Artificial intelligence research strategy of the United States: critical assessment and policy recommendations

Abstract

The foundations of Artificial Intelligence (AI), a field whose applications are of great use and concern for society, can be traced back to the early years of the second half of the 20th century. Since then, the field has seen increased research output and funding cycles followed by setbacks. The new millennium has seen unprecedented interest in AI progress and expectations with significant financial investments from the public and private sectors. However, the continual acceleration of AI capabilities and real-world applications is not guaranteed. Mainly, accountability of AI systems in the context of the interplay between AI and the broader society is essential for adopting AI systems via the trust placed in them. Continual progress in AI research and development (R&D) can help tackle humanity's most significant challenges to improve social good. The authors of this paper suggest that the careful design of forward-looking research policies serves a crucial function in avoiding potential future setbacks in AI research, development, and use. The United States (US) has kept its leading role in R&D, mainly shaping the global trends in the field. Accordingly, this paper presents a critical assessment of the US National AI R&D Strategic Plan and prescribes six recommendations to improve future research strategies in the US and around the globe.

Full text

TYPE Perspective
PUBLISHED 07 August 2023
DOI 10.3389/fdata.2023.1206139
OPEN ACCESS
EDITED BY
Rashid Mehmood,
King Abdulaziz University, Saudi Arabia
REVIEWED BY
V. S. Malemath,
KLE Dr. M.S. Sheshgiri College of Engineering
and Technology, India
Andrea Brunello,
University of Udine, Italy
Alberto Ochoa Zezzatti,
Universidad Autónoma de Ciudad
Juárez, Mexico
*CORRESPONDENCE
Ioannis A. Kakadiaris
ioannisk@uh.edu
RECEIVED 15 April 2023
ACCEPTED 24 July 2023
PUBLISHED 07 August 2023
CITATION
Gursoy F and Kakadiaris IA (2023) Artificial
intelligence research strategy of the
United States: critical assessment and policy
recommendations. Front. Big Data 6:1206139.
doi: 10.3389/fdata.2023.1206139
COPYRIGHT
©2023 Gursoy and Kakadiaris. This is an
open-access article distributed under the terms
of the Creative Commons Attribution License
(CC BY). The use, distribution or reproduction
in other forums is permitted, provided the
original author(s) and the copyright owner(s)
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accepted academic practice. No use,
distribution or reproduction is permitted which
does not comply with these terms.
Artificial intelligence research
strategy of the United States:
critical assessment and policy
recommendations
Furkan Gursoy and Ioannis A. Kakadiaris*
Computational Biomedicine Lab, University of Houston, Houston, TX, United States
The foundations of Artificial Intelligence (AI), a field whose applications are of
great use and concern for society, can be traced back to the early years of
the second half of the 20th century. Since then, the field has seen increased
research output and funding cycles followed by setbacks. The new millennium
has seen unprecedented interest in AI progress and expectations with significant
financial investments from the public and private sectors. However, the continual
acceleration of AI capabilities and real-world applications is not guaranteed.
Mainly, accountability of AI systems in the context of the interplay between AI
and the broader society is essential for adopting AI systems via the trust placed in
them. Continual progress in AI research and development (R&D) can help tackle
humanity’s most significant challenges to improve social good. The authors of this
paper suggest that the careful design of forward-looking research policies serves a
crucial function in avoiding potential future setbacks in AI research, development,
and use. The United States (US) has kept its leading role in R&D, mainly shaping
the global trends in the field. Accordingly, this paper presents a critical assessment
of the US National AI R&D Strategic Plan and prescribes six recommendations to
improve future research strategies in the US and around the globe.
KEYWORDS
artificial intelligence, research, development, policy, strategy, accountable AI
1. Introduction
The roots of Artificial Intelligence (AI) as a research field are usually traced back to
a workshop held in 1956 on the campus of Dartmouth College (McCarthy et al., 2006).
By the time of the workshop, some original ideas that characterize AI were already there.
