OpenAI was founded to “advance digital intelligence in the way that is most likely to benefit humanity as a whole” and “enact a safe path” to AI (Brockman & Sutskever, 2015, section 1). This mission statement seems to implicitly assume that openness is the optimal model of innovation considering its mission. However, the company’s open research strategy has been criticized (Metz, 2016). To shed further light onto this criticism, this paper is concerned with answering the following research question: What influence can the openness OpenAI’s research have on the company’s ability to “advance digital intelligence in the way that is most likely to benefit humanity”?

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Open innovation and AI: Can OpenAI benefit humanity?
Kasper Groes Ludvigsen
klud@itu.dk
Re-assembling innovation
KBREINN1KU-Autumn 2017
Exam essay
ITU

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Science and technology have long been identified as major sources of economic and social
development (Schumpeter 1939; Kondratiev 1978), however, their contribution to social well-being is
no longer taken for granted (Pellizzoni 2012). This seems to be particularly true for research in the field
of artificial intelligence (AI). The topic of AI is widely discussed with actors in the domain arguing
publicly about the implications of AI on our society (Kasriel, 2017) and mainstream media frequently
report on the progress of AI (e.g. The New York Times overview of AI related articles (The New York
Times, 2017)). While it is widely accepted that AI hold great benefits for the capabilities of the human
race (Bostrom, 2017), prominent research and industry figures also warn against the potential dangers
of AI (Price, 2017; David, 2017), some even calling the potential invention of artificial super
intelligence (ASI) an existential risk to humanity (Sulleyman, 2017). ASI is an AI much smarter than
the best human brains in practically every field, including scientific creativity, general wisdom and
social skills (Bostrom, 2006). AI experts generally believe that ASI will be achieved before 2075
(Müller & Bostrom, 2014). Amodei et al (2016) argue that a multitude of technical problems exist in
relation to prevention of accidents in AI systems. An accident may be a situation in which a human
designer intended the system to perform a certain task or achieve a certain objective (perhaps
informally specified), but the system produced harmful or unexpected results. This is a common issue
in all engineering disciplines, but it may be particularly important to address when building AI systems
(Steinhardt, 2015). One of the main challenges in AI development is the value alignment problem,
which is the challenge inherent in building intelligence which is provably aligned with human values,
and it is a problem that must be addressed even for relatively unintelligent AI systems (Russel, n.d). As
such, this challenge becomes more and more pressing as we approach ASI. However, human values are
obviously difficult to define. In response to concerns about the development of AI, a non-profit, open
source AI research company called OpenAI was founded to “advance digital intelligence in the way
that is most likely to benefit humanity as a whole” and “enact a safe path” to AI (Brockman &
Sutskever, 2015, section 1). This mission statement seems to implicitly assume that openness is the
optimal model of innovation considering its mission. However, the company’s open research strategy
has been criticized (Metz, 2016). To shed further light onto this criticism, this paper is concerned with
answering the following research question:

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What influence can the openness OpenAI’s research have on the company’s ability to “advance
digital intelligence in the way that is most likely to benefit humanity”?
Answering this question is an important exercise in making sense of one of the most notable actors
within AI research - a technology which, as shown above, could potentially have far reaching
consequences. The remainder of the paper is structured as follows: First, I review literature relevant to
the research questions. Then, I explain the methodology of the paper and introduce the reader more
thoroughly to the case company OpenAI. After, I analyze the case with point of departure in the
reviewed literature and lastly I discuss the findings, bringing in other theoretical perspectives and
conclude on the findings.
Literature review
In the following, I review literature relevant to answering the research question.
Open Innovation
Innovation was once conceived as being the work of a lone entrepreneur bringing innovations to
markets, but new models of innovation acknowledge that innovation processes are interactive and that
innovators rely on interaction with users, suppliers and with a range of institutions in the innovation
system (Laursen & Salter, 2006). In this conception of innovation, actors do not innovate alone. Rather,
they are nested in communities of practice and embedded in a dense network of interactions (Scott and
Brown, 1999; Brown and Duguid, 2000). Open innovation is one such type of innovation, and it refers
to "the use of purposive inflows and outflows of knowledge to accelerate internal innovation and
expand the markets for external use of innovation" (Chesbrough 2006, p. 1). Open innovation in the
form of open search for new ideas is linked to innovative performance, and theory suggests that firms
that are too internally focused will miss opportunities (Laursen & Salter, 2006; Chesbrough, 2006).
“Openess” is also a phenomenon in software development where it refers to “the practice of releasing
into the public domain (continuously and as promptly as is practicable) all relevant source code and
platforms and publishing freely about algorithms and scientific insights and ideas gained in the course

