RESEARCH ARTICLE
Artificial Interdisciplinarity: Artificial Intelligence
for Research on Complex Societal Problems
Seth D. Baum1
Received: 16 September 2019 / Accepted: 3 July 2020/
#Springer Nature B.V. 2020
Abstract
This paper considers the question: In what ways can artificial intelligence assist withinterdisciplinary research for addressing complex societal problems and advancing thesocial good? Problems such as environmental protection, public health, and emergingtechnology governance do not fit neatly within traditional academic disciplines andtherefore require an interdisciplinary approach. However, interdisciplinary researchposes large cognitive challenges for human researchers that go beyond the substantial
challenges of narrow disciplinary research. The challenges include epistemic divides
between disciplines, the massive bodies of relevant literature, the peer review of workthat integrates an eclectic mix of topics, and the transfer of interdisciplinary researchinsights from one problem to another. Artificial interdisciplinarity already helps withthese challenges via search engines, recommendation engines, and automated contentanalysis. Future “strong artificial interdisciplinarity ”based on human-level artificial
general intelligence could excel at interdisciplinary research, but it may take a long timeto develop and could pose major safety and ethical issues. Therefore, there is an
important role for intermediate-term artificial interdisciplinarity systems that could
make major contributions to addressing societal problems without the concerns asso-ciated with artificial general intelligence.
Keywords Artificialintelligence .Interdisciplinaryresearch .Automatedcontentanalysis .
Artificial general intelligence
1 Introduction
This paper explores the question of how artificial intelligence (AI) can be of value to
interdisciplinary research (IDR) aimed at addressing major societal problems such aspublic health, environmental protection, and emerging technology governance. ThesePhilosophy & Technology
https://doi.org/10.1007/s13347-020-00416-5
\*Seth D. Baum
seth@gcrinstitute.org
1Global Catastrophic Risk Institute, PO Box 40364, Washington, DC 20016, USA
problems are highly complex and multifaceted and do not fit neatly into traditional
academic disciplines. For example, the study of global warming integrates environ-mental science, social science, policy, energy engineering, and more. This extremebreadth makes IDR a very difficult cognitive challenge for even the most capablehuman researchers. If AI can help meet this challenge, that could be of considerablevalue for addressing many major societal problems.
This paper is especially interested in the possibilities for near- to intermediate-term
AI systems that use existing AI techniques or relatively straightforward extensions ofthem. These would ideally be systems that computer scientists could work toward rightnow, and that would also be well short of strong AI, i.e., human-level or super-human-level artificial general intelligence (AGI). A central question of this paper is: Can we
imagine tractable, real-world AI system designs that would make significant contribu-tions to understanding and addressing complex and important societal problems, butwithout posing the major risks and ethical issues associated with strong AI? Ih u m b l y
acknowledge that I do not know the answer to this question, but I do know that it is a
worthy question to ask, and I can lay out some relevant considerations. That is the aimof this paper.
To streamline the discussion, let us introduce the following term:Artificial interdisciplinarity (A-ID): Artificial intelligence that performs interdisci-
plinary research or supports other agents in the performance of interdisciplinaryresearch.
Some emphasis is warranted on the clause “or supports other agents in the perfor-
mance of. ”Per this definition, an AI system that human researchers use to perform IDR
counts as A-ID. Given this wider definition, it follows that there is A-ID in active useright now. Likewise, ideas for new forms of A-ID can leverage human collaboration.To make a valuable contribution to addressing societal problems, A-ID only needs tohelp with some aspect of IDR; it does not need to be able to complete IDR projects withno human collaboration. Indeed, it is plausible that an A-ID system that could succeedat the full range of IDR tasks would need to be strong AI.
As a scholarly contribution, this paper sits at the intersection of literatures on IDR
and both near-term and long-term AI, as well as the nascent concept of intermediate-
term AI. Regarding IDR, there is an active line of research on the cognitive challengesfaced by human interdisciplinary researchers (Bracken and Oughton 2006;K e e s t r a
2017 ; MacLeod 2018 ). To the best of my knowledge, this literature has not yet
considered the current or potential future role of AI. Regarding near-term AI, anextensive literature applies existing AI tools to support the research process. Most ofthis is oriented toward narrow disciplinary research, but some studies involve IDR(Nunez-Mir et al. 2017; Tuhkala et al. 2018). There is also a more general, extensive,
and longstanding use of computers in IDR, especially in the physical sciences (Crease
2017), though that is beyond the scope of this paper. Regarding long-term AI, some
studies consider the prospect of superintelligent AI designed as “oracles ”to answer a
wide range of questions that humans might have, some of which are in the domain ofIDR (Armstrong et al. 2012; Yampolskiy 2012). Each of these lines of research is
discussed in greater detail later in the paper. Finally, recent research has argued thatintermediate-term AI has gone overlooked relative to near-term and long-term AI(Parson et al. 2019a ,2019b ; Baum 2020). This paper provides dedicated attention to
intermediate-term AI and concurs that this time period merits attention.S. D. Baum
The paper also seeks to encourage AI research to orient itself toward addressing
important societal problems and to provide some direction for this. At present, much ofAI research is instead oriented toward more intellectual goals of expanding the capacityof AI systems or toward profitable activities of technology corporations. As is well-known in a lot of IDR, intellectual progress and profit do not necessarily bring progresson societal problems and in some cases can make the problems worse, for example,
intellectual progress on dangerous technologies and profitable activity that pollutes the
environment. IDR is also not necessarily good for society and can be oriented toward awide range of ends including intellectual progress and profit. Furthermore, even whenIDR is focused on addressing societal problems, it is still not a panacea for them —the
existence of literature addressing societal problems does not on its own ensure that theproblems actually get addressed. That said, IDR is vital for addressing complex societalproblems, and this is the primary focus of IDR today. The paper therefore encouragesA-ID research to join forces with the extensive communities of IDR focused on
addressing societal problems.
I approach the topic of AI and IDR as a veteran interdisciplinary researcher with
some knowledge of both AI and the meta-study of IDR. I am not a computer scientist,and so I can speak with less confidence on what the AI options for IDR may be.Therefore, this paper seeks to lay out some general considerations and to hopefullyserve as a prompt for an interdisciplinary conversation among computer scientists,scholars of IDR, and others with relevant perspectives. As this paper explains, this sortof interdisciplinary conversation can be difficult, but it is vital for making progress on a
wide range of cross-cutting topics.
In keeping with much of the literature on IDR, this paper is written in terms of
“societal problems ”instead of the concept of “social good ”that frames this special
issue. These two terms are broadly compatible, in that addressing societal problems isone major way to advance the social good. IDR is commonly used to make progress onspecific problems or issues, and is perhaps less commonly used to understand oradvance the good in a more abstract or general sense. The challenge of understandingthe complex and distinctive features of many specific societal problems is a primary
focus of IDR.
As the first dedicated discussion of A-ID, the paper provides a broad discussion.
Section 2 introduces IDR and outlines its cognitive challenges. Sections 3 –5d i s c u s sA -
ID over the near-term, intermediate-term, and long-term. Section 6 discusses issuesraised by A-ID. Section 7 concludes with general thoughts on the role of AI inaddressing societal problems.
