Governance of artificial intelligence

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Governance of artificial intelligence
Araz Taeihagh
To cite this article: Araz Taeihagh (2021): Governance of artificial intelligence, Policy and Society,
DOI: 10.1080/14494035.2021.1928377
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Governance of artificial intelligence
Araz Taeihagh
Policy Systems Group, Lee Kuan Yew School of Public Policy, National University of Singapore, Singapore
ABSTRACT
The rapid developments in Artificial Intelligence (AI) and the inten
sification in the adoption of AI in domains such as autonomous
vehicles, lethal weapon systems, robotics and alike pose serious
challenges to governments as they must manage the scale and
speed of socio-technical transitions occurring. While there is con
siderable literature emerging on various aspects of AI, governance
of AI is a significantly underdeveloped area. The new applications of
AI offer opportunities for increasing economic efficiency and quality
of life, but they also generate unexpected and unintended conse
quences and pose new forms of risks that need to be addressed. To
enhance the benefits from AI while minimising the adverse risks,
governments worldwide need to understand better the scope and
depth of the risks posed and develop regulatory and governance
processes and structures to address these challenges. This intro
ductory article unpacks AI and describes why the Governance of AI
should be gaining far more attention given the myriad of chal
lenges it presents. It then summarises the special issue articles
and highlights their key contributions. This special issue introduces
the multifaceted challenges of governance of AI, including emer
ging governance approaches to AI, policy capacity building, explor
ing legal and regulatory challenges of AI and Robotics, and
outstanding issues and gaps that need attention. The special issue
showcases the state-of-the-art in the governance of AI, aiming to
enable researchers and practitioners to appreciate the challenges
and complexities of AI governance and highlight future avenues for
exploration.
KEYWORDS
Governance; artificial
intelligence; AI; robotics;
public policy
1. Introduction
Artificial intelligence (AI) is rapidly changing how transactions and social interactions
are organised in society today. AI systems and the algorithms supporting their operations
play an increasingly important role in making value-laden decisions for society, ranging
from clinical decision support systems that make medical diagnoses, policing systems
that predict the likelihood of criminal activities and filtering algorithms that categorise
and provide personalised content for users (Helbing, 2019; Mittelstadt, Allo, Taddeo,
Wachter, & Floridi, 2016). The ability to mimic or rival human intelligence in complex
problem-solving sets AI apart from other technologies, as many cognitive tasks
CONTACT Araz Taeihagh spparaz@nus.edu.sg; araz.taeihagh@new.oxon.org Lee Kuan Yew School of Public
Policy, National University of Singapore, 469B Bukit Timah Road, Li Ka Shing Building, Level 2, #02-10 259771 Singapore
POLICY AND SOCIETY
https://doi.org/10.1080/14494035.2021.1928377
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://
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traditionally performed by humans can be replaced and outperformed by machines
(Bathaee, 2018; Osoba & Welser, 2017; Sætra, 2020).
While the technology can yield positive impacts for humanity, AI applications can
also generate unexpected and unintended consequences and pose new forms of risks
that need to be effectively managed by governments. As AI systems learn from data in
addition to programmed rules, unanticipated situations that the system has not been
trained to handle and uncertainties in human-machine interactions can lead AI
systems to display unexpected behaviours that pose safety hazards for its users (He
et al., 2019; Helbing, 2019; Knudson & Tumer, 2011; Lim & Taeihagh, 2019). In many
AI systems, biases in the data and algorithm have been shown to yield discriminatory
and unethical outcomes for different individuals in various domains, such as credit
scoring and criminal sentencing (Huq, 2019; Kleinberg, Ludwig, Mullainathan, &
Sunstein, 2018). The autonomous nature of AI systems presents issues around the
potential loss of human autonomy and control over decision-making, which can yield
ethically questionable outcomes in multiple applications such as caregiving and mili
tary combat (Firlej & Taeihagh, 2021; Leenes et al., 2017; Solovyeva & Hynek, 2018).
Responsibility and liability for harms resulting from the use of AI applications remain
ambiguous under many legal frameworks (Leenes et al., 2017; Xu & Borson, 2018) and
the automation of routine and manual tasks in domains such as data analysis, service,
manufacturing and driving enabled by machine-learning algorithms, chatbots and
driverless vehicles are expected to displace millions of jobs that will not be evenly
distributed within and across countries (Linkov, Trump, Poinsatte-Jones, & Florin,
2018; Taeihagh & Lim, 2019). Managing the scale and speed of AI adoption and their
attendant risks is becoming an increasingly central task for governments. However, in
many instances, the beneficiaries of these technologies do not bear the costs of their
risks, and these risks are transferred to the society or governments (Leenes et al., 2017;
Soteropoulos, Berger, & Ciari, 2018).
While there is considerable literature emerging on various aspects of AI, governance
of AI is an emerging but significantly underdeveloped area. To enhance the benefits of AI
while minimising the adverse risks they pose, governments worldwide need to under
stand better the scope and depth of the risks posed. There is a need to reassess the efficacy
of traditional governance approaches such as the use of regulations, taxes, and subsidies,
which may be insufficient due to the lack of information and constant changes (Guihot,
Matthew, & Suzor, 2017), and the speed and scale of adoption of AI threatens to outpace
the regulatory responses to address the concerns raised (Taeihagh, Ramesh, & Howlett,
2021). As such, governments face mounting pressures to design and establish new
regulatory and governance structures to deal with these challenges effectively. The
increasing recognition of AI governance across government, the public (Chen, Kuo, &
Lee, 2020; Zhang & Dafoe, 2019, 2020) and industry is evident from the emergence of
new governance frameworks in the meta-discourse on AI such as adaptive and hybrid
governance (Leiser & Murray 2016; Linkov et al., 2018; Tan & Taeihagh, 2021b), and self-
regulatory initiatives such standards and voluntary codes of conduct to guide AI design
(Guihot et al., 2017; IEEE 2019). The first half of 2018 saw the release of new AI strategies
from over a dozen countries, significant boosts in pledged financial support by govern
ments for AI, and the heightened involvement of industry bodies in AI regulatory
development (Cath, 2018), raising further questions regarding what ideas and interests
2 A. TAEIHAGH