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of the research.” (Bostrom, 2017, p. 1). It is worth noting here that, in Bostrom’s (ibid) conception of
“openess”, it is not a binary variable, as it can take on many forms.
Power and subjugated knowledges
As the proceeding sections will show, analysis and discussion of OpenAI’s activities can be situated in
the discourse on subjugated knowledges, and this theoretical perspective plays a crucial role in the
reassembling of OpenAI as an innovation. The topic of subjugated knowledges is therefore briefly
introduced here.
Through a struggle over time, global unitary knowledges have subjugated a wide range of knowledges
and disqualified them as ‘‘beneath the required level of cognition or scientificity’’ (Foucault, 1980, p.
82). Global unitary knowledges are the privileging of the methods of science, and these have led to the
subjugation of previously established erudite knowledge and of knowledge located at the margins of
society. These subjugated knowledges have been excluded from the ‘‘legitimate domains of formal
knowledge’’ (White & Epston, 1990, p. 26).
According to Hartman (2000), Foucault was concerned with how this knowledge was exercise of
power and practice of knowledge at the local level. Foucault (1980, p. 52) asserts that “it is not possible
for power to be exercised without knowledge, it is impossible for knowledge not to engender power”.
Thus, in a Foucauldian perspective, the established regimes can be deemed to be self-sustaining
because they enter into a virtuous cycle where the knowledge they produce legitimates their power, and
their power legitimates their knowledge. At its logical conclusion, this relation between knowledge and
power sustains the subjugation of knowledge and makes it difficult for knowledge outside of the
established regimes to surface and become legitimate. The practical application of a unitary body of
knowledge is exemplified by Hartman (2008, p. 20)
"For example, that powerful global and unitary body of knowledge, the
Diagnostic and Statistical Manual of Mental Disorders, Third Edition
(American Psychiatric Association, 1980), which is centrally established and
encoded in economic, medical, and educational systems, is practiced at the

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most local level--in the relationship between a social worker and a client. When
a social worker is required by an agency’s funding needs or by the rules of
third-party payers to attach a diagnostic label to a client, a powerful and
privileged classification system has entered this relationship and in all
likelihood has affected the worker’s thinking, the relationship, and the client’s
self-definition.”
Methodology
This paper is the result of qualitative inquiry in which I applied document and content analysis to
documentary secondary data (Saunders, Lewis & Thornhill, 2009). In analysing this data, a deductive
approach was applied in which the theoretical framework used in the analysis was framed a priori. I
used open innovation theory to analyze the extent to which OpenAI’s innovation processes are open
and to create a basis for understanding to what extent the company’s openness enables or hampers its
mission. I then introduce into the analysis Foucault’s notion of subjugated knowledges, which allowed
me to problematize the extent to which OpenAI’s innovation process is open. In the case of OpenAI,
these theories are closely interlinked as they allow us to make sense in different but complementing
ways of an observed phenomenon in the case of OpenAI. The analyzed documents are online news
articles, OpenAI’s mission statement, its first blog post in which the background for founding the
company is described and summaries of the company’s 39 research papers. The latter were used in a
content analysis which was guided by Kassarjian’s (1977) framework and served to identify how many
of the articles fall into three categories crated for the purpose of this analysis: “AI safety”, “the
solicitation of public opinion on AI development“ or “efforts to identify global human values”. The
first category is relevant given the company’s goal to “enact a safe path” to AI. The second and third
are relevant because they allow me to assess the extent to which the company make use of purposive
inflows of knowledge from the broader public and efforts made by the company to identify what
human values are. This allowed me to assess the extent to which OpenAI’s innovation process and
research activities helps accomplish its mission. I drew from Kassarjian’s (1977) framework because it
allows for the integration of existing categories into his framework; thus it allowed me to integrate the
three content categories mentioned above.

