Aïda Elamrani

With the significant progress of artificial intelligence (AI) and consciousness science, artificial consciousness (AC) has recently gained popularity. This work provides a broad overview of the main topics and current trends in AC. The first part traces the history of this interdisciplinary field to establish context and clarify key terminology, including the distinction between Weak and Strong AC. The second part examines major trends in AC implementations, emphasising the synergy between Global Workspace and Attention Schema, as well as the problem of evaluating the internal states of artificial systems. The third part analyses the ethical dimension of AC development, revealing both critical risks and transformative opportunities. The last part offers recommendations to guide AC research responsibly, and outlines the limitations of this study as well as avenues for future research. The main conclusion is that while AC appears both indispensable and inevitable for scientific progress, serious efforts are required to address the far-reaching impact of this innovative research path.

The accelerating advances in the fields of neuroscience, artificial intelligence, and robotics have been garnering interest and raising new philosophical, ethical, or practical questions that depend on whether or not there may exist a scientific method of probing consciousness in machines. This paper provides an analytic review of the existing tests for machine consciousness proposed in the academic literature over the past decade, and an overview of the diverse scientific communities involved in this enterprise. The tests put forward in their work typically fall under one of two grand categories: architecture and behaviour. Each category has its strengths and weaknesses. Architecture tests' main advantage is that they could apparently test for qualia, a feature that has been receiving increasing attention in recent years. Behaviour tests are more synthetic and more practicable, but give a stronger role to ex post human interpretation of behaviour. We show how some disciplines and places have affinities towards certain type of tests, and which tests are more influential according to scientometric indicators.