should shape AI governance to ensure inclusion and diverse representation of all
members of society (Hemphill, 2016; Jobin, Ienca, & Vayena, 2019).
This special issue introduces the multifaceted challenges of governance of Artificial
Intelligence, including emerging governance approaches to AI, policy capacity building,
and exploring legal and regulatory challenges of AI and Robotics. This introduction
unpacks AI and describes why the Governance of AI should be gaining far more attention
given the myriad of challenges it presents. The introduction then summarises of the
special issue articles are presented, and their key contributions are highlighted. Thanks to
the diverse set of articles comprising this special issue; it highlights the state-of-the-art in
the governance of AI and discusses the outstanding issues and gaps that need attention,
aiming to enable researchers and practitioners to appreciate the challenges that AI brings
better and understand the complexities of governance of AI and future avenues for
exploration.
2. AI – background and recent trends
Conceptions of AI date back to earlier efforts in developing artificial neural networks to
replicate human intelligence, which can be referred to as the ability to interpret and learn
from the information. Originally designed to understand neuron activity in the human
brain, more sophisticated neural networks were developed in the late 20th
century with
the aid of advancements in processing power to solve problems such as image and speech
recognition (Izenman 2008). These efforts led to the introduction of the concept of AI as
computer programs (or machines) that can perform predefined tasks at much higher
speeds and accuracy. In the most recent wave of AI developments facilitated by advance
ments in big data analytics, AI capabilities have expanded to include computer programs
that can learn from vast amounts of data and make decisions without human guidance,
commonly referred to as Machine-learning (ML) algorithms (Izenman 2008). Unlike
earlier algorithms that rely on pre-programmed rules to execute repetitive tasks, ML
algorithms are designed with rules about how to learn from data that involves ‘inferential
reasoning’, ‘perception’, ‘classification’, and ‘optimisation’ to replicate human decision-
making (Bathaee, 2018; Linkov et al., 2018). The learning process involves feeding these
algorithms with large data sets, from which they seek and test complex mathematical
correlations between candidate variables to maximise predictions of a specified outcome
(Kleinberg et al. 2018; Brauneis & Goodman, 2018). As these algorithms adapt their
decision-making rules with more experience, ML-driven decisions are primarily depen
dent on the data rather than on pre-programmed rules and, thus, typically cannot be
predicted well in advance (Mittelstadt et al., 2016).
Among AI experts and researchers, there is a broad consensus that AI still ‘falls
short’ of human cognitive abilities, and most AI applications that have been successful
to date stem from ‘narrow AI’ or ‘weak AI’, which refer to AI applications that can
perform tasks in specific and restricted domains, such as chess, image, and speech
recognition (Bostrom & Ludkowsky 2014; Lele, 2019b). Narrow AI is expected to
automate and replace many mid-skill professions due to their ability to execute routine,
cognitive tasks at much higher speeds and accuracy than their human counterparts
(Lele, 2019bb; Linkov et al., 2018). In future, it is expected that this form of AI will
eventually achieve ‘General AI’ or ‘artificial general intelligence’, a level of intelligence
POLICY AND SOCIETY 3

comparable to or surpassing humans due to the ability to generalise across different
contexts that cannot be programmed in advance (Bostrom & Ludkowsky 2014; Wang
& Siau, 2019). This introduction and the articles comprising this special issue focus on
applications of narrow AI.
Both industry and governments worldwide have enthused over the potential societal
benefits arising from AI and thus, have accelerated the technology’s development and
deployment across various domains. Some of the impetuses for deploying AI include
increasing economic efficiency and quality of life, meeting labour shortages, tackling
ageing populations and strengthening national defence, and they vary between govern
ments according to each nation’s unique strategic concerns (Lele, 2019; Taeihagh & Lim,
2019). For instance, governments in Japan and Singapore have supported the use of
assistive and surgical robots in healthcare and autonomous vehicles for public transpor
tation to meet labour shortages and tackle ageing populations (Inagaki, 2019; SNDGO
2019; Taeihagh & Lim, 2019; Tan & Taeihagh, 2021, 2021b). Cost-savings and increased
productivity are the main motivations for AI adoption in various sectors, which is already
transforming the manufacturing, logistic, service, and maritime industries (World
Economic Forum, 2018). AI-based technologies are also a strategic military asset for
countries such as China, US, and Russia, whose governments have made significant
investments in robots, drones and fully autonomous weapon systems for national
defence and geopolitical influence (Allen, 2019; Lele, 2019).
3. Understanding the risks of AI
Many scholars highlight the safety issues that can arise from deploying AI in various
domains. A major challenge faced by most AI applications to date stems from their lack
of generalizability to different contexts, in which they can face unexpected situations
widely referred to as ‘corner cases’ that the system had not been trained to handle
(Bostrom & Ludkowsky 2014; Lim & Taeihagh, 2019; Pei, Cao, Yang, & Jana, 2017).
For instance, fatal crashes have already resulted from trials of Tesla’s partially autono
mous vehicles due to the system’s misinterpretation of unique environmental conditions
that it had not previously experienced during testing. While various means of detecting
these corner cases in advance have been devised, such as simulating data on many
possible driving situations for autonomous vehicles, not all scenarios can be covered or
even envisioned by the human designers (Bolte, Bar, Lipinski, & Fingscheidt, 2019; Pei
et al., 2017). Due to the complexity and adaptive nature of ML processes, it is difficult for
humans to articulate or understand why and how a decision was made, which hinders the
identification of corner case behaviours in advance (Mittelstadt et al., 2016). As ML
decisions are highly data-driven and unpredictable, the system can exhibit vastly different
behaviours in response to almost identical inputs that make it difficult to specify ‘correct’
behaviours and verify their safety in advance (Koopman & Wagner, 2016). In particular,
scholars point out potential safety hazards that can also arise from the interaction
between AI systems and their users due to the problem of automation bias, where
humans afford more credibility to automated decisions due to the latter’s seemingly
objective nature and, thus, grow complacent and display less cautious behaviour while
using AI systems (Osoba & Welser, 2017; Taeihagh & Lim, 2019). Thus, human-machine
interfaces significantly shape the degree of safety, particularly in social settings that
4 A. TAEIHAGH