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In order to ensure reliability and validity of the data, only data from authoritative news outlets and
OpenAI’s own website was used. Dochartaigh’s (2002) recommendations for assessment of the
authority of documents available via the internet was used as a guide for the selection authoritative
sources.
Using a deductive approach has certain strengths, including linking the research into an existing body
of knowledge and provide an initial analytical framework (Saunders, Lewis & Thornhill, 2009). In
using a deductive approach, there is a risk of introducing a premature closure in the investigated issues
(ibid.) My awareness of this issue guided my analysis so as to avoid it. The pros of the chosen
methodology is that secondary data sources are likely to be of higher quality than what could be
collected by oneself (Stewart & Kamins 1993). Also, the data collection method offered me access to
insights into an organization that would otherwise be off-limits. In addition, the data I used is
permanent and publicly available allowing others to access it easily thereby opening up my conclusions
to public scrutiny (Saunders, Lewis & Thornhill, 2009). The limitations of the methodology is that
other insights would likely have occurred if other methods for data collection such as interviews had
been used. The use of secondary data sources also means that control over data quality is lost
(Saunders, Lewis & Thornhill, 2009).
The case
With more than 1 billion US dollars in funding (Brockman & Sutskever, 2015) and backing from
prominent figures in the technology and research sectors, OpenAI is a resourceful actor which can
potentially yield great progress in AI development. Given the potential of OpenAI’s research activities,
it is important to assess how the openness of the company’s research can affect its ability to achieve its
mission. Consequently of the research question, the most relevant aspects of OpenAI to look into is the
background for founding the company, the extent to which the company’s innovation processes are
open, the extent to which the company allows for knowledge to flow in and out of the company and the
nature of the company’s research activities. According to the company’s “Launch blog post”
(Brockman & Sutskever, 2015), the unpredictability of AI development, and the profound impacts it
can have on humanity, creates a need for “a leading research institution which can prioritize a good

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outcome for all over its own self-interest” and the founders are hoping OpenAI will become that
institution (ibid, paragraph 7). With this in mind, the company strives “to build value for everyone”, but
according to one of the co-founders, the exact goal of the company is “a little vague” (Friend, 2016).
The company encourages its researchers to publish all their work and any patented technology will be
publicly available, but one of the co-chairs has also stated publicly that the company will not release all
its source code (ibid). OpenAI will “collaborate with others across many institutions” and expect to
work with other companies, and a number of its research papers have been authored in collaboration
with other actors external to the organization. Here, I particularly notice that OpenAI does not mention
collaborations or consultations with the broader public. Indeed, the company is “planning a way to
allow wide swaths of the world to elect representatives to a new governance board” (Friend, 2016, ),
but judging from a lack of empirical evidence of the contrary, the governance board has yet to
materialize. As mentioned, I analyzed the 39 research papers published by OpenAI in order to
determine the number of papers related to AI safety, the solicitation of public opinion on AI
development or efforts to identify global human values. The results of this analysis are seen below:
Table 1: Number of published articles related to AI safety, solicitation of public opinion
and identifying human values
AI safety Solicitation of public
opinion
Identifying global
human values
Number of articles 3 0 0
Percentage of total
number of published
articles
7.69 % 0 % 0 %