Some notable examples are Turing’s seminal paper on computing machinery and intelligence
(Turing, 1950), the program called Logic Theorist that could prove mathematical theorems
using symbolic logic (Newell and Simon, 1956), the first neural net machine in 1951
(Crevier, 1993), and early efforts for self-learning checkers player (Sammut and Webb,
2010). As the Dartmouth workshop unified AI as a discipline, funding started to flow
into AI research. However, the AI researchers overpromised, and the challenges were
underestimated. Eventually, the promises were undelivered. As funders became unhappy
with the progress, the amount and flexibility of funding considerably declined in the 1970s
(Crevier, 1993). The following years are considered the setback years or the first AI Winter.
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In the 1980s, there was a renewed interest in AI with the
advent of expert systems (Crevier, 1993). Outside the United States
(US) and the United Kingdom, Japan began to invest in
the field (Shapiro, 1983). This period saw a great interest in
knowledge representation and the revival of the interest in neural
networks (McCorduck, 2004). The period is also characterized by
dramatically increasing commercial interest. However, commercial
vendors failed to develop workable solutions for real-world
problems. The late 1980s and early 1990s also see hundreds of
AI companies shutting down and the funding for AI dramatically
decreasing once again (Newquist, 1994). The late 1980s and early
1990s are popularly known as the AI Winter or the second
AI Winter.
AI research was reinvigorated in the late 1990s and accelerated
during the new millennium. Recent years have seen a dramatic
increase in the funding for AI research and commercial ventures
(Mousavizadeh et al., 2021;NSF, 2021). On the other hand, some
prominent researchers argue that AI abilities were overestimated
in the 2010s, and they anticipate that an AI autumn might
be imminent (Shead, 2020). One way to avoid such potential
setbacks in AI’s progress is the careful and visionary design of
research policies. The National Artificial Intelligence Research and
Development Strategic Plan (National Science and Technology
Council, 2016), referred to as the Plan in the rest of this paper, is
the current document highlighting the critical priorities for the US
federal investments in AI research and development. Considering
the leading role of the US, with more than 600 billion dollars
in gross domestic spending on R&D in 2019 (OECD, 2022), this
paper argues that the Plan has broader effects beyond the US in
shaping the future of AI research. Therefore, it is worthy of a critical
assessment by the academic community.
National Science and Technology Council, through which the
executive branch of the US federal government coordinates science
and technology policy, published the first version of the Plan in
2016 (National Science and Technology Council, 2016). Updated
in 2019, the Plan (National Science and Technology Council, 2019)
establishes federally funded AI research objectives by identifying
eight strategic priorities. The Plan focuses on issues the industry
is unlikely to handle on its own, presenting areas where federal
investment is most likely to benefit.
While there are favorable views regarding the social good
that AI can provide (Taddeo and Floridi, 2018;Tomasev et al.,
2020), there are also studies that criticize the unjustified and
hurried optimism regarding AI for social good (Moore, 2019)
as well as studies that highlight the potential risks of AI (Cave
and Heigeartaigh, 2018;Tzimas, 2021). Accordingly, the main
contribution of this paper is to provide a critical assessment
of the Plan and present recommendations to enhance the Plan
toward achieving a trustworthy and safe AI that is welcome in
society to progress the world toward a techno-social paradigm.
In this way, humans and accountable AI systems can collaborate
to address society’s most significant challenges, keeping the social
good and progress at the center. The remainder of this paper is
structured as follows. Section 2 provides summary descriptions
of the eight strategic priorities. Section 3 presents and discusses
recommendations to strengthen the Plan. Final remarks and
conclusions are provided in Section 4.
2. Strategic priorities
The Plan outlines eight strategies. The strategies span the entire
field rather than responding to or highlighting individual research
challenges. The first and second strategies include R&D areas
where further progress is needed to advance AI. The remaining
six strategies are presented as the cross-cutting R&D foundations
affecting the development of AI systems. Based on these eight
strategic priorities, future enhancements in the field of AI are
expected to assist individual applications of AI. Next, we review and
briefly explain each strategy.
