As artificially intelligent systems become more anthropomorphic and
pervasive, and their potential impact on humanity more urgent, discussions
about the possibility of machine consciousness have significantly intensified,
and it is sometimes seen as 'the holy grail'. Many concerns have been voiced
about the ramifications of creating an artificial conscious entity. This is
compounded by a marked lack of consensus around what constitutes consciousness
and by an absence of a universal set of criteria for determining consciousness.
By going into depth on the foundations and characteristics of consciousness, we
propose five criteria for determining whether a machine is conscious, which can
also be applied more generally to any entity. This paper aims to serve as a
primer and stepping stone for researchers of consciousness, be they in
philosophy, computer science, medicine, or any other field, to further pursue
this holy grail of philosophy, neuroscience and artificial intelligence.

人工智能系统、机器意识、意识基础、判定意识的标准以及哲学、神经科学和人工智能的圣杯是该研究论文的五个关键词和总结。

机器能有意识吗？通向机器意识的普适标准

Can a Machine be Conscious? Towards Universal Criteria for Machine  Consciousness

Humans frequently make decisions with the aid of artificially intelligent
(AI) systems. A common pattern is for the AI to recommend an action to the
human who retains control over the final decision. Researchers have identified
ensuring that a human has appropriate reliance on an AI as a critical component
of achieving complementary performance. We argue that the current definition of
appropriate reliance used in such research lacks formal statistical grounding
and can lead to contradictions. We propose a formal definition of reliance,
based on statistical decision theory, which separates the concepts of reliance
as the probability the decision-maker follows the AI's prediction from
challenges a human may face in differentiating the signals and forming accurate
beliefs about the situation. Our definition gives rise to a framework that can
be used to guide the design and interpretation of studies on human-AI
complementarity and reliance. Using recent AI-advised decision making studies
from literature, we demonstrate how our framework can be used to separate the
loss due to mis-reliance from the loss due to not accurately differentiating
the signals. We evaluate these losses by comparing to a baseline and a
benchmark for complementary performance defined by the expected payoff achieved
by a rational agent facing the same decision task as the behavioral agents.

通过提出一种统计决策理论为基础的可靠性的正式定义，将研究中对合理依赖的定义与人工智能系统准确预测的信号区分和形成准确信念所面临的挑战分离，为人工智能与人类之间的互补性研究提供了一个可以指导设计和解释的框架。