Sections 3 –5 are organized around time scales because each time scale poses distinct
issues for A-ID. Near-term AI can be defined as AI that currently exists or could exist
in the near future via relatively straightforward extensions of current AI systems.
Attention to near-term A-ID includes description of existing A-ID systems and pros-pects for extending them. Intermediate-term AI goes beyond what is already feasible ornow in development, but it stops short of the more extreme forms of AI that could existover the long term. This paper defines long-term AI as AI with capabilities that equal orexceed human cognition; terms for this include strong AI, human-level AI, artificialgeneral intelligence, ultraintelligence, and superintelligence. Discussions of AI areoften divided between the near term and the long term, with some debate over which
is more important (Baum 2018 ; Cave and Ó hÉigeartaigh 2019 ; Prunkl andArtificial Interdisciplinarity: Artificial Intelligence for...
Whittlestone 2020). Less attention has been paid to AI over intermediate time scales
(Parson et al. 2019a ,2019b ), though this paper finds that A-ID at all time scales can be
important.
2 Interdisciplinary Research and its Cognitive Challenges
IDR has been defined in a variety of ways (Klein 2017). One notable definition is
provided in the US National Academies report Facilitating Interdisciplinary Research :
Interdisciplinary research (IDR) is a mode of research by teams or individuals that
integrates information, data, techniques, tools, perspectives, concepts, and/or theoriesfrom two or more disciplines or bodies of specialized knowledge to advance funda-mental understanding or to solve problems whose solutions are beyond the scope of asingle discipline or area of research practice (Committee on Facilitating
Interdisciplinary Research and Committ ee on Science, Engineering, and Public
Policy 2005,p . 2 ) .
Interdisciplinarity is often compa red with related concepts, especially
multidisciplinarity and transdisciplinarity. To generalize, multidisciplinary research iscommonly understood to be the closest to disciplinary research, consisting of researchthat has input from multiple disciplines without integrating them; IDR takes anadditional step away from disciplinary research by also integrating insights from acrossdisciplines; transdisciplinary research is the furthest from disciplinary research by
transcending disciplinary boundaries altogether, as if they did not exist, and often also
transcending boundaries between academic and nonacademic sources of knowledge.These distinctions are fuzzy and contested (Klein 2017) but suffice for purposes of this
paper. Note that some of the research described in this paper may be more preciselyclassified as transdisciplinary, though the distinction between interdisciplinarity andtransdisciplinarity is not crucial for this paper and the paper will use the two terms moreor less interchangeably.
IDR has traditionally had two somewhat distinct motivations. See Bernstein ( 2015)
for an intellectual history. One motivation is intellectual, seeking the understanding of
cross-cutting topics and the synthesis of knowledge accumulated in (and beyond) thedisciplines. An especially ambitious form of this motivation, associated mainly withtransdisciplinarity, is for unifying all of the various strands of human knowledge andintellectual activity. The concept of consilience as developed by Wilson ( 1998)i so n e
work in this direction. While intellectually motivated IDR is commonplace, IDR isperhaps more commonly motivated by practical societal problems. Environmentalproblems have been a longstanding focus, given their societal importance and multi-
faceted nature, involving both social and ecological systems. More generally, IDR
often —but not always –addresses social and policy issues associated with science and
technology; this includes social and policy issues raised by AI.
A different type of motivation for IDR is the complexity of many important subjects
of study. Complexity can be defined in a variety of ways; one definition proposed in thecontext of IDR is that complexity is an attribute of systems whose components —which
may themselves be systems –interact in predominantly nonlinear ways (Newell 2001). I
would add that while this definition ’s emphasis on nonlinear systems has a mathemat-
ical tone, the interconnections found in IDR are often best understood in moreS. D. Baum
qualitative terms. Regardless, the definition points to the distinction between
multidisciplinarity and interdisciplinarity. Many research subjects do not fit neatlywithin any one academic discipline, but research on these subjects does not necessarilyrequire integration across disciplines. For example, oceanography and battery technol-ogy are both relevant to the study of global warming, but oceanographers and batteryengineers typically do not need to consider each others ’work when doing their own.
(This example is from a critique of IDR by Jacobs 2013,p . 1 3 0 –131.) This disconnected
work would classify as multidisciplinary but not interdisciplinarity. As a contrastingexample, global warming policy may affect industrial activity, which affects green-house gas emissions, which affect climate patterns, which affect natural hazards, whichaffect human welfare. Interdisciplinary global warming policy research may considerthese complex interconnections to assess the overall merits of a policy idea.
All research, whether interdisciplinary or disciplinary, has social as well as intellec-
tual dimensions. Indeed, one argument in favor of disciplines is that they provide a
useful structure for organizing populations of human researchers within universities
(Jacobs 2013). Interdisciplinary research centers serve a similar social purpose. Like-
wise, some challenges for IDR are of a social and not intellectual nature. For example,the discipline-based department system at most universities often incentivizes disci-plinary research over IDR. These social dimensions may be less relevant to an A-IDRsystem. AI may be more skilled at IDR than humans because AI lacks social reasons tocluster into disciplines. However, if an A-IDR system derives its knowledge from thecorpus of human scholarship, then it may learn and perpetuate disciplinary divisions,
just as current AI systems learn and perpetuate human biases obtained from other
human-produced datasets.
The remainder of this section surveys some cognitive challenges of IDR. All forms
of research can be cognitively difficult, but IDR poses some distinct challenges thatmake it especially challenging for human researchers. Note that these challenges alsoapply to transdisciplinary research and to a lesser degree to multidisciplinary research.For more general discussion of interdis ciplinarity, multidisciplinarity, and
transdisciplinarity, see e.g., Hoffmann et al. ( 2013 ), McGregor ( 2014 ), Bernstein
(2015), Lawrence ( 2015), Scholz and Steiner ( 2015), Menken and Keestra ( 2016),
and Frodeman ( 2017); for an alternative and more critical perspective, see Jacobs
(2013).
2.1 Disciplinary DividesDifferent academic disciplines commonly approach the same topic from different
perspectives, using different conceptual paradigms and different language, featuring
different intellectual traditions and standards, and often covering different aspects of the
same topic. This creates the cognitive challenge of integrating the disparate input into aunified understanding of a topic (Bracken and Oughton 2006;K e e s t r a 2017;M a c L e o d
2018). As a consequence, IDR requires a laborious and often frustrating process of
translation between different disciplines. It is a translation of language as well as ofintellectual norms and epistemic perspectives.
Much of the challenge comes from the fact that human expertise builds from years
of focused study and practice. The human mind can achieve high levels of performance
on specific tasks by forming complex representations of the task and its surroundingArtificial Interdisciplinarity: Artificial Intelligence for...
context —for example, chess masters “chunking ”(Chase and Simon 1973)t o g e t h e r
complex patterns of chess pieces. The sheer difficulty of developing expertise precludesindividual researchers from being expert on the full range of subjects that are relevant tocomplex societal problems. Furthermore, the very nature of human expertise can makeit harder for experts in one discipline to understand other disciplines. Indeed, the term“discipline ”implies a certain disciplining of the human mind to think in certain ways.
A human mind disciplined to think in one way can struggle to think in another way.