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Analysis
The following analysis shows how an innovation process can be dismantled and reassembled using
various innovation-related theories. In particular, it shows how an empirical observation, i.e. the lack of
public consultation and purposive inflow of knowledge, can be analyzed using disparate theories and
how these analyses can yield different but complimentary understandings of the observed phenomenon
and its implications. In this section, I apply the theoretical lenses of open innovation and subjugated
knowledges to the case of OpenAI. To reiterate, open innovation is “the use of purposive inflows and
outflows of knowledge to accelerate internal innovation and expand the markets for external use of
innovation" (Chesbrough 2006, p. 1). At first sight, it seems natural to classify OpenAI as an instance
of open innovation, but upon closer inspection, this might not be the case. As is clear from the case
description above, OpenAI definitely has purposive outflow of knowledge as exemplified the
encouragement of researchers to publish findings and the release of source code. The company also has
some inflow of knowledge, exemplified by active collaboration with other actors within the research
and industry domain. However, OpenAI does not actively solicit the public opinion on issues related to
the development of AI. Although the company plans to involve the broader society, these plans have
yet to materialize. It therefore seems fair to conclude that, despite the apparent openness of the
company’s innovation process, the process lacks inflow of knowledge to some extent, and it is
therefore not open enough to be characterized as open innovation. This raises a theoretical question: Is
the applied definition of open innovation too rigid? After all, OpenAI’s innovation process is quite
open. If we fail to acknowledge OpenAI as an instance of open innovation, our research of open
innovation cases, hence our understanding and knowledge of open innovation, will be limited. The
failure of the applied definition to encompass OpenAI is therefore not trivial. One could argue that, just
like openness in software development is not a binary variable, neither should open innovation be. I
therefore introduce a distinction between directed and unidirected open innovation. Acknowledging the
importance of networks in innovation, the terms are inspired by Newman’s (2003) characterisation of
network edges as being either directed, meaning that information runs in only one direction, or being
unidirected meaning that information can flow in multiple directions. It must be noted that directed and
unidirected open innovation should be considered two ends of a continuum rather than a binary
variable. Using this typology, OpenAI is mostly an instance of directed open innovation due to the

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large extent to which the company makes use of a purposive outflow of knowledge and the somewhat
limited extent to which inflow of knowledge is used.
The assessment of the impact of OpenAI’s openness on its ability to accomplish its mission is complex.
In the short term, the outflow of knowledge from OpenAI will likely yield positive outcomes, but this
may not be the case in the long term. The desirability of the long term consequences of openness
depends on whether the objective is to benefit current or future generations. Openness about safety
measures and goals are likely to be positive on both counts. However, other forms of openness, for
instance regarding source code, science and possibly capability could increase competition around the
time of the introduction of advanced AI, which could increase the probability that “winning the AI
race” is incompatible with applying safety measures which slow down the development process or
imposes constraints on the performance of the AI (Bostrom, 2017). As such, it seems the very open
nature of OpenAI’s knowledge outflow may be problematic in the longer term.
In the short and long term, an issue also arises due to the lack of a purposive inflow of knowledge,
particularly in relation to its lack of inflow of knowledge in the form of public consultation. This is
because open search in open innovation processes is correlated with better innovative performance.
This means that not only would the purposive inflow of knowledge in the form of public consultation
make it more likely that OpenAI achieves its goal of benefitting humanity, it would also make the
company achieve this goal more quickly in the sense that its innovative performance would increase. A
practical recommendation arising from this analysis is therefore that OpenAI should purposefully use
the inflow of knowledge of the public in the development of AI.
On the subjugation of knowledge and the nature of OpenAI’s research activites
The apparent failure of OpenAI to have a purposive inflow of knowledge from all societal actors and to
actively solicit public opinion on innovation efforts can also be understood from the perspective of
Foucault’s subjugated knowledges. OpenAI clearly attempts to justify its research by reference to
benefits it will have for humanity. However, applying Fourcault’s (1980) perspective on subjugated
knowledge in the analysis of OpenAI allows us to understand that the position assumed by OpenAI
subjugates knowledge of the public. As such, OpenAI may fail to produce societally beneficial research