involve frequent interactions with users such as robots for personal care, autonomous
vehicles, and service providers.
The decision-making autonomy of AI significantly reduces human control over their
decisions, creating new challenges for ascribing responsibility and legal liability for the
harms imposed by AI on others. Existing legal frameworks for the ascribing of respon
sibility and liability for machine operation treat machines as tools that are controlled by
their human operator based on the assumption that humans have a certain degree of
control over the machine’s specification (Matthias 2004; Leenes & Lucivero, 2014).
However, as AI relies largely on ML processes that learn and adapt their own rules,
humans are no longer in control and, thus, cannot be expected to always bear respon
sibility for AI’s behaviour. Under strict product liability, manufacturers and software
designers could be subject to liability for manufacturing defects and design defects, but
the unpredictability of ML decisions implies that many erroneous decisions made by AI
are beyond the control of and cannot be anticipated by these parties (Butcher & Beridze,
2019; Kim et al. 2017; Lim & Taeihagh, 2019). This raises critical questions regarding the
extent to which different parties in the AI supply chain will be held liable in different
accident scenarios and the degree of autonomy that is sufficient to ‘limit’ the responsi
bility of these parties for such unanticipated accidents (Osoba & Welser, 2017; Wirtz,
Weyerer, & Sturm, 2020). It is also widely recognised that excessive liability risks can
hinder long-run innovation and improvements to the technology, which highlights
a major issue regarding how governments can structure new liability frameworks that
balance the benefits of promoting innovation with the moral imperative of protecting
society from the risks of emerging technologies (Leenes et al., 2017).
Given the value-laden nature of the decisions automated by algorithms in various
aspects of society, AI systems can potentially exhibit behaviours that conflict with societal
values and norms, prompting concerns regarding the ethical issues that can arise from
AI’s rapid adoption. One of the most intensively discussed issues across industry and
academia is the potential for algorithmic decisions to be biased and discriminatory. As
ML algorithms can learn from data gathered from society to make decisions, they could
not only conflict with the original ethical rules they were programmed with but also
reproduce the inequality and discriminatory patterns of society that is contained in such
data (Goodman & Flaxman, 2017; Osoba & Welser, 2017; Piano, 2020). If sensitive
personal characteristics such as gender or race in the data are used to classify individuals,
and some characteristics are found to negatively correlate with the outcome that the
algorithm is designed to optimise, the individuals categorised with these traits will be
penalised over others with different group characteristics (Liu 2018). This could yield
disparate outcomes in terms of risk exposure and access to social and economic benefits.
Bias can also be introduced through the human designer in constructing the algorithm,
and even if sensitive attributes are removed from the data, there are techniques for ML
algorithms to use ‘probabilistically inferred’ variables as a proxy for sensitive attributes,
which is much harder to regulate (Kroll et al., 2016; Osoba & Welser, 2017). The risk of
bias and discrimination stemming from the optimisation process in AI algorithms
reflects a dominant concern surrounding discussions of fairness in AI governance –
the trade-off between equity and efficiency in algorithmic decision-making – (Sætra,
2020) and how a balance can be struck to produce socially desirable outcomes catering to
the different groups’ ethical preferences remains subject to debate.

A vast body of literature and government reports have highlighted issues of data
privacy and surveillance that can arise from AI applications. As algorithms in AI systems
utilise sensors to collect data and big data technologies to store, process and transmit data
through external communication networks, there have been concerns regarding the
potential misuse of personal data by third parties and increasing calls for more holistic
data governance frameworks to ensure reliable sharing of data within and between
organisations (Gasser & Almeida, 2017; Janssen, Brous, Estevez, Barbosa, & Janowski,
2020). AI systems store extensive personal information about their users that can be
transmitted to third parties to profile individuals’ preferences, such as using past travel
data collected in autonomous vehicles to tailor advertisements to passengers (Chen et al.,
2020; Lim & Taeihagh, 2018), using personal and medical information collected by
personal care robots and networked medical devices for the surveillance of individuals
(Guihot et al., 2017; Leenes et al., 2017; Tan, Taeihagh, & Tripathi, 2021). The ownership
of such data and how AI system developers should design these robots to adhere to
privacy laws are key concerns that remain to be addressed (Chen et al., 2020; Leenes et al.,
2017). Surveillance is also a key concern over the use of AI in many domains, such as
surveillance robots in the workplace that monitor employee performance and govern
ment agencies potentially using autonomous vehicles to track passenger movements with
negative implications for democratic freedoms and personal autonomy (Leenes et al.,
2017; Lim & Taeihagh, 2018).
The autonomy assumed by AI systems to make decisions in place of humans can
introduce ethical concerns in their application across various sectors. Studies have
underlined the potential for personalisation algorithms used by digital platforms to
undermine the decision-making autonomy of data subjects by filtering information
presented to users based on their preferences and influencing their choices. By exerting
control over an individual’s decision and reducing the ‘diversity of information’ pro
vided, personalisation algorithms can reduce personal autonomy and, thus, be construed
as unethical (Mittelstadt et al., 2016). In healthcare, the use of robots to provide personal
care services has prompted concerns over the potential loss of autonomy and dignity of
care recipients if robots excessively restrict patients’ mobility to avoid dangerous situa
tions (Leenes et al., 2017; Tan et al., 2021). Studies have yet to examine how these risks
can be balanced against their potential benefits for autonomy in other scenarios, such as
autonomous vehicles increasing mobility for the disabled and elderly (Lim & Taeihagh,
2018), and personal care robots offering patients greater freedom of movement with the
assurance of being monitored (Leenes et al., 2017). In the military, autonomous weapon
systems such as drones and unmanned aerial vehicles have been developed to improve
the precision and reliability of military combat, planning and strategy, but there has been
increasing momentum across industry and academia, including prominent figures, high
lighting their ethical and legal unacceptability (Lele, 2019; Roff, 2014). Central to these
concerns is the delegation of authority to a machine to exert lethal force ‘independently
of human determinations of its moral and legal legitimacy’ and the lack of controllability
over these adaptive systems that could amplify the consequences of failure, prompting
fears of a dystopian future where such weapons inflict casualties and escalate crises at
a much larger scale (Firlej & Taeihagh, 2021; Scharre, 2016; Solovyeva & Hynek, 2018).
Unemployment and social instability resulting from the automation of routine cog
nitive tasks remains one of the most publicly debated issues concerning AI adoption
6 A. TAEIHAGH