Compounding the problem is the fact that academia is traditionally divided into
thematic disciplines that do not map to societal problems. A scholar is typically trainedand employed as, for example, a computer scientist or a social scientist, not as an experton the social implications of computers. Academia has made some effort to restructuretoward IDR, but the traditional disciplines still dominate. As a consequence, disciplin-ary divides are even greater than they in principle need to be.
2.2 Massive Literatures
Even if a researcher or research team is able to parse the disparate disciplinary
contributions to a particular topic, there remains the challenge of reading the relevantliterature. Even within a single discipline, literature review can be a highly laborioustask. For complex interdisciplinary topics, it quickly becomes overwhelming.
For example, the Intergovernmental Panel on Climate Change (IPCC) is a body of
top global warming researchers from around the world. It produces periodic syntheses
of the global warming literature for high-level policy audiences. IPCC reviews are
massive exercises, involving hundreds of scientists each putting in hundreds of hoursover periods of up to 5 years (Victor 2015). Despite this massive scale, the literature is
now at a point where the IPCC ’s teams of researchers are struggling to keep up (Minx
et al. 2017). Climate change is an especially complex issue with a large literature even
by IDR standards, but the IPCC is also an unusually large IDR project. Other IDRprojects with narrower scopes and smaller teams face similar literature challenges.
The IPCC is distinctive in its effort to synthesize the entire peer-reviewed literature
on a complex interdisciplinary topic. Researchers who merely wish to contribute to the
literature do not need to read so much. However, gaining a basic understanding ofglobal warming in all its facets still requires reading at least some literature acrossnumerous subjects in natural science, social science, engineering, the humanities,policy, etc. This is an extensive undertaking, and it is compounded by the addedchallenge of translating across the disciplinary divides.
2.3 Peer Review
IDR poses distinct peer-review challenges. IDR commonly works at the interface of
multiple traditional academic disciplines. In many cases, it is uncommon to haveexpertise in each of the disciplines. Indeed, the person or team performing the IDRmay be the only one in the world working across that particular set of topics. Forexample, this paper works at the intersection of AI and IDR; I am not aware of anyother research that does this.
The eclectic mixes of topics found in IDR ma ke it unusually difficult to peer review
(Laudel 2006; Pautasso and Pautasso 2010; Holbrook 2017). If no other researchers haveS. D. Baum
expertise across the range of topics covered i nt h ew o r k ,t h e ni tm a yb en e c e s s a r yt of o r ma n
interdisciplinary team of rev iewers, but then this team must overcome its own disciplinary
divides, which is a substantial task just to com plete a single review. Or, the task of working
across the disciplinary divides may fall to the people handling the submissions —journal
editors, grant program managers, etc. —but then this just adds to their burden.
It has been suggested that the difficulty of reviewing IDR, combined with the
tendency of researchers to favor work from their own specialty, biases reviewers
against IDR (Laudel 2006). One study has found that IDR grant proposals are indeed
funded at a lower rate (Bromham et al. 2016). This is another way in which the current
academy may be systematically biased against IDR, limiting its contribution to ad-dressing important societal problems.
2.4 Transfer
Some research aims to transfer insights from the study of one societal issue to another.
Transfer is seldom discussed in literature about IDR, which tends to focus on one issueat a time (but see Krohn 2017). Transfer is not a panacea, especially given the distinct
attributes that each societal issue has, which limits the extent to which insights transferfrom one issue to another. Nonetheless, it can be a powerful way to study societalissues. Transfer can be especially helpful for newly emerging issues that have not yetbuilt up a robust literature, such as issues involving emerging technologies. Forexample, Altmann and Sauer ( 2017) transfer insights from strategic studies of nuclear
weapons to the study of newly emerging autonomous weapon systems. Baum ( 2017a )
transfers insights from the psychology of promoting environmentally beneficial behav-iors among the lay public to the study of how to encourage AI researchers to pursuesocially beneficial AI designs.
The psychology of transfer finds that peopl e tend to be more successful at it when there
is relatively little “distance ”between the domain transferred from and the domain
transferred to(Perkins and Salomon 1992). For example, it may be easier to transfer
insight from one weapon system to another (as in Altmann and Sauer 2017)t h a ni ti st o
transfer insight from lay public environmental behavior to expert behavior on technology
development (as in Baum 2017a ). Unfortunately, much of the insight available to be
transferred lies at a considerable conceptual distance. A major challenge is simplyrecognizing the cross-issue similarity. Indeed, another finding from the psychology oftransfer is that people often struggle to apply lessons to “problem isomorphs, ”i.e., to
another domain that is functionally equivalent but different in appearance (Simon andHayes 1976). People even struggle with problem isomorphs when both domains are
presented to them. IDR transfer is more difficult because researchers typically must
identify the two domains for themselves. On top of that, IDR transfer can require bridging
three sets of epistemic divides: the divide between the two issues and the divides withineach of the two issues. This can make for an especially difficult cognitive task.
3 Near-Term A-ID
This section discusses A-ID that currently exists or could exist with relatively straight-
forward extensions of current technology. Note that this section takes a relatively broadArtificial Interdisciplinarity: Artificial Intelligence for...
view of what qualifies as AI. By narrower standards, some of what is presented here
may not qualify as AI. The exact definition and scope of AI is complex and contro-versial and beyond the scope of this paper; for further discussion, see, e.g., Legg andHutter ( 2007) on definitions and McCorduck ( 2004) on how the scope of what is
considered to classify as AI has gotten narrower over time.
3.1 Search Engines
Perhaps the most ubiquitous form of A-ID is the search engine for Internet and
scholarly databases, including Dimensions.ai, Euretos, Google Scholar, IRIS.AI,Microsoft Academic, Omnity, Semantic Scholar, SourceData, and Web of Science.Search engines are valuable for many forms of research, but they are especiallyvaluable for IDR. In narrow disciplinary fields, researchers need to follow a relativelysmall body of literature. They may even be able to keep up with the relevant literature
without the use of search engines, instead identifying literature by reading certain
journals and sharing relevant literature within peer communities. In contrast, IDRfrequently pushes researchers outside their areas of familiarity and requires them toidentify relevant literature from within extremely large bodies of work.
Search engines can help with each of the IDR challenges described in Section 2.
They can provide some help to the challenge of overcoming disciplinary divides byproviding a tool to explore the literatures of other disciplines, though this leaves openthe challenge of understanding publications in unfamiliar disciplines. They can be used
to navigate the massive literatures on interdisciplinary topics, such as by searching for
multiple keywords to find literature on specific themes for a societal problem —for
example, “global warming ”and“cost-benefit analysis. ”T h e yc a nb eu s e dt oi d e n t i f y
peer reviewers —for example, the authors of papers on global warming and cost-benefit
analysis. And, with some creativity, they can support the transfer of insights acrosssocietal problem —for example, one could search for literature on global warming and
cost-benefit analysis to identify insights that can be transferred to cost-benefit analysisof other global environmental issues.
Search engines could be improved. To my eyes, an important area for improvement
is in the handling of synonyms and, more generally, the identification of terminology.When searching for literature on an unfamiliar topic, identifying the right keywords tosearch for can be a major impediment. Once one knows the right keywords, relevantliterature often flows abundantly. It would be especially valuable to have tools thatcould identify keyword synonyms used by other disciplines. Handcrafted resources,such as Wikipedia, can be helpful, but such resources are not available for allkeywords. Automated tools for this could be quite helpful.
