AIP Human-Centered Automation is an approach to the development of balanced automation systems between a person and the software and technical environment, based on the methods and tools of the AIP Institute to maintain the skills of users in any conditions and extreme situations, and the development of human cognitive functions through continuous training.
AIP human-centered automation is an approach to the development of balanced automation systems between a person and the software and technical environment, based on the methods and tools of the AIP Institute to maintain the skills of users in any conditions and extreme situations, and the development of human cognitive functions through continuous training.
The global phenomenon of total automation in connection with the development of the level of development of neural networks (AI) is more manifested in the direction of partial or complete replacement of human activity with autonomously executed functions (autonomous modules). In most cases, the control automation used is executed in the background without direct control by the operator, thereby eliminating the human factor from the system. This type of automation can be considered as completely autonomous .
In this case, a person is no more than one of the subjects taken into account in the automation system, including with the use of AI, along with animals or static objects, which occasionally take into account individual parameters (weight, dimensions, speed of movement). It is obvious that with this approach, there is an effect of excessive automation, which leads to the occurrence of emergencies.
A report by the National Transportation Safety Board (US National Transportation Safety Board, 2017, about Tesla) concluded that the use of ubiquitous automation does not necessarily lead to an improvement in human life. Accidents with autopilots are one of the clearest examples of the results of the use of automation “because we can”, without due consideration of the human factor. An untrained driver expects more from a system called “autopilot ” than it can give, as a result of which unforeseen accidents and errors occur.
Questions about the use of autopilots and the level of automation are quite acute in aviation. The publication of the organization Safety Alerts for Operators (SAFO) reflected concern about autopilots: “The constant use of automatic flight systems does not strengthen the knowledge and skills of the pilot when performing manual flights.” Thus, there is a growing problem with the training of pilots and the loss of proper qualifications due to relaxation and not using skills in flight. An increase in the dependence of pilots on autopilots leads to a weakening of their ability to fly without it and can lead to critical consequences in cases of unforeseen outages (source: “Autopilot: An Accident” by JPS Hawkeye).
In vital areas, such as transport (cars, trains, planes), medicine (surgery), and even maintenance of nuclear power plants, the introduction of fully autonomous systems without due consideration of the human factor leads to a decrease in the vigilance of operators and the loss of their qualifications, which in turn adversely affects the behavior of such specialists in critical situations when there are failures in the automation systems.
Automation systems implemented using neural network technology are a “black box”, whose decision-making algorithms are incomprehensible to users (source: IBM AI Guidelines) Despite the achievement of high efficiency in solving problems, there is a significant disadvantage in the widespread use of such systems – the inability to extract (extract) knowledge suitable for transmission and even more so for training the user.
In this regard, we see a promising path for the development of automation as a human-oriented system, where algorithms built around human knowledge, skills, and physical abilities take into account the balance between the necessary computer control for rapid automation and the human desire for improvement when necessary.
The purpose of the human-oriented approach in automation  is primarily to increase efficiency, expand the capabilities and compensate for the limitations of the operator, and not to replace the functions performed by a person with autonomous intelligent systems.
Using the example of vehicle automation in this paradigm, the software will adjust the system to the skills and physical condition of a person, being at the same time a backup assistant capable of intercepting control in the event of an incident. This solution gives you the freedom to practice and train the driver. The vehicle becomes both a simulator and an extension of the functional capabilities of a person. Such a symbiosis, while reducing the delay factor of data exchange between a person and an AI system, possibly even through implants, will allow us to realize the original goal of automation – expanding the capabilities and compensating for the limitations of the operator, and not freeing from any activity with a decrease in cognitive functions as a result.
Such human-oriented automation is implemented through the introduction of continuous assessment and training processes. The AIP Institute has launched and is improving several initiatives that jointly create an ecosystem for human-centered automation. These initiatives are also aimed at solving acute issues that arise when creating automation systems, for example, solving the problem of skill reduction, which undermines the human skills that may be required in case of automation failures, and the difficulty of maintaining vigilance when user actions become less frequent.
 Mitchell, C. M. (1996). Human-Centered Automation: A Philosophy, Some Design Tenets, and Related Research. In Human Interaction with Complex Systems (pp. 377-381). Springer, Boston, MA.
 Abbott, K. H., & Schutte, P. C. (1989). Human-centered automation and AI-Ideas, insights, and issues from the Intelligent Cockpit Aids research effort.
Description of the concept of a human-centered approach to automation through continuous training
A person-oriented approach to the development of automation systems using continuous learning technologies provides rapid professional growth for student users (Personal Grow) right at the workplace and adaptation of interaction with the environment (Activity-Based Adaptation) based on user activity. This effect is achieved due to the balanced integration of the user-learner into a specialized software and hardware environment, and the built-in algorithms for evaluating and teaching AIP allow achieving the required productivity within the framework of the performed labor functions (Operator Expert Model).
The three main concepts used in the human-oriented approach based on AIP are described below.
Specialized software and hardware environment is a specialized software and hardware environment with automation elements involved in the work activity of a student user. Using the AIP algorithm with continuous evaluation allows you to integrate the user into the environment to expand its capabilities. When the user interacts with the environment, the system can adapt to the preferences, the psychophysiological state of the user, as well as the state of the environment based on personal cognitive data and connected data sources.
Iconographic model of the user-learner – the user-operator model is built based on cognitive data collected through the assessment and testing of the user in the map of cognigraphic data and his characteristics (not cognigraphic data). The personal dynamic map of the student user is constantly updated based on the methods of continuous assessment and testing built into the AIP algorithm, as well as the collected user activity when interacting with the environment. Built-in methodologies for working with memorizing information take into account forgetfulness and non-repeatable actions when analyzing a personal dynamic map when forming a training set of materials.
The iconographic model of the operator – The operator model is formed based on the experience of professionals and the best practices of experts presented in the ontology of cognitive data. Specialized templates of cognitive data containing a detailed description of all the necessary and specific actions, skills, and knowledge to perform the work function of the user-student.
Based on the comparison of the cognitive data of the expert model and the user model of the algorithm, the AIP forms personalized learning routes, focusing on the missing ones. This model is centrally updated in the system and broadcast to all operators to confirm their qualifications or teach new skills and knowledge included in the model.
Human-centered automation of AIP for vehicles
The AIP Institute, together with Electric Vehicle Automation S. R. L., is developing a comprehensive human-oriented automation solution based on AIP for multidisciplinary vehicle operators, integrated through special scenarios into the control and control automation system based on ADANEC Hardware Automation Scenarios.
Methods of assessment and training of AIP allow developing theoretical and practical knowledge, cognitive, psychomotor, and other physiological capabilities of a person necessary for labor actions.
A comprehensive solution based on a person-oriented AIP approach for multidisciplinary vehicle operators (drivers) includes:
- Adaptive scenarios of interaction with the system of continuous evaluation of the qualification of the operators based on the testing and the observed parameters (ride quality, successful orders, work with specialized equipment, etc.);
- Specialized cognigraphic data maps based on the best expert and practices in each area for the different roles of operators of vehicles, including operator-driver operator-courier, the remote operator control of the fleet, and other;
- The Cognigraph system for generating and working with personal dynamic maps of cognitive data for operators;
- Adaptive methods and educational materials implemented in AIPas to develop the knowledge and skills of the user (driver) within the framework of specialized cognitive data maps;
- Virtual assistant interfaces for interaction with the user through the training components of the system feedback in the form of visual or audio notifications (prompts), testing components;
- Processing and analysis of profile data are carried out both on the client device and a remote analytical service, depending on the complexity of the models and analysis methods used.