(Frey & Osborne, 2017; Linkov et al., 2018). The effects of automation are already felt in
industries such as the manufacturing, entertainment, healthcare, finance, and transport
sectors as companies increasingly invest in AI to reduce labour costs and boost efficiency
(Linkov et al., 2018). While technological advancements have historically created new
jobs as well, there are concerns that the distribution of employment opportunities is
uneven across sectors and skill levels. Studies show that highly routine and cognitive
tasks that characterise many middle-skilled jobs are at a high risk of automation. In
contrast, tasks with relatively lower risks of automation are those that machines cannot
easily replicate – this includes manual tasks in low-skilled, service occupations that
require flexibility and ‘physical adaptability’, as well as high-skilled occupations in
engineering and science that require creative intelligence (Frey & Osborne, 2017;
World Economic; Forum, 2018). As high- and low-skilled occupations benefit from
increased wage premiums and middle-skilled jobs are being phased out, automation
could exacerbate income and social inequalities (Alonso et al. 2018).
4. Governing AI
4.1 Why AI governance is important
Understanding and managing the risks posed by AI is crucial to realise the benefits of the
technology. Increased efficiency and quality in the delivery of goods and services, greater
autonomy and mobility for the elderly and disabled, and improved safety from using AI
in safety-critical operations such as in healthcare, transport and emergency response are
the many socio-economic benefits arising from AI that can propel smart and sustainable
development (Agarwal, Gurjar, Agarwal, & Birla, 2015; Lim & Taeihagh, 2018;
Yigitcanlar et al., 2018). Thus, as AI systems develop and increase in complexity, their
risks and interconnectivity with other smart devices and systems will also increase,
necessitating the creation of both specific governance mechanisms, such as for health
care, transport and autonomous weapons, as well as a broader global governance frame
work for AI (Butcher & Beridze, 2019).
4.2 Challenges to AI governance
The high degree of uncertainty and complexity of the AI landscape imposes many
challenges for governments in designing and implementing effective policies to govern
AI. Many challenges posed by AI stem from the nature of the problem, which are highly
unpredictable, intractable and nonlinear, making it difficult for governments to formu
late concrete objectives in their policies (Gasser & Almeida, 2017; Perry & Uuk, 2019).
ML systems’ inherent opacity and unpredictability pose technical challenges for
governments in ensuring the accountability of AI. Firstly, the opacity of complex ML
algorithms remains a major barrier to AI governance as it limits the extent of
transparency, explainability and accountability that can be achieved in AI systems
(Lim & Taeihagh, 2019; Mittelstadt et al., 2016). Even with mandated levels of
transparency and explainability of algorithms, it is impossible for the experts them
selves to interpret how certain algorithmic outputs are derived from its inputs and
designing the algorithm to be more explainable reduces their complexity, which has

been shown to undermine accuracy and performance (Felzmann, Villaronga, Lutz, &
Tamò-Larrieux, 2019; Piano, 2020). This issue is highlighted as a severe limitation of
the EU General Data Protection Regulation in increasing algorithmic transparency to
tackle discrimination (Goodman & Flaxman, 2017). Algorithms are often kept inten
tionally opaque by their developers to prevent cyber-attacks and to safeguard trade
secrets, which is legally justified by intellectual property rights, and the complexity of
extensive datasets used by ML algorithms makes it nearly impossible to identify and
remove all variables that are correlated with sensitive categories of personal data
(Carabantes, 2020; Goodman & Flaxman 2016; Kroll et al., 2016). As most individuals
lack sufficient technical literacy or the willingness to pay for accessing such expertise
to help them to interpret these explanations, requirements by the likes of GDPR for
mandated explanations are unlikely to inform and/or empower them (Carabantes,
2020; Felzmann et al., 2019). Secondly, ML decisions are unpredictable as they are
derived from the data and can differ significantly with slight changes in inputs. The
lack of human controllability over the behaviour of AI systems suggests the difficulty
of assigning liability and accountability for harms resulting from software defects, as
manufacturers and programmers often cannot predict the inputs and design rules
that could yield unsafe or discriminatory outcomes (Kim et al. 2017; Kroll et al.,
2016).
Data governance is a key issue surrounding the debate on AI governance, as multiple
organisational and technological challenges exist that impede effective control over data
and attribution of responsibility for data-driven decisions made by AI systems (Janssen
et al., 2020). Data fragmentation and lack of interoperability between systems limits an
organisation’s control over data flows throughout its entire life cycle and shared roles
between different parties in data sharing clouds the chain of accountability and causation
between AI-driven decisions/events and the parties involved in facilitating that decision
(Janssen et al., 2020).
Existing governance and regulatory frameworks are also ill-equipped to manage the
societal problems introduced by AI due to the insufficient information required for
understanding the technology and regulatory lags behind AI developments. Major
technology companies and AI developers such as Google, Facebook, Microsoft, and
Apple possess huge informational and resource advantages over governments in regulat
ing AI, which significantly supersedes governments’ traditional role in distributing and
controlling resources in society (Guihot et al., 2017). The information asymmetries
between technology companies and regulators compound the difficulty for the latter in
understanding and applying new or existing legislation to AI applications (Taeihagh
et al., 2021). Regulators are struggling to meet these informational gaps and are lagging
behind due to rapid developments in the technology, which in turn leads to the formula
tion of laws that are ‘too general’ or ‘vague’ and lack specificity to effectively regulate the
technology (Guihot et al., 2017; Larsson, 2020). In particular, lawmakers can be deterred
from outlining specific rules and duties for algorithm programmers to allow for future
experimentation and modifications to code to improve the software, but doing so
provides room for programmers to evade responsibility and accountability for the
system’s resulting behaviour in society (Kroll et al., 2016). These challenges demonstrate
how the four governing resources traditionally used by governments for regulation are
insufficient to manage the risks arising from AI and the need for governments to find new
8 A. TAEIHAGH