Synonym identification is an active subject of AI research. One approach is to
analyze patterns in the text surrounding a word, known as “distributional word vectors ”
or“word embeddings, ”on grounds that synonyms can be used in the same way and
therefore tend to be surrounded by similar text (Leeuwenberg et al. 2016;M o h a m m e d
2020). However, this work uses large linguistic datasets (i.e., large collections of text).
Much less text is available for identifying synonyms in academic literature, especiallyfor the many specialized concepts and small subfields. Indeed, the rarity of specialistterms can be a distinguishing characteristic of academic publications. One academic
search engine, Omnity ( https://www.omnity.io ), uses the rarest words of a document toS. D. Baum
identify other documents with a similar mix of rare words on grounds that documents
with the same rare words are likely to be related (Perkel 2017). Omnity ’s approach may
work well within a discipline, but it may be less well suited for search across disciplinesthat use different terms for the same concepts. Further research may be needed todevelop techniques for synonym identification in small academic literatures.
3.2 Recommendation Engines
At least two forms of recommendation engines are currently available for research. The
first provides recommendations of publications. For example, Google Scholar providespublication recommendations that are customized for individual user profiles, andElsevier provides publication recommendations that are customized for specific publi-cations on its ScienceDirect website. A more specialized example is the project http://x-
risk.net that provides recommendations of publications in the field of catastrophic risk
(Shackelford et al. 2020 ). These recommendations provide an additional way for
researchers to identify relevant literature on the topics they are studying. Note thatthe recommendations cover topics that researchers have already identified, via theiruser profiles or the publications they are already looking at, so they are less valuable forthe IDR challenge of exploring unfamiliar lines of research. Therefore, in terms of thechallenges described in Section 2, these recommendation engines can be especiallyvaluable for handling massive literatures, and they may be of relatively limited valuefor overcoming disciplinary divides.
A second form of research recommendation engine recommends potential peer
reviewers to journal editors. This is used, for example, in the Elsevier Evise system,which was launched in 2015. Insofar as Evise provides good recommendations of peerreviewers, it can lighten the burden on journal editors, as noted, for example, by theeditor of the interdisciplinary Elsevier journal Ecological Economics (Hukkinen 2017).
Another example is the Toronto Paper Matching System developed for matchingpapers to reviewers for computer science conferences (Charlin and Zemel 2013). While
the Toronto Paper Matching System is used predominantly for computer science, it
potentially could be expanded to more IDR domains. The value of recommendation
engines for IDR could be especially large due to the difficulty of identifying appropriatereviewers for IDR.
One way to improve interdisciplinary recommendation engines would be to stream-
line the process of building specialized recommendation engines. The recommendationengine presented by Shackelford et al. ( 2020) uses a custom artificial neural network
trained with a hand-coded dataset of articles found to be relevant to the field ofcatastrophic risk by an open ( “crowdsourced ”) team of researchers in the field. Other
fields could apply the same approach. Hand-coding the training dataset is laborious but
is vital for aligning recommendation engine results with the interests of people in thefield. The labor required for this may be large, but it does not require any skills otherthan knowledge of the field. In contrast, developing the artificial neural network doesrequire more specialized skills. Fields that lack people with these skills may struggle todevelop their own recommendation engines. Therefore, the process of building field-specific recommendation engines could be streamlined by developing an artificialneural network that could be trained with data from any field and in particular by
developing a more accessible user interface for it.Artificial Interdisciplinarity: Artificial Intelligence for...
3.3 Automated Content Analysis
A final application of AI to research is the use of machine learning for analyzing the
content of the literature on a given topic, known as “automated content analysis ”
(Nunez-Mir et al. 2017). Essentially, it treats academic literature as an instance of
“big data ”, sometimes referred to as “big literature ”(Nunez-Mir et al. 2016). Automat-
ed content analysis analyzes clusters of key words and phrases to produce statistical
trends in literatures. This can be of value for learning what a literature has tended tofind, where gaps may lie, etc. It can also be used for identifying relevant literature on atopic and in this capacity may be considered a form of search engine. It is of particularinterest in the context of systematic reviews, in which the aim is to provide a completeand unbiased account of the literature on a particular topic. In terms of the challengesdescribed in Section 2, this can be of high value for handling the massive literatures ofIDR.
While automated content analysis has been used mainly for narrow disciplinary
research, it has been used for IDR studies of forest ecology (Nunez-Mir et al. 2017)a n d
participatory design (Tuhkala et al. 2018 ). The Nunez-Mir et al. ( 2017 ) study is
illustrative of the possibilities. It analyzes the text of 14,855 abstracts in 7 forestryjournals. It uses a mix of supervised and unsupervised seeding, meaning that centralconcepts are obtained via a mix of human input and algorithmic text mining. Subse-quent algorithmic analysis assesses the preponderance of the concepts in the abstractdatabase. A primary finding of the study is that most of the abstracts do not show an
interdisciplinary approach and very few considered social dimensions of forest ecology.
In consideration of this finding, Nunez-Mir et al. ( 2017) call for more IDR on forestry,
especially its social dimensions.
Note that the Nunez-Mir et al. ( 2017) study does not reveal anything about the
nature of forests or their interdisciplinary attributes beyond the list of central concepts.The output is a map of the literature, not an interpretation of what the literature meansand what its societal implications are. This is a general limitation of current automatedcontent analysis. Thus, as Sutherland and Wordley ( 2018,p . 3 6 6 )w r i t e :
Advances in artificial intelligence and machine learning could make it easier to
perform tasks such as locating papers for defined topics using search terms, categoriz-ing papers as relevant for further consideration, and producing systematic maps. But forall fields, assessing the quality of individual studies, writing up summaries and so onwill continue to require skilled humans, at least for the foreseeable future.
The above remark about “the foreseeable future ”suggests that the AI needed for
interpreting literature and its societal implications is significantly beyond the capacityof current AI. My own judgment is that this is probably correct. Therefore, interpreta-
tion is considered in the following section on intermediate-term A-ID.
4 Intermediate-Term A-ID
Intermediate-term A-ID systems would go significantly beyond current capabilities but
not so far beyond that they would constitute “strong ”A-ID with human-level or super-
human-level capabilities. Intermediate-term is of interest because it could be of greater
value to IDR than near-term A-ID while avoiding the downsides associated with strongS. D. Baum
AI (see Section 5). Ideally, designs for intermediate-term A-ID would derive at least in
part from existing AI techniques such that current AI researchers could work towardtheir development. There can also be value to envisioning intermediate-term A-ID thatrequires new techniques but would stop short of strong AI. Of course, the sooner newA-ID capabilities become available, the sooner they can contribute to addressingimportant societal problems.
As stated in the introduction, I am not a computer scientist, and so I am less
confident in my thoughts on the design of intermediate-term A-ID systems. Nonethe-less, a few basic thoughts on computer science factors are offered as a contribution tothe topic, alongside some social factors that I can comment on more confidently.