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because the company fails to consult the general public, and instead base its research on what its
employees deem to be the safe path to AGI. This places OpenAI among the centralized, political,
economic and institutional regimes, and like Foucault, one could be concerned with how the regimes
exercise power as they are practiced at the local level. Applying this Foucauldian perspective to the
innovation case of OpenAI, the fact that knowledge produced by OpenAI is privileged is not only
problematic because it subjugates other knowledges. The real problem may arise in the way this
knowledge is practiced at the local level, i.e. how the knowledge affects the lives of those whose
knowledge is subjugated. One could imagine a situation in which knowledge produced by OpenAI aids
the development of AI which ends up being used in ways that further subjugates knowledge or which is
not aligned with the worldviews of actors at the local level. If OpenAI achieves its goal of becoming
the dominant research institution within the field of AI, it is of particular importance that public
opinion is solicited and that the subjugation of knowledge is prevented in this process.
Returning to Hartman’s (2008) powerful example of the powerful unitary body of knowledge, the
Diagnostic and Statistical Manual of Mental Disorders, an analogy to AI development can be made.
Instead of a human doctor applying the manual, it could be applied by an AGI and practiced at the most
local level, namely in the relationship between a social worker and a client. If the worker’s thinking,
the relationship and the client’s self-definition is affected by the application of the manual by a social
worker, these will all be radically changed once an AGI enters the relationship. The social worker
might not be needed, the relationship will be between a human and a machine and the client’s self-
definition will be affected by the functioning of a machine. For this reason, it is highly important that
knowledges are not subjugated in the discourse on the desirable development of AI.
If knowledges remain subjugated in OpenAI’s innovation processes, it seems unlikely that the company
will accomplish its mission of creating AI for the benefit of humanity, because if you do not know what
humanity thinks of AI, it will be impossible to build something that humanity approves of. OpenAI’s
apparent subjugation of knowledge plausibly also means that the company will be unable to sufficiently
tackle the value alignment problem. As mentioned, the value alignment problem is the challenge
inherent in building intelligence which is provably aligned with human values. Again, with no efforts
in identifying human values, the company will arguably not be able to design AI which aligns with
human values. Thus, the lack of inflow of knowledge, and particularly the subjugation of knowledge

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will reduce OpenAI’s ability to accomplish its mission. As stated in the introduction, the value
alignment problem must be addressed even for relatively unintelligent AI systems, so from this point of
view, the fact that OpenAI is not engaging in the identification of human values or the resolution of the
value alignment problem through its research makes matters worse. It could also be argued that for a
company with the goal of “enacting the safe path to AGI”, more than 7.69 % of published articles
should be about AI safety.
Discussion
As stated in the analysis, OpenAI’s lack of inflow of knowledge from the public could hamper its
mission. In the following, I will discuss various counter arguments and the challenges OpenAI will face
if it implements the plan of including the public in its innovation processes.
OpenAI’s plan to include the public
One could argue that OpenAI’s plans to include the public should to some extent alleviate the company
of the criticism presented above. Accepting such an argument assumes that a company will do what it
says it will do, and such an argument could be countered with reference to organizational hypocrisy
(Brunsson, 2003). The mere fact that the company “plans” to include the broader public may not be
more than an instance of organizational hypocrisy - an act of communication undertaken by a company
as a means of postponing the implementation of a decision taken to satisfy certain stakeholders.
Critique of deliberative innovation processes
Critics of deliberative democracy assert that collective crafting is constituted through acts of power
such as control and exclusion (Mouffe 1999). This critique not only applies to deliberative democracy
at the state level, but also to the application of deliberate processes in innovation (Oudheusden, 2014).
Thus, we cannot simply consider a deliberate innovation processes an inclusive “weighing of interests”
(ibid, 73), as any attempt by OpenAI to include subjugated knowledges in its innovation processes
would be subject to battles for power and the right to be heard. This theoretical disposition points to the
practical difficulties OpenAI would have in facilitating broad inclusion of subjugated knowledges in its