ways of acquiring information and devising effective policies that can adapt to the
evolving AI landscape (Guihot et al., 2017; Wirtz et al., 2020).
Amidst the issues with ‘hard’ regulatory frameworks, industry bodies and govern
ments have increasingly adopted self-regulatory or ‘soft law’ approaches to govern AI
design, but they remain limited in their effectiveness. Soft law approaches refer to
‘nonbinding norms and techniques’ that create ‘substantive expectations that are not
directly enforceable’. Industry bodies have released voluntary standards, guidelines,
and codes of conduct (IEEE 2019), and governments alike have formed expert com
mittees to devise AI strategies and released multiple AI ethics guidelines and govern
ance frameworks (PDPC et al. 2020; AI; Hleg, 2019; Jobin et al., 2019). Guidelines can
be amended and adapt more rapidly than traditional regulation and thus are advanta
geous in keeping pace with technological developments (Hemphill, 2020; Larsson,
2020). This approach has been adopted in response to previous emerging technologies
and can promote ethical, fair, and non-discriminatory practices in AI design, but
many issues exist regarding their efficacy. Firstly, the voluntary nature of self-
regulatory initiatives cannot assure that the outlined principles will always be adhered
to, particularly as they are often not subject to uniform enforcement standards
(Butcher & Beridze, 2019; Hemphill, 2016). Secondly, governments will face the
challenge of ensuring consistent application of these guidelines in designing the
same AI technology across different sectors if the principles differ across multiple
guidelines and are not well-coordinated with regulations (Cath et al. 2018; Guihot
et al., 2017).
In addition, self-regulation alone could be insufficient and even undesirable for AI
governance due to their inability to ensure inclusivity and representation of diverse
stakeholders. The deep involvement of industry stakeholders in developing ethical
principles and regulations for AI raises concerns that corporate interests dominate
AI regulations, which has been an ongoing critique of self-regulatory initiatives to
govern emerging technologies in general (Hemphill, 2016). For instance, major tech
nology companies have exerted significant influence over the framing of AI issues and
the formulation of AI policy, such as through lobbying efforts and their inclusion in AI
expert groups formed by governments (Cath et al. 2018; Jobin et al., 2019). Studies
have highlighted the risks of regulatory capture by AI industries due to the significant
informational advantages of AI developers that make their latter’s technological
expertise ‘particularly valuable’ for regulators (Guihot et al., 2017). Furthermore, the
‘inscrutable’ and highly opaque nature of ML algorithms could be used by corporations
to legitimise deep industry involvement in AI regulations and the choices made behind
these regulations are often made away from public scrutiny. The unbridled influence of
corporations, as well as key political figures in the AI landscape, could exacerbate
power imbalances and social inequalities, as the ideologies and interests of an elite few
could manifest themselves through the design of AI and the decisions that they make
in society (Jobin et al., 2019). To ensure greater inclusivity and diversity in AI
governance, more research is required to examine the key actors, their roles, the
dominant ideas, and values promoted in AI policies, whether there is a global con
vergence in these values across different countries and the degree to which these values
reflect society’s interests or are politically motivated (Jobin et al., 2019; Mulligan &
Bamberger, 2018).

4.3 Steps forward for AI governance
The conceptual framing of AI is a crucial determinant of how the problems introduced by
AI are understood, whether they are included in policies and the degree of priority
afforded to the problem in public policy formulation (Perry & Uuk, 2019), but the issue
of framing has yet to be extensively discussed as a key component of AI governance. As
AI is still developing with the potential to grow more salient and diverse, the complexity
of its challenges suggests that decision-making in AI systems needs to be carefully
conceptualised according to their context of application, and these framing processes
should be subject to public debate (Cunneen, Mullins, & Murphy, 2019). For instance,
how policymakers frame the relative importance of different ethical principles arising
from a particular AI application, conflicts between ethical principles, and the justification
of their importance have critical implications on the resulting trade-offs from designing
AI systems and the public’s compliance with different ethical guidelines (Piano, 2020).
The initial framing of AI is also critical to avoid amplifying perceived risks and fears
surrounding new technologies and instead promote a more balanced discourse on what
AI is, the goals and norms that AI should reinforce in society, and the design require
ments to maximise its benefits while minimising its risks (Cath et al. 2018; Cunneen et al.,
2019)
To address the governance challenges posed by the uncertainty and complexity of AI
developments, there are increasing calls for the adoption of innovative governance
approaches such as adaptive governance and hybrid or ‘de-centred’ governance (Dafoe,
2018; Linkov et al., 2018b; Pagallo, Casanovas, & Madelin, 2019; Tan & Taeihagh, 2021b).
Characteristic of adaptive and hybrid governance is the diminished role of the govern
ment in controlling the distribution of resources in society. This is defined in hybrid
governance as a combination of state and non-state actors, or blurring the public/private
distinction by different degrees where regulation exists as a combination of industry
standards and ‘public regulatory oversight’ (Guihot et al., 2017; Hemphill, 2016). Hybrid
governance can exist in the forms of co-regulation, enforced self-regulation, and meta-
regulation (Hemphill, 2016), all of which emphasise the increasing role played by non-
state actors and the need for ‘ongoing assessment of the balance of power’ between
private and public actors (Leiser & Murray, 2016). Similarly, adaptive governance
emphasises the need to shift away from ‘command and control’ measures (Gasser &
Almeida, 2017) towards more flexible approaches characterised by the iterative adjust
ment and improvement of regulations and policies as new information is gathered (Li,
Taeihagh, De Jong, & Klinke, 2021; Linkov et al., 2018; Tan & Taeihagh, 2021b). Adaptive
approaches are purported to be advantageous for proactively identifying and addressing
the risks introduced from ML systems that are expected to change over time, as well as
raising the public’s awareness of AI and engaging with the public to identify new issues
that have not yet entered the government’s agenda (Cihon, Maas, & Kemp, 2020; Linkov
et al., 2018; Pagallo et al., 2019). Flexibility is critical to enable diverse groups of
stakeholders to build consensus around the norms and trade-offs in designing AI
systems, as well as for global AI governance to be applicable across different geographical,
cultural, and legal contexts and aligned with existing standards of democracy and human
rights (Gasser & Almeida, 2017; Wirtz, Weyerer, & Geyer, 2019). Examples of adaptive
governance include laws that require regular risk assessments of the regulated activity,
10 A. TAEIHAGH

