Are we humans holding Artificial Intelligence responsible for attitudes that are actually our gaps?

Explore how human-machine systems influence competition, cooperation, contagion, and decision-making, revealing patterns in collective social outcomes.

High-frequency trading algorithms enhance market efficiency but may amplify volatility and reduce diversity, risking financial stability.

Advancing human-machine research with agent-based models, ethical experiments, and a sociological perspective before AI complexity outpaces understanding.

AI design must enhance trust, adapt to cultural contexts, and embrace complexity for resilient, ethical, and efficient human-machine systems.

Machines enhance human systems with efficiency and persistence but can also trigger instability, confusion, and polarization in social interactions.

Exploring human-machine social systems to address challenges like misinformation, market crashes, and road safety for a better, connected future.

Explore the dynamics of human-machine interactions, highlighting behavioral patterns, cognitive biases, and the evolving social roles of autonomous machines.

AI policy must prioritize diversity, resilience, and human-machine co-evolution to prevent systemic risks and adapt to cultural and societal changes.