4.1 InterpretationOne direction to extend current A-ID capabilities would be via the interpretation of
research publications. As Sutherland and Wordley ( 2018) explain, this is a task currently
left for human researchers (see Section 3.3). However, progress in the field of machinereading (a.k.a. natural-language understanding/interpretation) could change this. Forexample, one project to automate interpretation is the Elsevier-sponsored ScienceIE(Augenstein et al. 2017). The current focus of ScienceIE is on identifying phrases and
relations between phrases within narrow disciplinary literatures. Its aims are ambitious:“Say you have a question about a paper: A machine learning model reads the paper and
answers your question ”(Augenstein as interviewed by Stockton 2017).
Automated interpretation of research publications would be of considerable value
for many forms of research, but it may be of particularly large value for IDR, due to thelarge and diverse literatures involved in IDR. An integrated system for automatedcontent analysis and interpretation would be especially valuable for synthesizing theinsights contained in vast and diverse IDR literatures. Such capability could drasticallyreduce the burden of human researchers in IDR synthesis projects like the IPCC andcould likewise enable similar synthesis projects across a wider range of societalproblems. In terms of the IDR challenges described in Section 2, interpretation would
be especially valuable for handling disciplinary divides and massive literatures, espe-
cially if combined with automated content analysis.
Publications can be interpreted in many different ways, some of which may be easier
for A-IDR. For example, interpreting whether a publication has presented results thatare statistically significant may be easier for A-IDR than interpreting whether thepublication has presented results that could help address some societal problem. Itmay be the case that current AI paradigms based on neural networks will struggle withthe interpretation of complex interdisciplinary texts because, as discussed by Marcus
(2018 ), neural networks are skilled at finding statistical relationships in complex
datasets but struggle to handle causal relationships, hierarchies, and open-ended envi-ronments, all of which are important attributes of interdisciplinary texts. However, theextent to which this is indeed the case is left as a question for future research on A-ID.
4.2 TranslationGiven the difficulty of translating across the epistemic divides that exist between
academic disciplines (Section 2.1), it could be of considerable value to have A-IDArtificial Interdisciplinarity: Artificial Intelligence for...
systems that could assist with the translation. A-ID translation would convert between
the jargons of different disciplines or between that of one jargon and a more widelyaccessible plain English. This would reduce the linguistic burden of IDR, therebyfacilitating researchers to work across a wider range of disciplines, either alone or inteams. It would also be of value for peer reviewers tasked with reviewing interdisci-plinary work that includes topics outside the reviewers ’own areas of expertise.
Potentially, A-ID translation could build off existing work on machine translation
between languages (e.g., English to Spanish). An active area of research is forunsupervised machine translation (e.g., Lample et al. 2018 ; summarized in plain
English by Ranzato et al. 2018), in which translation does not require large datasets
of pre-translated text. Unsupervised machine translation would be important for inter-disciplinary translation because large datasets generally do not exist and would beexpensive to produce.
A-ID translation may require techniques that go beyond cross-language machine
translation because the text of different disciplines commonly varies by conceptual
basis and not just by language. In other words, much of the challenge of reading textfrom other disciplines is that readers do not know the concepts that the words aredescribing or at least are not accustomed to thinking in terms of these concepts.Interdisciplinary translation is about explaining unfamiliar concepts just as much as itis about explaining unfamiliar terminology. In contrast, cross-language machine trans-lation generally involves concepts that are familiar in both languages. For example,Ranzato et al. ( 2018) presents the translation of the phrase “cats are lazy ”from English
to Urdu; presumably Urdu speakers are already familiar with the concept of a lazy cat.
Therefore, successful A-ID translation may need to include a capacity for conceptidentification and translation that goes substantially beyond cross-language machinetranslation.
4.3 TransferThe challenge of interdisciplinary transfer (Section 2.4) maps neatly to the AI topic of
transfer learning (Pan and Yang 2009). Both involve the same essential process of
applying knowledge gained in one domain to another domain with similar features,especially to avoid the resource-intensive process of building complex knowledge setsfor multiple domains. Complex societal problems often have some similarities witheach other. Human researchers can barely scratch the surface of the potential ofinterdisciplinary transfer due to its cognitive difficulty and the very large number ofcombinations of similar societal problems. If AI transfer learning could be applied toIDR, it could open up vast possibilities for learning about societal problems.
Current AI transfer learning involves tasks that are rather far removed from IDR. For
example, one common AI transfer learning task is image recognition (Zamir et al.2018). Whereas images are readily expressed in statistical terms (e.g., via the binary
representation of a digital image file), societal problems are not. To be sure, there areways to express societal problems in some quantitative terms, such as via cost-benefitanalysis for the evaluation of solutions. But much of the insight involved in interdis-ciplinary transfer is qualitative. A-ID transfer learning would need reliable ways toquantify this insight. It would also need some degree of translation, given the differing
terminology and other linguistic constructs that can exist across societal problems.S. D. Baum
There is some reason to believe that A-ID transfer learning could be quite difficult to
achieve. Computers are very capable at churning through large numbers of combina-tions of predefined concepts, just as they are very capable at churning through largenumbers in general. However, they may struggle at identifying the sorts of connectionsbetween concepts that humans find meaningful. In a discussion of creativity in humansand computers, Boden ( 2009) argues that humans are relatively skilled and computers
relatively unskilled, at “combinatorial creativity, ”meaning creativity in making new
associations between seemingly unrelated concepts. Humans have rich mental modelsof the world built up throughout lifetimes of experience and observation, which enableus to devise analogies and imagery and other forms of combinatorial creativity.Endowing computers with anything similar is very difficult, and computers likewisestruggle at combinatorial creativity. Interdisciplinary transfer is, at least in part, a formof combinatorial creativity, and so A-ID systems may struggle with it.
5 Long-Term A-ID
As an abstract matter, it is not hard to imagine some future AI that is capable ofperforming the full range of cognitive tasks involved in IDR. A strong AI/human-levelAGI would presumably be able to do IDR at least as well as humans could. Whereas“narrow ”AI is only intelligent for a narrow range of cognitive tasks, AGI is general in
the sense of being able to perform a wide range of cognitive tasks. Human-level AGI
could perform the same breadth of cognitive tasks as human minds and with the same
skill as human minds. Such an AI could do any other cognitive task that humans cando, so it would presumably also be capable of IDR, potentially even so capable as toeffectively solve all of society ’s problems. The AI may not be operating in complete
isolation from humans —studies of human problems would likely require some human
participation —but the AI may be able to play the entire suite of cognitive roles
currently played by human interdisciplinary researchers.
The prospect of solving societal problems is one common motivation for developing
AGI. This is seen in the stated goals of active AGI research and development projects.
For example, the AGI project AIDEUS observes that the “limitation of intellectual
possibilities is fully experienced by scientists ”and that this “concerns all problems of
people –from traffic jams to economic crises and wars. ”That is very much in the spirit
of the present paper. AIDEUS states this as a motivation for building strong AI.
1As
another example, DeepMind envisions AI for “helping humanity tackle some of its
greatest challenges, from climate change to delivering advanced healthcare. ”2Finally,
Jeff Hawkins of Numenta calls for AI to help humanity “face challenges related to
disease, climate, and energy ”(Hawkins 2017). Note that these and other examples can
be obtained from Baum ( 2017b ).