soft law approaches that involve collaboration with the affected stakeholders to develop
guidelines, and legal experimentation and regulatory sandboxes to test innovative frame
works for liability and accountability for AI that will be adapted in iterative phases (Cath
et al. 2018; Hemphill, 2020; Linkov et al., 2018; Philipsen, Stamhuis, & De Jong, 2021).
New governance frameworks can also be adapted from the approaches taken to
regulate previous emerging technologies (Gasser & Almeida, 2017). Studies have ana
lysed hybrid governance to regulate the Internet and emerging digital technologies. For
instance, Leiser and Murray (2016) highlight the need to account for the increasing role
of non-state actors, particularly private actors such as technology companies that control
the exchange of information across the globe, transnational private actors that are
developing design principles, as well as the role of civil society groups in ensuring
accountability of the former two. Lessons could be drawn from the experiences of
governing previous emerging technologies such as the Internet, nanotechnology, aviation
safety and space law (Butcher & Beridze, 2019; Snir, 2014) to inform the governance of AI
and other new emerging technologies. In addition, a key research agenda for future
studies on AI governance would be to analyse the distinctive features of AI technology
that warrants different approaches from previous technologies.
An emerging body of literature has proposed governing AI systems through their
design, where social, legal, and ethical rules can be enforced through code to regulate the
behaviour of AI systems (Leenes & Lucivero, 2014). For instance, provisions in data
protection laws can be translated into technical specifications for robots equipped with
cameras to automatically blur faces to prevent categorisation of individuals based on
sensitive characteristics such as ethnicity, as well as to remove data after it has exceeded
a specified storage timeframe or to restrict third party access to particular categories of
data (Leenes et al., 2017). However, several implementation challenges must also be
tackled to govern AI systems through code effectively. Many legal and ethical rules
cannot be translated into explicit code, which includes ‘subtle exceptions’ that often
require value judgements and consideration of contextual factors for interpretation, and
the resulting machine interpretation also depends on how it was initially designed
(Leenes & Lucivero, 2014; Mulligan & Bamberger, 2018). Other risks that governments
need to manage from such an approach are potential manipulations of encoded rules to
‘subvert regulatory aims’, which is easily masked by the opacity of ML processes
(Mulligan & Bamberger, 2018).
Common to recent studies in their proposed frameworks for AI governance is the
emphasis on building broad societal consensus around AI ethical principles and ensuring
accountability, but there is a need for studies examining how these frameworks can be
implemented in practice. Gasser and Almeida’s (2017) three-tiered framework comprises
a technical layer involving the AI system processes and data structures, a layer for the
ethical design of AI, and the third layer encompassing AI’s societal implications and the
role of regulation and legislation. Rahwan (2018) proposes extending the ‘human-in-the-
loop’ approach to a broader ‘society-in-the-loop’ approach where society is first respon
sible for finding consensus on the values that should shape AI and the distribution of
benefits and costs among different stakeholders. Other proposals centre on the need for
greater centralisation and cross-cultural cooperation to improve coordination among
national approaches (Cihon et al., 2020; Óhéigeartaigh, Whittlestone, Liu, Zeng, & Liu,
2020). Cihon et al. (2020) propose a framework to centralise the current fragmented state

