Introduction
Artificial Intelligence (AI) has been a hot topic for the last
2-3 years for politicians, technologists, and many people in civil
societies globally.The use of the technology has obvious benefits
for increasing productivity and value produced by businesses and
organizations, along with dangers from misuse, such as deep fake
propaganda and serious security risks. Two recent efforts to
develop legislation addressing AI technology offer an opportunity
to compare and contrast the differing approaches in the European
Union (EU) and the United States (US).
Summary of the EU AI Act
On January 26, 2024, the Council of the European Union issued a
“Proposal for a Regulation of the European Parliament and of
the Council laying down harmonised rules on artificial intelligence
(Artificial Intelligence Act) and amending certain Union
legislative acts.” (EU AI Act). That proposal has been adopted
by the EU Parliament and will likely become EU law before the end
of 2024. (See https://assets.ey.com/content/dam/ey-sites/ey-com/en_gl/topics/ai/ey-eu-ai-act-political-agreement-overview-february-2024.pdf).
(See also: Summary: What does the European Union
Artificial Intelligence Act Actually Say?, February 23, 2024, Maria
Villegas Bravo, https://epic.org/summary-what-does-the-european-union-artificial-intelligence-act-actually-say/).
The EU AI Act sets up a risk categorization scheme to prohibit
some types of AI systems and identify the rest as either high risk,
requiring significant regulation and management, or general-purpose
AI (GPAI) systems that can be deployed if they voluntarily adhere
to Codes of Practice outlined in the Act. The EU describes the
purposes of the Act in multiple ways within the proposed
legislation, but a succinct statement of the intent follows:
A Union legal framework laying down harmonised rules on
artificial intelligence is therefore needed to foster the
development, use and uptake of artificial intelligence in the
internal market that at the same time meets a high level of
protection of public interests, such as health and safety and the
protection of fundamental rights, including democracy, rule of law
and environmental protection as recognised and protected by Union
law.’ (EU AI Act Article 5).
Prohibited AI systems are identified in Title
II, Article 5 of the EU IA Act to address a variety of unacceptable
use cases for AI.These include AI that may distort human behaviors
through deployment of subliminal, manipulative, deceptive
techniques, or by exploiting vulnerabilities related to age,
disability, or socio-economic circumstances.Other prohibited
activities include banning systems the classify biometric data, use
social scoring, assess likelihood of future criminal behavior,
scraping face recognition data, and related manipulative or
socially distortive activities.
High-Risk AI Systems are described in Title
III, Chapter 1, Article 6 through reference to EU Directives (Annex
II) or through specific use cases described in Annex III.Those use
cases include biometrics, critical infrastructure, education and
vocational training, employment, use in certain public systems,
some law enforcement uses, border control and immigration, and some
uses by judicial authorities.The requirements for high-risk AI
Systems are dealt with in Title III, Chapter 2, Article
8.Compliance will require such actions as establishing a risk
management system, conducting data governance, preparing technical
documentation, using automated record keeping, providing
instructions for us, ensuring human oversight, meet high levels of
cybersecurity and establishing a quality management system.
(Seehttps://artificialintelligenceact.eu/high-level-summary/).
The EU will also establish harmonized standards, conformity
assessment, registration and related requirements for providers of
AI models and systems. (See Title III, Chapter 5).
General Purpose AI (GPAI) Models are classified
and regulated by Title VIIIA, Chapter 2, Article 52c.All GPAI model
providers must provide technical documentation, instructions for
use, establish a policy with respect to the EU Copyright Directive,
and publish a summary about the content used for training. For some
GPAI models, providers must also meet the transparency obligations
found in Title IV, Article 52.However, free and open license GPAI
model providers only need to comply with the Copyright Directive
and publish the training data summary, unless they present a
systemic risk. To simplify compliance with the harmonization
standards for GPAI model providers, they may rely upon codes of
practice. IF a GPAI model presents systemic risk, then more
rigorous requirements are imposed, although not as stringent as
those for high-risk systems, including model evaluations,
adversarial testing, tracking and reporting serious incidents, and
meeting cybersecurity protections (EU AI Act, Title VIIIA, Chapter
3).
Summary of the U.S. Executive Order on AI
On October 30, 2023, the President issued an Executive Order on
the Safe, Secure, and Trustworthy Development and Use of Artificial
Intelligence (AI EO) (See https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/).
