Immersed in automation, many choices we make include some form of a computationally-modeled process.
However, todays AI systems go beyond imposing suggestions andknow pretty well what we do and what we want.
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Misuse and disuse of AI both bring an additional set of technical challenges for establishing trustworthy human-machine interaction.
Trust plays a significant role in decreasing the cognitive complexity users face in interacting with sophisticated technology.
Consequently, its absence leads to an AI models underutilization or abandonment.
Regulating trust comes intuitively with grasping the learning processusing interpretability as its measure.
However, introducing feedback from both humans and machines increases the complexity of the mentioned challenges.
The process becomes even more complex with the introduction of potential user types that could manipulate the machine.
Users must be able to easily understand an AIs performance to make it assess its ability.
Conflicting situations are poorly resolved due to unreliable human-machine interactions.
Giving visible effort by the machine could indicate that it is acting in the interest of the user.
Such positive behavior of the automated system could be easily understood with visualization which in turn might increase trust.
As visualization enhances the comprehension, it may influence perceived functionality and reliability of complex systems.
Visualization reduces cognitive information overload and provides better insight into complex functioning.
Moreover, communicating risks facilitates credibility and perception on trustworthiness.
However, visualizing stages of machines inner processes is not sufficient for its full understanding.
Using interactive visualizations in machine learning enables direct and immediate outputgenerating effective visual feedback during the learning process.
The idea behind promotinghuman-machine symbiosisis not to train automation and replace some of our activities.
Actions have been taken in that direction and platforms such asArchspikeare working on providing qualitative human-machine feedback.
The platform understands users intentions and how that knowledge changes with consecutive human input over time.
Macaque introduces self performance-improving by employing both human and AI capacities.
With time, operators get regulated and less biased results based on the multiple synchronized end-user input.
The future environment and its vitality will depend on the ability to use intelligent applications and systems thinking.
AI architects have to understand the workings of automated systems so that develop effective feedback and increase model performance.
Multiple synchronized or unsynchronized flows of information need to be integrated into efficient bidirectional loops.
The central aspect of every process are human cognitive functions and its further development using automation.
AI systems should support objective rational thinking and engage and motivate users instead of imposing recommendations.
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