of international AI governance, outlining methods to monitor and coordinate national
approaches and examining the disadvantages of centralisation relative to decentralisa
tion, such as regulatory lags and limited adaptability to rapidly evolving risks. Among
these approaches, there are increasing calls to produce more concrete specifications on
implementing these governance frameworks in practice and identifying the parties in
government that are responsible for leading different aspects of AI governance (Wirtz
et al., 2020).
5. Overview of the special issue articles
The articles in the special issue tackle the issues of governing AI and robotics through
case studies and comparative analyses addressing gaps in the literature pertaining to
examining the risks and benefits of deploying AI in different applications and sectors,
identifying dominant ideals envisioned in the meta-discourse on AI governance and their
implications. Furthermore, exploration of new legal/regulatory/governance approaches
taken by various countries/governments to govern AI and examination of approaches
which can enhance implementation of AI and cross-country comparisons of AI govern
ance approaches is conducted. Authors have explored the limitations/effectiveness of
different AI governance approaches through case studies and derived key lessons to
facilitate policy learning. Below a brief summary of the articles in the special issue on the
Governance of AI and Robotics is presented.
5.1 Framing governance for a contested emerging technology: insights from AI
policy (Ulnicane, Knight, Leach, Stahl, & Wanjiku, 2021)
Whether AI develops in socially beneficial or problematic ways largely depends on public
policies, governance arrangements and regulation. In recent years many national govern
ments, international organisations, think tanks, consultancies, and civil society organisa
tions have launched their AI strategies outlining benefits and risks as well as initial
governance and regulatory frameworks. There are many questions regarding the regula
tion and governance of emerging disruptive technologies (Taeihagh et al., 2021). This
article address the following question regarding the governance of AI: What governance
and regulatory models are emerging to facilitate benefits and avoid risks of AI? What role
do different actors – governments, international organisations, business, academia, and
civil society – play in the emerging governance of AI? Do radical technological innova
tions in AI require radical or rather incremental innovations in governance? Are emer
ging governance and technology frameworks in different countries, regions and
organisations converging or diverging and why?
To study these questions, Ulnicane et al. (2021) draw on a dataset of more than 60 AI
strategies from ‘national governments, international organisations, consultancies, think
tanks and civil society organisations’ worldwide. The interdisciplinary research frame
work of the article draws on concepts and approaches from Science, Technology and
Innovation Studies, Policy Analysis and Political Science. It consists of two main pillars.
First, to study risks, uncertainties, and unintended consequences of AI in policy docu
ments, the concepts of policy framing and positive and negative expectations are used.
Second, to study emerging governance and regulation frameworks outlined in the policy
12 A. TAEIHAGH

documents, concepts of governance and governance models are used. AI policy docu
ments are analysed to establish how they frame AI. The emerging governance of AI is
analysed according to diverse governance models such as market, participatory, flexible,
and deregulated (Peters, 2001). This article contributes to the studies of emerging
disruptive technologies by analysing how the framing of risks and uncertainties of AI
leads to the development of specific governance and regulatory arrangements mapping
similarities and differences across countries, regions, and organisations.
5.2 Steering the governance of artificial intelligence: national strategies in
perspective (Radu, 2021)
The latest wave of AI developments is built around deep learning, speech and image
recognition, and data analytics tools deployed daily, such as banking, e-learning, medical
diagnosis – and more recently, smart vehicles (Radu, 2021). The article empirically
investigates the governance responses to AI developments worldwide. The article exam
ines recent AI national strategies worldwide to uncover the modalities through which
regulation is articulated at the state level.
The article highlights the key elements of the dominant regulatory discourses and
compares AI national projects. Using content analysis and comparative methods, Radu
(2021) examines the strategies of numerous countries to determine the articulation of
AI governance and the co-production of political and socio-technological constructs.
Building on the empirical evidence, the article highlights how the collective represen
tation of a technical project is predefined politically and is encapsulated into a vision
that is integrated into regulating the relevant sectors of society (Flichy, 2008). As such,
the article contributes to the policy discourse around AI by conceptualisation the
debates and critically examining the governance of AI and its complex effects in the
future.
5.3 The Governance of Artificial Agency (Gahnberg, 2021)
Gahnberg argues for the need for new regulatory strategies to conceptualise the chal
lenges of governing artificial agency. In the article, he argues that the notion of ‘intelli
gence’ is a vague metric of the success of the system and, the key characteristic of an AI
system is its overall ability to act as an autonomous agent within a specific environment
(Gahnberg, 2021). In the article, he posits that while different AI applications range from
use in filtering Spam to killer robots, they share a common characteristic of being
delegated authority to perform tasks regarding a specific objective(s).
The article underlines the social and legal constraints for delegating this authority to
an artificial agent, including legal and ethical provisions that may prohibit tasking the
agent to pursue certain objectives (e.g. using lethal force). Gahnberg (2021) further
highlights the role of non-state actors in the development of governance mechanisms,
such as in the case of development of standards and best practices developed by the
technical community for fail-safe mechanisms or initiatives such as the Algorithmic
Justice League that aims to mitigate the risks of algorithmic bias through creating
inclusive training data. By examining the challenges of governing artificial agency, the
article provides insights into the debates about AI governance.

5.4 Governing the adoption of robotics and autonomous systems in long-term
care in Singapore (Tan & Taeihagh, 2021)
Autonomous systems and robotics have been dubbed as a viable technological solution to
address the ever-increasing demand for long-term care globally, which is exacerbated by
ageing populations (Robinson, MacDonald, & Broadbent, 2014; Tan et al., 2021).
However, adopting new technologies in long-term care, like all other emerging technol
ogies, involves unintended consequences and poses risks (Taeihagh et al., 2021). For
instance, there are issues about safety, privacy, cybersecurity, liability, influence to the
existing health workforce, patient’s autonomy, patient’s dignity, moral agency, social
justice, and trust that need to be addressed by the government in the process of adoption
of autonomous systems (Sharkey & Sharkey, 2012; Stahl & Coeckelbergh, 2016; Tan et al.,
2021). Addressing these risks and ethical issues warrant the assessment of the govern
ment’s policy capacity (Wu, Ramesh, & Howlett, 2015) and the various governance
strategies that government chooses to deploy (no response, prevention-oriented, precau
tion-oriented, control-oriented, toleration-oriented and adaptation oriented) in the
implementation of these autonomous systems (Li, Taeihagh, & De Jong, 2018, Li et al.,
2021LinkManagerBM_REF_hD9ODVk6; Taeihagh & Lim, 2019).
The article is a theoretically-informed empirical study that examines the adoption of
autonomous systems in long-term care as a policy measure to arrest the issue of rising
social care demand due to the ageing population by examining Singapore as a case
study. The article reviews various existing and potential applications of autonomous
systems in social care and long-term care for older people in Singapore. It then
discusses the technological risks and ethical implications of deploying these systems
to users, society, and the economy at large. Finally, it assesses the extent to which
governance strategies influence the policy response adopted by the Singapore govern
ment, specifically in balancing the need for innovation and addressing uncertainties
arising from the deployment of emerging technologies in healthcare. Insights gathered
are important for policy learning, especially in understanding the governance process
of novel and emerging technologies as possible solutions to address the demand-supply
mismatch in the health sector. The analysis will showcase the interactive dynamics of
policy capacity, governance strategies and policy response as three major ingredients in
the governance of emerging technologies and calibrate their respective proportions to
facilitate effective implementations of novel technologies in healthcare (Tan &
Taeihagh, 2021).
5.5. Exploring governance dilemmas of disruptive technologies: the case of
care robots in Australia and New Zealand (Dickinson, Smith, Carey, & Carey,
2021)
While there is a range of experiments ongoing worldwide applying AI and robotics in
various care settings (Tan et al., 2021), with high expectations for positive outcomes,
Dickinson et al. (2021) are concerned with the unexpected consequences and risks, and
their article explores the use of robots in care services in the Australian and New Zealand.
They point out that while there is a burgeoning literature on robots, much of it is around
technical efficacy, acceptability, or the legal ramifications. They point out the lack of
14 A. TAEIHAGH