The goal of the AI EO is to establish a government-wide effort to
develop and govern AI using eight guiding principles.These are:
- Artificial Intelligence must be safe and secure.
- Governance must promote responsible innovation, competition,
and collaboration. - While developing use of AI we must support American
workers. - Artificial Intelligence policies must be consistent with
advancing equity and civil rights. - The interests of Americans who increasingly use, interact with,
or purchase AI and AI-enabled products in their daily lives must be
protected. - Americans’ privacy and civil liberties must be
protected. - The Federal Government must manage the risks from its own use
of AI and increase its internal capacity to regulate, govern, and
support responsible use of AI. - The Federal Government should lead the way to global societal,
economic, and technological progress.
The regulatory effort will be built upon existing resources,
including the AI Risk Management Framework, NIST AI 100-1, the
Secure Software Development Framework, and several cybersecurity
laws and executive orders.There is a clear emphasis on identifying
and prohibiting use of specific types of systems and activities
such as synthetic content, dual use foundation models,
cybersecurity, biosecurity, and preventing the malicious use of
Federal Data for AI training.The White House will establish an AI
Council to help coordinate the activities of federal agencies and
act as a central repository for federal guidelines, policies, and
communications.The AI EO establishes numerous reports and guideline
deadlines for federal agencies to provide the comprehensive
overview needed for responsible legislation and regulations in
alignment with the guiding principles.
Comparison of Similarities and Differences
The EU AI Act addresses many of the same concerns as those being
reviewed by the United States in its AI EO. However, the EU has
many directives in place that will shape the EU AI Act.The guiding
principles behind its legislative effort differ as well, due to the
multinational composition of the EU and the various concerns of the
member states. The U.S. effort is somewhat less constrained by such
multinational considerations, but prior legislation and multiple
regulatory demands of various agencies will inevitably shape the
proposals coming to the White House from the multitude of agencies
involved. A similar effort to develop legislation and regulations
for digital assets resulted in the overwhelming enforcement
requirements crowding out the innovation and other positive
benefits of a policy on digital assets. The same danger exists for
AI legislation.
Technical Analysis
Introduction to Generative AI
The broadest takeaway from the recent developments in AI, and in
particular Generative AI, is that intelligence is now available as
a service. As an analogy, generative AI takes the benefits the
industrial revolution applied to factory work and applies it to
knowledge work. As noted in both the Executive Orders concerning AI
and digital assets, AI offers the potential for great benefits with
correspondingly great risks. AI acts as an accelerant for work
requiring human intelligence, speeding up the time it takes to do
routine and repetitive tasks that require intelligent human action.
Generative AI like OpenAI’s ChatGPT, Google’s Bard, or
Anthropic’s Claude model are examples of applications based on
large language models (LLMs) that have been the focus of much of
the attention around the exponential growth in the AI sector. These
applications primarily rely on applying algorithms to massive
amounts of data to generate content, process data, and synthesize
insights based on prompts written in natural language. As
generative AI LLMs are trained on human created data, clarified
through human reinforcement learning, and applied to tasks that
would typically require human intelligence to reason out, the
different AI models can dramatically decrease the amount of time it
takes to accomplish tasks that formerly require human intelligence
to accomplish. Automating these routine tasks in content creation,
software development, customer service and sales, frees up human
intelligence for other tasks, and has the potential to dramatically
increase the value of productivity for workers utilizing generative
AI in their workflows.
Evolution and Current State
Generative AI arrived slowly, and then all at once. Built on the
foundations of deep learning, generative AI can be broadly defined
as applications built on LLMs. Deep learning has powered many of
the advances in generative AI, but the most recent models like
ChatGPT 4, Google’s Bard, and Anthropic’s Claude differ in
that they can process extremely large datasets with varying types
of data, perform more than one task, and generate novel synthetic
content from the underlying dataset. Trained on broad ranges of
data including text, images, video, audio, and computer code, the
generative AI application in use today is capable of generalized
functions that can be adapted to flexible use cases, depending on
the need of the user. These use cases include editing, summarizing
and answering questions, drafting new content, generating computer
code, and performing data analysis, to name a few. To understand
the exponential growth the field is undergoing, Anthropic’s AI
Claude was capable of processing roughly 9,000 tokens of text per
minute when it was introduced in March 2023, and is now capable of
processing 100,000 tokens of text per minute, equivalent to size of
an average novel.(See, “The Economic Potential of AI,”
McKinsey and Company, June 2023, page 5). Generative AI requires
significant investment in hardware infrastructure and resources,
reflected in the $8 billion raised by Generative AI companies
between 2020 to 2022, “accounting for 75% of total investments
in such companies during that period.”