The first strategy is concerned with making long-term
investments in AI research. In addition to the incremental
research with predictable short-term outcomes, this strategy
aims to sustain long-term research that may be riskier but
potentially have very large payoffs. The strategy explicitly
highlights (i) knowledge discovery from multi-modal, noisy,
and big data; (ii) perceptual capabilities of AI systems via
sensors and other means; (iii) understanding of theoretical
limitations of AI concerning available hardware; (iv) general-
purpose artificial intelligence that is capable of performing different
kinds of tasks like humans do; (v) coordination of multi-AI
systems; (vi) human-like AI that can learn from small sample
sizes, and that can explain itself; (vii) robotic technologies;
(viii) hardware specialized for AI; and (ix) AI for improving
hardware design. This strategy mentions several vital concepts
such as perception and attention, commonsense and probabilistic
reasoning, combinatorial optimization, knowledge representation,
natural language processing, and human-machine interaction as
prioritized areas for fundamental AI research.
The second strategy is concerned with developing effective
methods for human-AI collaboration. The strategy suggests that
many applications of AI will not be completely autonomous.
Instead, a combination of AI and human systems will work
together. An effective and efficient human-AI collaboration
requires additional R&D. The strategy highlights some
development challenges: (i) human-aware intelligent systems
that are capable of intuitive interaction with humans; (ii) AI
techniques that enhance human capabilities, for instance, through
wearable devices; (iii) human-AI interfaces to present increasingly
complex data in a human-understandable manner; and (iv) better
language processing systems that overcome current challenges
such as noisy surroundings, heavily accented speech, impaired
speech, and real-time dialogue with humans. The strategy also
argues that trust in AI is necessary for human-AI collaborations,
which is related to fairness, explainability, and transparency.
The third strategy is concerned with understanding and
addressing AI’s ethical, legal, and societal implications. This
strategy focuses on fundamental concepts such as trustworthiness,
fairness, transparency, accountability, explainability, and
ethics. The strategy presents three subsections to explore
critical challenges: (i) incorporating fairness, transparency,
and accountability in the design of AI systems, (ii) building
ethical AI; and (iii) designing system architectures incorporating
ethical reasoning.
The fourth strategy is concerned with ensuring the safety and
security of AI systems. The strategy emphasizes the vital role of
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safety and security in achieving robust and trustworthy AI systems.
The strategy presents several challenges: (i) developing AI systems
that are capable of explaining the reasons behind the outputs they
produce; (ii) building trust in AI; (iii) enhancing verification and
validation of AI systems by meeting formal specifications and
user’s operational needs, respectively; (iv) robustness against cyber-
attacks; and (v) developing self-monitoring architectures for the
safety of self-modifying systems.
The fifth strategy involves developing shared public datasets
and environments for AI training and testing. The strategy presents
three critical areas of importance: (i) developing a wide variety
of accessible datasets for the needs of the whole spectrum of
AI applications; (ii) ensuring responsiveness of training and
testing resources to public and commercial needs; and (iii) open-
source software for making AI technologies more accessible.
The strategy further stresses the importance of findability,
accessibility, interoperability, and reusability principles for datasets
and potential privacy and bias issues in datasets. Moreover, the need
for computational resources to process data is underlined.
The sixth strategy is concerned with measuring and evaluating
AI technologies based on well-established standards and
benchmarks. The strategy highlights several areas as needing
further progress: (i) developing AI standards for the broad
spectrum of AI; (ii) establishing benchmarks for evaluating AI
and its compliance to the standards; (iii) increasing the availability
of testbeds in all areas of AI; and (iv) engaging users, industry,
government, and academia in standards and benchmarks. Further,
the strategy calls attention to measuring and evaluating AI
systems to assess and assure safety, security, privacy, robustness,
explainability, transparency, and fairness.
The seventh strategy is to understand better the national AI
R&D workforce needs. It highlights the increasing demand for AI
expertise and calls for improving the existing efforts for advancing
the AI R&D workforce. The strategy explicitly mentions enhancing
instructional capacity from K-12 to graduate level, nurturing
computer scientists and experts from other fields such as cognitive
science, economics, linguistics, and others.