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innovation processes. Unfortunately, empirical evidence of how deliberation is accomplished is sparse
(ibid.) Thus, future research should address questions pertaining to the distribution of power such as
“What actors are relevant to include and which are not?” Another challenge OpenAI would face would
be to make the outcomes of deliberate processes count in policy and science arenas (Oudheusden,
2014). OpenAI is indeed an important actor in the field of AI development, but it is not the only actor,
and it is therefore important for the company to learn to wield political influence and strategies
(Wesselink & Hoppe, 2011)
The desirability of democratic inclusion
Through the theoretical lenses applied in the analysis, OpenAI’s lack of inflow of knowledge and the
company’s apparent subjugation of knowledge is criticized on the grounds that it will hamper its ability
to accomplish its mission. It also implicitly assumes that expertise is a negotiated attribute (Nahuis and
Van Lente 2008), meaning that expertise is not considered the prerogative of scientists or other
formally recognized experts, but of publics more broadly. It is generally recognized that the inclusion
of the public in deliberation often leads to tensions between formally recognized experts and laypersons
over what constitutes evidence and who is entitled to speak why, when and on behalf of whom or what
(Oudheusden, 2014). This points to a challenge that OpenAI would have to learn to manage if it were
to fulfill its plan of establishing a governance board to engage in deliberation on the development of
AI. This challenges also ties back to the notions of power, control and exclusion in deliberate
innovation processes mentioned above.
Due to its openness, non-profit status and its mission of doing research free of economic obligations,
OpenAI can be viewed as a rebellion against market-based innovation much like innovation commons
(Allen & Potts, 2016; Brian, 2015). In addition, OpenAI’s plan of allowing “wide swaths of the world
to elect representatives to a new governance board” seems akin to the concept of democracy. OpenAI’s
innovation process and plan to include the broader public in its endeavors can therefore be criticized
from the perspective of Hayek (1960) who is a notable proponent of the notion that democracy is not as
important as the conservation of the free market, because the free market will create or sustain the
liberty of the individual, whereas democracy tends to diminish it (Hayek, 1960). He argues that

13. 13
government should only be guided by majority opinion if the opinion is independent of government,
but that opinion in many instances it is not. Majority decisions, he posits, show us what people want at
the moment, but not what would be in their interest if they were better informed and unless their
opinion could be changed by persuasion, they are of no value. From this perspective, OpenAI can be
criticized as opposing the free market and potentially limiting the liberty of the individual, and Hayek’s
critique of democracy points to the necessity of a well-informed discussion when including the broader
publics in deliberate innovation processes. The extent to which Hayek’s criticism of democracy can be
applied to the case of OpenAI provides an interesting avenue for further inquiry. Speaking of OpenAI’s
apparent rebellion against market forces entering into research, I also welcome a broad discussion on
the extent to which OpenAI is in fact solving the right problem. As proposed by Allworth (2015),
attention should be directed at solving the problem that market interests hinder the development of
technology which benefits humanity.
Conclusion
This paper set out to investigate how the openness of OpenAI’s innovation activities can affect the
company’s ability to achieve its mission which is to “advance digital intelligence in the way that is
most likely to benefit humanity”. An important empirical observation were made in the course of
analyzing OpenAI, namely that the company does not include the broader public in its innovation
processes. In addition, a mere 7.69 % of the company’s research papers are on AI safety which seems
low given the company’s mission of enacting the safe path to AGI, and none of its research activities
strive to identify global human values or solicit the public’s opinion. This is a lack of purposive inflow
of knowledge and OpenAI’s innovation processes can therefore not be deemed open innovation. Thus,
the company’s innovation performance will likely be hampered. To allow for a more flexible
perspective on what constitutes open innovation, I propose using the term directed open innovation to
describe instances of open innovation in which there is only either inflow or outflow of knowledge, and
the term unidirected open innovation to describe cases with both inflow and outflow of knowledge.
This distinction will enable the investigation of a broader range of companies such as OpenAI under
the open innovation framework, thus improving our understanding of what openness in innovation is. I
therefore consider this to be one of the main contributions of this paper. Using this distinction, which is

14. 14
a continuum rather than a binary variable, OpenAI is mostly an instance of directed open innovation.
The lack of public consultation also constitutes subjugation of knowledges, which allows us to
understand that knowledge produced by OpenAI is unitary and privileged. Therefore, when OpenAI’s
knowledge is practiced at the local level, problems may arise. One could imagine a situation in which
knowledge produced by OpenAI aids the development of AI which ends up being used in ways which
further subjugates knowledge or which is not aligned with the worldviews of actors at the local level.
Due to OpenAI’s subjugation of knowledge and the lack of inflow of knowledge into the organization,
the AI developed by OpenAI may be misaligned with human values. Thus the company will fail to
tackle an important issue in AI development.
In sum, from the perspectives of the applied theories, OpenAI’s failure to include the public in its
innovation processes reduces the company’s ability to achieve its goal, because it subjugates
knowledges thus failing to identify human values and align with those, and because the lack of a
purposive inflow of knowledge in the form of soliciting public opinion could hamper its innovative
performance.

15. 15
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