As valuable as AGI could be for IDR and for addressing complex societal problems,
it faces several important downsides. First is the technical difficulty of building AGI.Despite the numerous projects working on AGI, the current state of the art remainsquite far removed from it. It is not clear if or when AGI would be built; projections of
1http://aideus.com/community/community.html
2https://deepmind.com/blog/learning-through-human-feedbackArtificial Interdisciplinarity: Artificial Intelligence for...
decades or longer are common (Baum et al. 2011; Grace et al. 2018). That is a long
time to wait for solutions to major societal problems. For some problems, such asnuclear war or extreme pandemics, the effects could be so severe that civilization wouldfail before it has the chance to build AGI.
The desire to use AGI for societal problems could be additionally problematic by
putting a dangerous time pressure on AGI development. Herein lies a dilemma. On one
hand, earlier AGI would help with other societal problems. On the other hand, a rushed
AGI could skimp on built-in safety measures and ethical design, increasing the risk ofadverse or even catastrophic harm caused by the AGI. To the extent that major societalproblems can be addressed without AGI, this buys AGI developers more time to get itdone right.
As an aside, note that some scholarship on IDR may be of value for the safe
development of AGI, noting that the project of building AGI is itself an interdisciplin-ary task. For example, Keestra ( 2017) documents how a failure of interdisciplinary
communication contributed to the 1986 space shuttle Challenger disaster and analyzes
how future interdisciplinary communication could go better. Communication amongthose developing and launching AGI may benefit from this analysis.
Careful AGI design may be needed even for AGI systems that exist only to provide
input on societal problems. This is a theme of the literature on the prospect ofsuperintelligent “oracle ”AIs that are designed to only answer questions that humans
pose to it (Armstrong et al. 2012;Y a m p o l s k i y 2012). The oracle design is sometimes
conceived as a way to make AGI safe. The hope is that by restricting the AI to only
answering questions, humanity can prevent the AI from taking dangerous physical
actions to alter the world. However, studies of superintelligent oracles have raised theconcern that they could still be dangerous, for example, by manipulating the peoplewho interact with it (ibid.). The prospect of strong A-ID being dangerous in this way isanother reason to explore near- and intermediate-term A-ID that can help addresssocietal problems without raising the concerns associated with strong AI.
Finally, it should be noted that the development of AGI is itself an interdisciplinary
issue that would benefit from improved capacity for IDR. The development of AGI
involves computer science, ethics, risk management, and more. Important questions
include how best to design the AGI, how best to manage the human teams that areworking on the design, and when to launch the AGI given the potential benefits andharms that could come from it. Insofar as near- and intermediate-term A-ID couldimprove the available answers to these sorts of questions, it could improve theoutcomes from AGI development. This gives another reason to pursue near- andintermediate-term A-ID, including the concepts outlined in this paper.
6D i s c u s s i o n
The limitations of near-term A-ID and the difficulties and risks of long-term A-IDsuggest an important role for intermediate-term A-ID. A-ID interpretation, translation,and transfer could offer powerful capabilities for IDR and, more generally, for under-standing and addressing complex societal problems. They could combine with existingA-ID systems (search engines, recommendation engines, and automated content anal-
ysis) to provide support across all of the cognitive challenges of IDR. This would notS. D. Baum
necessarily automate the full suite of IDR tasks —it could still leave important roles for
human researchers —but it could nonetheless be of considerable value to IDR.
On the other hand, some of these new capabilities could push uncomfortably toward
AGI. Yampolskiy ( 2013) argues that the understanding of natural language is “AI-
complete, ”meaning that any AI system that is capable of understanding natural
language would necessarily also be an AGI with a wide range of other capabilities.
Similarly, Armstrong et al. ( 2012, p.305) postulate that “the task of advancing any field
involving human-centred issues —such as economics, marketing, politics, language
understanding, or similar —is likely to be AI-complete, ”and Stockton ( 2017)s u g g e s t s
that an AI capable of peer-reviewing research papers would require AGI. Exactly what,if anything, is AI-complete may be controversial and is beyond the scope of this paper,but it is nonetheless worth considering the implications of these arguments.
These arguments imply that significant portions of A-ID interpretation, translation,
and transfer may be impossible without AGI. Interpretation inherently involves under-
standing natural language, and so there may be very little A-ID interpretation that can
be done without AGI. The translation of concepts may require understanding naturallanguage, and so, without AGI, A-ID translation may be limited to the translation ofterminology. Finally, transfer may also require understanding natural language andtherefore AGI unless the societal problems of IDR can be reduced to a simplerquantitative form. The extent to which A-ID interpretation, translation, and transfercan be done without AGI is an important open question.
Another concern is that intermediate-term A-ID could introduce certain biases into
the research. This could occur if the A-ID interprets or evaluates research differently
than human interdisciplinary researchers. For example, Hukkinen ( 2017) expresses
concern that AI may succeed at assessing the intellectual rigor of research publicationsbut struggle at assessing their significance for societal problems. Indeed, the ScienceIEproject is explicitly framed in terms of scientific publications, but the interpretation of
publications in other (non-science) areas that are essential to IDR for societal problems(such as ethics and public policy) require different ways of thinking and potentiallydifferent AI techniques. An A-ID system that can perform well on scientific rigor but
poorly on ethics and policy could lead to weak or even harmful insight on how to
address societal problems. More generally, intermediate-term A-ID systems must becarefully evaluated to ensure that they are producing constructive assistance to IDR.This will likely benefit from close collaboration between computer science developersof A-ID and human interdisciplinary researchers and potentially also people with otherbackgrounds, including stakeholders from outside academia.
The inclusion of non-academic stakeholders is especially important with an eye
toward actually addressing societal problems instead of just advancing the academic
study of them. The existence of IDR literature does not on its own imply that the
problems actually get addressed. Absent close engagement from non-academic stake-holders, there is a risk of producing solutions that work well in theory but not inpractice. Additionally —and importantly —engagement with nonacademic stakeholders
can result in different formulations of what the problems are in the first place. For thesereasons, IDR —and especially transdisciplinary research —often calls for stakeholder
participation in the research process.
Readers should be aware that IDR often involves controversy, especially when it
reaches out beyond the academy. Complex and important societal problems oftenArtificial Interdisciplinarity: Artificial Intelligence for...
involve heated disagreements between different stakeholders: between environmental-
ists and the fossil fuel industry over global warming, between rival geopolitical powersover nuclear weapons, etc. In some cases, there can be clever technical solutions thatplease everyone or at least do not upset anyone. However, more typically, solutionsinvolve difficult tradeoffs between competing factions and fundamental moral values.Readers are encouraged to consider contributing to A-ID, but they should do so with an
awareness of all that this entails.
In the extreme case, A-ID could even be misused so as to make societal problems
worse. For example, some of the same IDR that can help environmentalists refine theirmessages can also do the same for the fossil fuel industry, a dynamic that may becompounded by the extensive financial resources that the industry has at its disposal.Existing interdisciplinary research communities have often handled this by engagingwith the ethical dimensions of societal problems and striving for ethically sound IDRpractice. Work on A-ID should do the same.