The last strategy concerns expanding public-private
partnerships to accelerate advances in AI. The strategy explicitly
states government, universities, and industry entities for public-
private partnerships. The benefits of such collaboration include
leveraging resources to push innovation, supporting the practices
based on these innovations, and enhancing the training for future
researchers and practitioners.
3. Recommendations
The increasingly decisive role of AI in people’s lives necessitates
a sociotechnical viewpoint (Sartori and Theodorou, 2022) that
encompasses everything from the conception of an AI system
to the consequences of its use in the real world. Such a
sociotechnical viewpoint concerns interactions and other complex
relations between human and AI systems (Herrmann and
Pfeiffer, 2022). The current version of the National Artificial
Intelligence Research and Development Strategic Plan already
addresses several sociotechnical aspects. This section proposes and
discusses six recommendations to enhance the Plan for achieving
trustworthy AI.
I. The first strategy describes fundamental AI research areas where
further efforts are encouraged. While the topics around Causal
AI (Yao et al., 2021;Scholkopf, 2022) are already receiving
increasing attention from the machine learning community,
the Plan does not discuss causality in AI. However, it is still a
domain with challenging questions and potentially significant
benefits (Dhar, 2020). Exploring causal relations in a system
helps us understand the system and potentially improve AI
applications (Sgaier et al., 2020). Causal AI also provides tools
for Explainable AI (Chou et al., 2022) and fairness (Mitchell
et al., 2021), for instance, via counterfactual analysis (Kasirzadeh
and Smart, 2021). Another key topic that is worthy of
inclusion is symbolic and connectionist approaches to AI (Goel,
2022) and their potential integration, which are tightly linked
with explainability of AI, learning efficiency, and knowledge
representation.
Recommendation: The Plan should include Causal AI and the
integration between symbolic and connectionist approaches
as additional areas that require commitment for long-term
fundamental research. Future research will help AI advance
to the next stage in its capabilities, robustness, fairness, and
explanatory power.
II. The second strategy addresses human-AI collaboration.
However, it primarily focuses on creating “AI systems that
effectively complement and augment human capabilities.” It
acknowledges the challenges regarding human-aware AI, AI
techniques for human augmentation, human-AI interfaces, and
language processing systems. In general, these challenges are
concerned with improving AI systems. However, improving
human-AI collaboration does not depend solely on technical
improvements regarding AI and its interfaces or mechanistic
details of how humans collaborate with AI. In addition, it
requires an understanding and improvement of how humans
interact with and perceive the decisions or other outputs
produced by AI systems (Bader and Kaiser, 2019;Araujo
et al., 2020;Meissner and Keding, 2021). Human oversight
of AI (Wagner, 2019;Koulu, 2020) is an area where further
research is needed to understand how human decision-makers
may influence or be influenced by AI decisions and to design
appropriate and feasible monitoring and oversight mechanisms
necessary to improve trust toward AI systems and minimize
risks and harms.
Recommendation: The Plan should support research initiatives
that tackle questions related to understanding and improving
when and how humans can oversee and modify the decisions by
AI systems such that the adoption of AI in relatively higher-risk
situations may be increased while avoiding unacceptable risks.
III. The third strategy describes three key research challenges in AI’s
ethical, legal, and societal implications. These are (i) improving
fairness, transparency, and accountability by design, (ii) building
ethical AI, and (iii) designing architectures for ethical AI.
However, as described in the Plan, these three challenges
largely overlap without clear and intuitive distinctions. Also,
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explainability is discussed in the fourth strategy, which is
concerned with the safety and security of AI systems. In
contrast, this paper argues that it is more appropriate to discuss
explainability concerning the other components of the third
strategy and within its scope.
Recommendation: The third strategy may be rewritten to present
notions and challenges concerning social implications and
accountability of AI systems, which include concepts such as
responsibility, explainability, robustness, and fairness. It should
also contain references to other related strategies, such as the
second strategy on human-AI collaboration, the fourth strategy
on privacy and security of AI systems, and the sixth strategy
on developing methods, metrics, benchmarks, and standards to
evaluate AI systems.
