By Javier Surasky
To regulate Artificial Intelligence
(AI) globally, we first need a shared understanding of what we regulate. This
is the critical starting point for building ethical AI governance that supports
sustainable development. Time is of the essence—if we don’t act swiftly, the
consequences could be unpredictable. The clock is ticking, but we’re still in
the game.
AI has emerged as a transformative
force in the 21st century, reshaping social and economic structures at an
unprecedented pace. Its spread into every corner of life poses profound
questions for international law system, designed to govern territorial entities
now grappling with autonomous, data-driven, and borderless technologies.
The series Black Mirror often
highlights AI’s darker potential. Episodes like “White Bear” (Season 2, Episode
2) depict dystopian worlds where humanity’s sensitivity has eroded—a must-watch
for anyone interested in law and ethics. Yet, the most unsettling stories, like
“Common People” (Season 7, Episode 1), feel uncomfortably close to our reality,
a chapter in which we see a world where corporations wield unchecked power over
cutting-edge digital technologies, mirroring our current concerns.
This post will explore the first
steps toward legally defining AI and why it matters for international law.
International law evolves in
response to society’s needs. The changes AI brings (new ways of connecting,
producing goods, and communicating) reshape its foundations. While this isn’t
the first technological revolution international law has faced (think steam
engines, electricity, aviation, or the internet), AI is different. Its
development is decentralized, driven by a distributed ecosystem that’s tough
for any single state to control.
The first step toward global
regulation is agreeing on what AI is. This process has begun, but we’re far
from a universal definition.
Two competing approaches are
stalling progress:
· Risk-first advocates
prioritize controlling AI’s dangers over speeding up its development. This
camp, led by the European Union and top global experts, calls for guardrails to
ensure safety and ethics.
· Market-driven
proponents, primarily in the United States, argue for letting innovation run
free. They believe state regulations will only slow down AI’s benefits.
Caught in this tug-of-war, early
international definitions of AI lean heavily on technical perspectives. For
example, the 2022 ISO 22989 standard defines AI as “AI
research and development of mechanisms and applications of AI systems” (3.1.4),
and “Artificial Intelligence System” as an “engineered system that generates
outputs such as content, forecasts, recommendations or decisions for a given
set of human-defined objectives”. While useful, the definition lacks government
backing, legal weight, and consideration of ethical and social dimensions
critical for defining rights and responsibilities.
Other attempts, though non-binding,
offer more nuance. The OECD’s
2019 Recommendation on Artificial Intelligence
(OECD/LEGAL/0449) describes an AI system as “a machine-based system that can,
for a given set of human-defined objectives, make predictions, recommendations,
or decisions influencing real or virtual environments. AI systems are designed
to operate with varying levels of autonomy.” It highlights machines, autonomy,
and specific outputs like predictions or decisions.
UNESCO’s 2021 Recommendation on the Ethics of AI takes a broader view, defining AI systems as “information-processing
technologies that integrate models and algorithms to produce capabilities for
learning and performing cognitive tasks, leading to outcomes like prediction
and decision-making in material and virtual settings.” Here, the focus shifts
to information processing, learning, and varying degrees of autonomy.
The European Union broke new ground with the first legally binding definition in its AI Regulation (EU) 2024/1689, adopted on June 13, 2024. It defines an AI system as: “a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.” (article 3.1)
This definition emphasizes
machines, autonomy, and inference as the core of AI’s output generation.
Nationally, definitions vary widely. The U.S., in its 2020 National AI Initiative Act, mirrors the OECD, describing AI as “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments. Artificial intelligence systems use machine and human-based inputs to: (A) perceive real and virtual environments; (B) abstract such perceptions into models through analysis in an automated manner; and (C) use model inference to formulate options for information or action.” (article 3.3).
The UK’s 2023 AI White Paper “defines”
AI “by reference to the two characteristics that generate the need for a
bespoke regulatory response”, namely “adaptivity” and “autonomy” (section
3.2.1).
China lacks a unified legal
definition, but Shanghai’s 2022 AI Industry Regulation refers to AI as systems “of
theories, methods, technologies, and applications that uses computers and
computer-controlled machines to simulate, extend, and expand human
intelligence, perceive the environment, acquire knowledge, and use knowledge to
achieve optimal results.” (article 2).
Brazil has not yet passed a law on
AI. However, the Senate has already approved a bill on the use of AI. Article 2.1 defines
"Artificial intelligence system" as “computer system, with varying
degrees of autonomy, designed to infer and achieve a given set of objectives,
using approaches based on machine learning and/or logic and knowledge
representation, through input data from machines or humans, to produce
predictions, recommendations or decisions that can influence the virtual or
real environment”.
These definitions share some common
threads—autonomy, machine-based systems, and outputs like predictions—but
diverge on key details. Some mention specific technologies like machine
learning, while others focus on hardware versus software or the scope of
autonomy and outputs.
Why does this heterogeneity matter? Because it is the starting point to build an AI shared
definition, which is necessary for tackling pressing issues such as AI’s safety,
impact on human rights, humanitarian law, sustainable development, and
environmental protection, among others.
As we look to the future, one thing
is clear: regulating and governing AI is the most urgent challenge facing
international law today. A unified definition is our first step toward ensuring
AI serves humanity ethically and sustainably.