Finally, it should be noted that A-ID could help with all IDR, not just IDR on
societal problems. As discussed in Section 2, IDR is perhaps most commonly per-formed for societal problems, but some IDR is oriented toward scientific and intellec-tual progress without any particular regard for societal problems. Such A-ID may besignificant from the perspective of philosophy of science; this matter is left for futureresearch.
7 Conclusion
This paper has articulated the concept of A-ID, outlined its importance for helpinghuman researches cope with and overcome the considerable cognitive challenges ofIDR, and explained how this can help address complex societal problems. A-ID existstoday in the form of search engines, recommendation engines, and automated contentanalysis, but this leaves human researchers with the bulk of the work. Insofar as A-IDsystems could contribute more to IDR, this could be a major contribution to addressing
major societal problems. AI researchers interested in applying AI to societal problems
may find this a worthy area of application. This paper describes some potential A-IDresearch directions; readers with more background in computer science than myselfmay have further suggestions. However, there are some difficult issues involved in A-ID that researchers should be cautious about, especially regarding the controversiesinherent to complex societal issues, the potential for misuse of A-ID, and the potentialrelation of A-ID to strong AI/AGI. For their part, interdisciplinary researchers cancontribute by using A-ID tools in their research and by contributing to efforts to
improve the tools. Advancing A-ID is itself an interdisciplinary task that will benefit
from contributions from people with a variety of backgrounds.
Essential IDR cognitive tasks may be AI-complete, meaning that A-ID capable of
these tasks would need to be strong AI. These tasks could include the interpretation ofresearch publications, the translation of research concepts across disciplinary divides,and the transfer of IDR insights to new societal problems. If weak/narrow A-ID isunable to handle these tasks, or other essential IDR tasks, then human interdisciplinaryresearchers may be largely on their own until the advent of strong AI. This would mean
that, despite all the disparate ongoing accomplishments of narrow AI, it would makeS. D. Baum
relatively little contribution to addressing society ’s most complex and important
problems. Alternatively, if there could be progress on A-ID that would not requireAGI, then this could be a major contribution and would be a worthy focus for the fieldof AI.
Acknowledgments Robert de Neufville, Roman Yampolskiy, Stuart Armstrong, Daniel Filan, Gorm
Shackelford, Machiel Keestra, Mahendra Prasad, Josh Cowls, and two anonymous reviewers provided helpful
comments on an earlier version of this paper. Any remaining errors are the author ’s alone.
References
Altmann, J., & Sauer, F. (2017). Autonomous weapon systems and strategic stability. Survival, 59 (5), 117 –
142.
Armstrong, S., Sandberg, A., & Bostrom, N. (2012). Thinking inside the box: Controlling and using an oracle
AI.Minds and Machines, 22 (4), 299 –324.
Augenstein, I., Das, M., Riedel, S., Vikraman, L., & McCallum, A. (2017). Semeval 2017 task 10: Scienceie-
Extracting keyphrases and relations from scientific publications. Proceedings of the InternationalWorkshop on Semantic Evaluation (SemEval at ACL 2017). https://arxiv.org/abs/1704.02853 .
Baum, S. D. (2017a). On the promotion of safe and socially beneficial artificial intelligence. AI & SOCIETY,
32(4), 543 –551.
Baum, S.D. (2017b). A survey of artificial general intelligence projects for ethics, risk, and policy. Global
Catastrophic Risk Institute working paper 17-1.
Baum, S. D. (2018). Reconciliation between factions focused on near-term and long-term artificial intelli-
gence. AI & SOCIETY, 33 (4), 565 –572.
Baum, S. D. (2020). Medium-term artificial intelligence and society. Information, 11 (6), 290. https://doi.
org/10.3390/info11060290 .
Baum, S. D., Goertzel, B., & Goertzel, T. G. (2011). How long until human-level AI? Results from an expert
assessment. Technological Forecasting and Social Change, 78 (1), 185 –195.
Bernstein, J. H. (2015). Transdisciplinarity: a review of its origins, development, and current issues. Journal of
Research Practice, 11 (1), article R1.
Boden, M. A. (2009). Computer models of creativity. AI Magazine, 30 (3), 23 –34.
Bracken, L. J., & Oughton, E. A. (2006). ‘What do you mean? ’The importance of language in developing
interdisciplinary research. Transactions of the Institute of British Geographers, 31 (3), 371 –382.
Bromham, L., Dinnage, R., & Hua, X. (2016). Interdisciplinary research has consistently lower funding
success. Nature, 534 , 684 –687.
Cave, S., & Ó hÉigeartaigh, S. S. (2019). Bridging near- and long-term concerns about AI. Nature Machine
Learning, 1 (1), 5 –6.
Charlin, L., & Zemel, R. S. (2013). The Toronto Paper Matching System: An automated paper-reviewer
assignment system. International conference on machine learning (ICML) 2013, Workshop on PeerReviewing and Publishing Models.
Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4 (1), 55 –81.
Committee on Facilitating Interdisciplinary Research and Committee on Science, Engineering, and Public
Policy. (2005). Facilitating interdisciplinary research . Washington, D.C.: National Academies Press.
Crease, R. P. (2017). Physical sciences. In R. Frodeman (Ed.), The Oxford handbook of interdisciplinarity
(Second ed., pp. 71 –87). Oxford: Oxford University Press.
Frodeman, R. (Ed.). (2017). The Oxford handbook of interdisciplinarity (Second ed.). Oxford: Oxford
University Press.
Grace, K., Salvatier, J., Dafoe, A., Zhang, B., & Evans, O. (2018). When will AI exceed human performance?
Evidence from AI experts. Journal of Artificial Intelligence Research, 62 ,7 2 9 –754.
Hawkins, J. (2017). What intelligent machines need to learn from the neocortex. IEEE Spectrum, 2 June .
https://spectrum.ieee.org/computing/software/w hat-intelligent-machines-need-to-learn-from-the-
neocortex .
Hoffmann, M. H., Schmidt, J. C., & Nersessian, N. J. (2013). Philosophy of and as interdisciplinarity.
Synthese, 190 (11), 1857 –1864.Artificial Interdisciplinarity: Artificial Intelligence for...
Holbrook, J. B. (2017). Peer review, interdisciplinarity, and serendipity. In R. Frodeman (Ed.), The Oxford
handbook of interdisciplinarity (Second ed., pp. 485 –497). Oxford: Oxford University Press.
Hukkinen, J. I. (2017). Peer review has its shortcomings, but AI is a risky fix. Wired , 30 January, https://www.
wired.com/2017/01/peer-review-shortcomings-ai-risky-fix .
J a c o b s ,J .A .( 2 0 1 3 ) . In defense of disciplines: Interdisciplinarity and specialization in the research university .
Chicago: University of Chicago Press.
Keestra, M. (2017). Metacognition and reflection by interdisciplinary experts: Insights from cognitive science
and philosophy. Issues in Interdisciplinary Studies, 35 ,1 2 1 –169.
Klein, J. T. (2017). Typologies of interdisciplinarity: the boundary work of definition. In R. Frodeman (Ed.),
The Oxford handbook of interdisciplinarity (Second ed., pp. 21 –34). Oxford: Oxford University Press.