IV. The trust to be placed in AI and its expanding role in
society depends not only on the benefits of AI but also
on its risks, potential harms, and remedies (Knowles and
Richards, 2021). Regardless of the efforts that are possibly
spent to make AI systems safe, it is not typically attainable to
ensure a given AI system is perfectly safe and free from risks
(Alfonseca et al., 2021). When due efforts are not provided or
unknown/undiscovered factors are in play, known risks increase
and unknown risks emerge. To improve trust in future AI
systems, on the one hand, the types and nature of unknown
and typically undiscovered risks should be explored by future
research. On the other hand, remedy mechanisms should be
developed and implemented. Such efforts closely relate to risk
ratings, certifications, and insurance for AI. Especially given
the unattainability of perfect AI systems, insurance is a helpful
and necessary mechanism. However, for AI systems, evaluating
the probability and severity of risks and harms is not currently
feasible, which provides an obstacle for AI insurance to emerge
due to the uncertainties around pricing or settlements.
Recommendation: The Plan should support research
initiatives that tackle questions related to understanding
and operationalizing the risks and harms of AI systems so that
risk ratings, certifications, and insurance become feasible for AI
systems. This recommendation relates to Strategies 3, 4, and 6.
V. The seventh strategy addresses the increasing demand for AI
researchers and practitioners. While it acknowledges that the AI
workforce is not composed only of computer and information
scientists and engineers but also includes multidisciplinary
teams, it appears to present the other fields and domains as areas
“in which AI may be applied.” We suggest that multidisciplinary
work where people from different disciplines work together is
insufficient. Instead, an interdisciplinary and transdisciplinary
approach (van den Besselaar, 2001) is needed to integrate
knowledge from various disciplines to cross disciplinary
boundaries to employ a holistic perspective. Accordingly, there
is a growing need for social scientists with backgrounds in
anthropology, economics, education, law, linguistics, political
science, psychology, and sociology to conduct interdisciplinary
and transdisciplinary research on the challenging problems at
the crossroads of AI and social sciences (Kwok, 2019;Royer,
2019).
Recommendation: Considering the emerging intertwined nature
of AI and human lives, the importance of cultivating an
interdisciplinary and transdisciplinary AI workforce should
be emphasized.
VI. The eighth strategy supports expanding public-private
partnerships focusing on government-university-industry
research and development partnerships. Given the social
implications of AI, civil society organizations play a relevant
and valuable role in representing the expectations of the
broader society.
Recommendation: The eighth strategy should be expanded
to include collaboration with civil society organizations,
particularly concerning future developments regarding the
societal implications of AI.
4. Conclusion
The US is leading in shaping AI research and development
trends globally. Such trends are highly relevant for the future of the
field, especially to direct resources to prevent another AI Winter,
improve social good, and ensure the safe progress of the society
toward the new sociotechnical paradigm. Given this pressing issue,
this paper investigates the official AI R&D strategies of the US
government with a critical lens. It offers six recommendations to
improve AI research strategies in the US and beyond.
The first recommendation calls for more fundamental research
on causality in AI. The second recommendation calls for a
better understanding of and mechanism design for human
oversight of AI. The third recommendation calls for a clear
and comprehensive presentation of accountable AI to guide
future research. The fourth recommendation calls for further
efforts to facilitate risk ratings, certifications, and insurance
for AI systems. The fifth recommendation calls for more
interdisciplinary and transdisciplinary research. Finally, the sixth
recommendation calls for the participation of civil society actors in
AI research collaborations.
Data availability statement
The original contributions presented in the study are included
in the article/supplementary material, further inquiries can be
directed to the corresponding author.
Author contributions
FG and IK contributed to conception and design of the
study, contributed to manuscript revision, read, and approved the
submitted version. FG wrote the first draft of the manuscript. All
authors contributed to the article and approved the submitted
version.
Funding
This material is based upon work supported by the National
Science Foundation under Grant CCF-2131504.
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Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Author disclaimer
Any opinions, findings, and conclusions or recommendations
expressed in this material are those of the authors and do not
necessarily reflect the views of the National Science Foundation.
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