan-ning and scheduling concerns dur-ing manufacture, such as shortening supply chains to reduce ecological footprints. There is also a nascent movement toward AI for sustainable design, including cradle-to-cradle design,18 intended to eliminate waste through low-energy reclamation processes.

Although Figure 1 labeled “intel-ligent systems” as an area distinct from optimization and cyberphys-ical systems (CPSs), they are not mutually exclusive. Optimization has a rich history both in and outside of traditional AI boundaries. Exemplar applications of optimization for sus-tainability include supply-chain plan-ning, optimal wind-farm arrange-ment on small and large scales, and reserve and corridor design, where land is purchased for the benefi t of selected species under budget con-straints.2 We can imagine that in each of these examples, climate change (whether the reader believes it is hu-man caused or not) will alter what is optimal, and thus characterizing so-lution robustness and adapting solu-tions in the face of change are impor-tant challenges.

Machine learning is another im-portant methodology for sustainabil-ity. Machine learning methods are

used for such varying applications as learning to identify and count in-dividuals, or otherwise estimate dis-tributions of a particular species;19

learning patterns of use for different appliances from simple household sensors;20 and learning to predict fail-ures in aging civil infrastructure.21

Learning is also integral in real-izing a great promise of computing for customization, where nuanced characterizations of individuals are possible that are much richer than

binary-valued opinion-poll labels such as “liberal,” “conservative,” “waste-ful,” or “thrifty.” With these richer characterizations, we can fi t sustain-ability-relevant actions to individuals, resulting in large savings in energy and waste, for example, in comfort-driven services. Consider hotel air conditioners that are left running so that a new guest will not experi-ence a few minutes of uncomfort-able warmth. In an integrated cyber-physical-social network that has learned my preferences, air condition-ers in a room reserved for me will be shut down, at least until my arrival.

Intelligent CPSs, as the last illustra-tion suggests, are yet another class of systems that will receive considerable

attention in this new AI and Sustain-ability department. CPSs are at the intersection of computing and the physical world. They include static and dynamic sensor networks and smart appliances, buildings, cars, highways, and cities. Through monitoring and action in the physical world, CPSs will have second-order effects relative to sustainability concerns, and these ef-fects might be environmentally harm-ful or benefi cial. Robotics is another highly relevant CPS class, particularly for monitoring the environment. Considerable work is underway on au-tonomous underwater vehicles (AUV) for monitoring ocean and fresh-water ecosystems and autonomous aerial and ground vehicles for moni-toring in emergencies ranging from nuclear accidents to wildfi res.

Looking AheadThis article has barely touched on the vast possibilities for intelligent systems to address sustainability concerns. Work in this area will of-ten boil down to augmenting human decision-making capabilities in the face of uncertainty and other complex-ities. In some cases, such as emergency response, intelligent systems will re-duce the latency of response while in-creasing its quality. In other settings requiring and allowing for delibera-tion, intelligent systems can facilitate better-informed and better-reasoned decisions. We can hope that reli-ance on intelligent systems will have positive second- and third-order ef-fects on the manner in which humans reason—a machine learning system, for example, typically requires data and decisions stemming from their recommendations will be informed by evidence, perhaps serving as ex-emplars of reasoning. Pedagogical goals and strategies can be designed into these systems from their incep-tion, thus not simply offl oading work

Work in this area will often boil down to augmenting human decision-making capabilities in the face of uncertainty and other complexities.

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and/or providing recommendations, but helping humans to become better problem solvers at the same time.

Clearly, research, education, and application in sustainability will chal-lenge AI along many trajectories, tak-ing us outside our usual boxes, as ap-plication inspired and use-driven basic research often does.22 It’s an impor-tant time for AI as we grapple with the complexities of designing a sustainable and equitable society. I am excited to see what emerges and hope that much of it will be reported in these pages.

AcknowledgmentsI thank Erwin Gianchandani and Mary

Lou Maher for helpful comments on earlier

drafts of this article. I acknowledge support

from the US National Science Foundation,

under the auspices of the Intergovernmen-

tal Personnel Act, where I served as a pro-

gram director from 2007 to 2010, during

which much of my experience in computing

and sustainability was amassed and ideas

formulated. The opinions expressed herein

are not necessarily those of NSF or the col-

leagues who I have acknowledged.

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Douglas H. Fisher is an associate profes-

sor of Computer Science at Vanderbilt Uni-

versity. Contact him at douglas.h.fisher@

vanderbilt.edu.

Selected CS articles and columns are also available for free at

http://ComputingNow.computer.org.

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