Machine Learning Foundations
AI is not new and is based around Machine Learning methodologies
that have been around for decades. Machine Learning is technology
that uses supervised or unsupervised learning to analyze data and
make predictions based on the analysis. Supervised learning comes
with labels associated with the data and unsupervised learning does
not. Rather, unsupervised learning seeks to find patterns without
being told what to look for. With generative AI, humans are
involved and help fine tune the results in what’s known as
Reinforcement Learning with Human Feedback (RLHF). The human
feedback piece is important, as it can help mitigate some of the
risks associated with this technology. The type of data utilized
and generated by generative AI models isn’t purely text data,
but includes image data, video, and speech. There is also
translation between the different types of data, and written
prompts can make images and video, and excel charts can be pasted
into generative AI models like ChatGPT and give analysis based on
the image input, as one example. Generative AI models are deep
learning models based upon the human brain and how it learns, which
allows intelligence to be utilized on-demand as a service. The
machine learning foundations of generative AI lend the ability of
LLMs to learn and adapt, giving rise to many of the emergent
properties that are predicted to add so much value in the form of
enhanced productivity for companies and organizations.
Capabilities and Applications
Generative AI could add trillions of dollars of value to the
global economy. The benefits will (at first) likely come in the
areas of customer operations, marketing and sales, software
engineering, and R&D. Low hanging fruit for generative AI
applications are in content creation and processing of
data/information that would normally take human intelligence time
to parse and process, without an equivalent addition of value for
the work. Automating and speeding low level tasks that require
human intelligence would free up time for more creative, impactful,
and innovative work. The key gains lie in the ability of generative
AI models to understand natural language for tasks, lowering the
barrier for workers to be able to benefit from applying intelligent
content generation in flexible situations. However, generative AI
applications don’t stop there, and have developed emergent
capabilities that surprise even the designers of the models
themselves. AI researchers and developers discovered that LLMs
began to develop emergent capabilities, capable of roleplaying,
teaching, writing code, drafting strategy, giving legal and medical
advice, coaching, and more. This is where much of the value has the
potential to be realized when working with LLMs. Generative AI
models will likely completely change the face of how workers
produce value in our current economy over time, equivalent to
revolutions like the power loom in industrial revolution era mills,
applied to the realm of knowledge work. Of course, such
revolutionary change is not without significant challenges.
Challenges
Many of the technological revolutions we’ve experienced in
human history, including agriculture, the printing press, the
telegraph, were slow revolutions. AI is a technology expanding at
an exponential rate, making it a fast revolution. There is
tremendous potential to transform how we work (especially with
knowledge work consider generative AI capabilities), but AI also
carries significant risk, which is the spirit behind the EU AI Act
and the AI executive order. We simply don’t know the widespread
impact yet on how accelerating access and the speed, accuracy and
creativity of intelligence on demand will be worldwide. The US and
EU have put in place frameworks to stay ahead of these
developments, and to chart a middle path between two basic
responses to change: denial or panic. Responding to the rapid
changes generative AI represents, it is easy to either go into
denial, thinking AI won’t impact one’s job, or panic,
thinking AI will displace everyone and take all jobs. A middle path
is helpful here, as a positive viewpoint is that AI will make you
and your team productive on a level never seen before. Regardless
of the productive benefits, there are issues concerning data
security, compliance, and utilizing LLMs to undertake actions like
building weapons or planning terror attacks. These issues must be
taken into account as AI continues to develop, and sensible
regulation to help guide and shape growth can ensure the data of
users isn’t used without their consent/renumeration, models are
compliant with US or EU frameworks, and guardrails are in place to
prevent models from helping design actions/weapons that can harm
others. One thing is certain, an active role will need to be taken
by regulators to handle the exponential growth we are currently
witnessing in the generative AI space.
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