attention to the implementation of robots in care settings and their implications for
policymaking.
Informed by semi-structured interviews with policymakers, care providers, suppliers
of robots and technology experts, Dickinson et al. (2021) elicit a series of ‘dilemmas’ of
governance faced in this space and identify three dilemmas relating to independence and
surveillance, the re-shaping of human interactions, the dynamics of caregiving and
receiving care illustrating some of the tensions involved in the governance of robotics
in care services and draw attention to the issues that governments need to address for the
effective adoption of these technologies.
5.6 Law and tech collide: foreseeability, reasonableness, and advanced driver
assistance systems (Leiman, 2021)
There have been a number of fatalities involving automated vehicles since May 2016,
which have been highly publicised. Simultaneously, millions of serious injuries and
fatalities have occurred due to collisions by human-operated vehicles. In jurisdictions
where compensations depend on the establishment of fault, many of the injured or their
dependents will not recover any compensations (Leiman, 2021). In many Australian
jurisdictions, the standard of care required presents significant challenges when applied
to partly, highly, and fully automated vehicles (ibid).
Leiman (2021) explores how the existing regulatory framework in Australia considers
perceptions of risk in establishing legal liability in the case of motor vehicles and whether this
approach can be applied to automated vehicles. The article examines whether the law itself
may affect the perceptions of risk in view of the data generated by the automated vehicles and
considers the efficacy of no-fault or hybrid schemes for legal liability in existence in Australia
and compares them with the alternative legislation passed by the Parliament in the UK that
imposes responsibility on the insurers. Leiman also discusses proposals concerning the role
of government in assuring safety and fault in the case of Automated vehicles.
5.7 Co-regulating algorithmic disclosure for digital platforms. Di Porto and
Zuppetta (2021)
Di Porto and Zuppetta (2021) explore the increasing ability of IT-mediated platforms to
adopt algorithmic decision making and whether disclosure self-regulation, as it currently
stands at the EU level, is an appropriate strategy to address the risks posed by the
increasing adoption of algorithmic decision making taken by private entities. While
general data protection regulation (GDPR) requires platforms to provide information
about the treatment of personal data by AI systems and self-assess the risks of data
breaches, there is no reference made to the different abilities of the receiving entities to
understand the meaning and consequences of algorithmic decisions.
The article argues that the disclosure self-regulation should be rethought considering
the new AI developments and points out that since final consumers and SMEs are unaware
of the technical underpinnings of these platforms and the value of personal data, the
regulators should step in and address this issue. Furthermore, as platforms have increas
ingly deployed AI and Big data, they have gained the ability to manipulate the information
they produce, thus weakening the validity of consumers and small businesses choices (Di

Porto & Zuppetta, 2021). The authors advocate that the regulators should tackle informa
tion asymmetry, low bargaining power and wrong information and endorse an enforced
co-regulatory approach that allows participation of platforms and consumers, and devel
opment of personalised discloses based on the needs of the consumers to empower them.
Acknowledgments
Araz Taeihagh is grateful for the funding support provided by the Lee Kuan Yew School of Public
Policy, National University of Singapore. The special issue editor would like to thank the editors of
Policy and Society Journal and all the anonymous reviewers for their constructive feedback and
support for this and other articles in the special issue on the Governance of AI and Robotics.
A special thanks to Hazel Lim, Devyani Pande, and Siying Tan of the Policy Systems Group and
the events team at the Lee Kuan Yew School of Public Policy for their support in various aspects of
organising this special issue and the accompanying workshop held on August 30-31, 2019.
Disclosure of potential conflicts of interest
No potential conflict of interest was reported by the author.
Funding
This work was supported by the National University of Singapore.
Notes on contributor
Araz Taeihagh (DPhil, Oxon) is the Head of Policy Systems Group at the Lee Kuan Yew School of
Public Policy, Principal Investigator at the Centre for Trusted Internet and Community at the
National University of Singapore, and Visiting Associate Professor at Dynamics of Inclusive
Prosperity Initiative, at the Rotterdam School of Management, Erasmus University. He has lived
and conducted research on four continents, and since 2007, his research interest has been on the
interface of technology and society. Taeihagh is interested in socio-technical systems and focuses
on the unique challenges that arise from introducing new technologies to society (e.g., crowdsour
cing, sharing economy, autonomous vehicles, AI, MOOCs, ridesharing). Taeihagh focuses on: a)
how to shape policies to accommodate new technologies and facilitate sustainable transitions; b)
the effects of these new technologies on the policy process; and c) changing the way we design and
analyse policies by developing innovative practical approaches to address the growth in the
interdependence and complexity of socio-technical systems. Araz has more than two decades of
experience working with firms on PMC and Design issues relating to chemical, petroleum and
construction projects and provides technical and policy consultations on environmental, trans
portation, energy, and technology related issues.
ORCID
Araz Taeihagh http://orcid.org/0000-0002-4812-4745
16 A. TAEIHAGH

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