Krohn, W. (2017). Interdisciplinary cases and disciplinary knowledge: epistemic challenges of interdisciplin-
ary research. In R. Frodeman (Ed.), The Oxford handbook of interdisciplinarity (Second ed., pp. 40 –52).
Oxford: Oxford University Press.
Lample, G., Ott, M., Conneau, A., Denoyer, L., & Ranzato, M. A. (2018). Phrase-based & neural unsuper-
vised machine translation. https://arxiv.org/abs/1804.07755
Laudel, G. (2006). Conclave in the tower of Babel: how peers review interdisciplinary research proposals.
Research Evaluation, 15 (1), 57 –68.
Lawrence, R. J. (2015). Advances in transdisciplinarity: epistemologies, methodologies and processes.
Futures, 65 (2015), 1 –9.
Leeuwenberg, A., Vela, M., Dehdari, J., & van Genabith, J. (2016). A minimally supervised approach for
synonym extraction with word embeddings. Prague Bulletin of Mathematical Linguistics, 105 , 111 –142.
Legg, S., & Hutter, M. (2007). Universal intelligence: a definition of machine intelligence. Minds and
Machines, 17 (4), 391 –444.
MacLeod, M. (2018). What makes interdisciplinarity difficult? Some consequences of domain specificity in
interdisciplinary practice. Synthese, 195 (2), 697 –720.
Marcus, G. (2018). Deep learning: A critical appraisal. https://arxiv.org/abs/1801.00631
McCorduck, P. (2004). Machines who think: 25th (Anniversary ed.). Natick: AK Peters.
McGregor, S. L. T. (2014). Introduction to special issue on transdisciplinarity. World Futures, 70 (3–4), 161 –
163.
Menken, S., & Keestra, M. (Eds.). (2016). An introduction to interdisciplinary research: theory and practice .
Amsterdam: Amsterdam University Press.
Minx, J. C., Callaghan, M., Lamb, W. F., Garard, J., & Edenhofer, O. (2017). Learning about climate change
solutions in the IPCC and beyond. Environmental Science & Policy, 77 , 252 –259.
Mohammed, N. (2020). Extracting word synonyms from text using neural approaches. International Arab
Journal of Information Technology, 17 (1), 45 –51.
Newell, W. H. (2001). A theory of interdisciplinary studies. Issues in Integrative Studies, 19 ,1–25.
Nunez-Mir, G. C., Iannone, B. V., Pijanowski, B. C., Kong, N., & Fei, S. (2016). Automated content analysis:
addressing the big literature challenge in ecology and evolution. Methods in Ecology and Evolution,
7(11), 1262 –1272.
Nunez-Mir, G. C., Desprez, J. M., Iannone III, B. V., Clark, T. L., & Fei, S. (2017). An automated content
analysis of forestry research: are socioecological challenges being addressed? Journal of Forestry, 115 (1),
1–9.
Pan, S. J., & Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on Knowledge and Data
Engineering, 22 (10), 1345 –1359.
Parson, E., Re, R., Solow-Niederman, A., & Zeide, E. (2019a). Artificial intelligence in strategic context: an
introduction. AI Pulse , 8 February, https://aipulse.org/artificial-intelligence-in-strategic-context-an-
introduction .
Parson, E., Fyshe, A., Lizotte, D. (2019b). Artificial intelligence ’s societal impacts, governance, and ethics:
Introduction to the 2019 Summer Institute on AI and Society and its rapid outputs. AI Pulse ,2 6
September, https://aipulse.org/artificia l-intelligences-societal-impac ts-governance- and-ethics-
introduction-to-the-2019-summer-institute-on-ai-and-society-and-its-rapid-outputs .
Pautasso, M., & Pautasso, C. (2010). Peer reviewing interdisciplinary papers. European Review, 18 (2), 227 –
237.
Perkel, J. (2017). Omnity opens multilingual semantic searches up to academia. Nature Jobs , 12 January,
http://blogs.nature.com/naturejobs/2017/01/12/omnity-opens-multilingual-semantic-searches-up-to-academia .
Perkins, D. N., & Salomon, G. (1992). Transfer of learning. International Encyclopedia of Education (pp.
6452 –6457). Oxford: Pergamon Press.S. D. Baum
Prunkl, C., & Whittlestone, J. (2020). Beyond near- and long-term: Towards a clearer account of research
priorities in AI ethics and society. In Proceedings of the Third AAAI / ACM Annual Conference on AI,Ethics, and Society, New York.
Ranzato, M., Lample, G., Ott M. (2018). Unsupervised machine translation: a novel approach to provide fast,
accurate translations for more languages. Facebook Code, 31 August ,https://code.fb.com/ai-
research/unsupervised-machine-translation-a-novel-approach-to-provide-fast-accurate-translations-for-more-languages .
Scholz, R. W., & Steiner, G. (2015). Transdisciplinarity at the crossroads. Sustainability Science, 10 (4), 521 –
526.
Shackelford, G. E., Kemp, L., Rhodes, C., Sundaram, L., ÓhÉigeartaigh, S. S., Beard, S., Belfield, H.,
Weitzdörfer, J., Avin, S., Sørebø, D., Jones, E. M., Hume, J. B., Price, D., Pyle, D., Hurt, D., Stone, T.,Watkins, H., Collas, L., Cade, B. C., Johnson, T. F., Freitas-Groff, Z., Denkenberger, D., Levot, M., &Sutherland, W. J. (2020). Accumulating evidence using crowdsourcing and machine learning: A livingbibliography about existential risk and global catastrophic risk. Futures, 116 , 102508. https://doi.
org/10.1016/j.futures.2019.102508 .
Simon, H. A., & Hayes, J. R. (1976). The understanding process: problem isomorphs. Cognitive Psychology,
8(2), 165 –190.
Stockton, N. (2017). If AI can fix peer review in science, AI can do anything. Wired , 21 February.
https://www.wired.com/2017/02/ai-can-solve-peer-review-ai-can-solve-anything .
Sutherland, W. J., & Wordley, C. F. (2018). A fresh approach to evidence synthesis. Nature, 558 ,3 6 4 –366.
Tuhkala, A., Kärkkäinen, T., & Nieminen, P. (2018). Semi-automatic literature mapping of participatory
design studies 2006-2016. In proceedings of Participatory Design Conference (PDC ’18), DOI https://doi.
org/10.1145/3210604.3210621 .
Victor, D. (2015). Climate change: embed the social sciences in climate policy. Nature, 520 (7545), 27 –29.
Wilson, E. O. (1998). Consilience: The unity of knowledge . New York: Knopf.
Yampolskiy, R. V. (2012). Leakproofing singularity: Artificial intelligence confinement problem. Journal of
Consciousness Studies, 19 (1–2), 194 –214.
Yampolskiy, R. V. (2013). Turing test as a defining feature of AI-completeness. In X.-S. Yang (Ed.), Artificial
intelligence, evolutionary computing and metaheuristics (pp. 3 –17). Berlin: Springer.
Zamir, A. R., Sax, A., Shen, W., Guibas, L. J., Malik, J., & Savarese, S. (2018). Taskonomy: Disentangling
task transfer learning. In Proceedings of the IEEE Conference on Computer Vision and PatternRecognition (pp. 3712-3722).
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