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At least 253 records · Page 14

Learning About Cockpit Automation: From Piston Trainer to Jet Transport

Two experiments explored the idea of providing cockpit automation training to airline-bound student pilots using cockpit automation equipment commonly found in small training airplanes. In a first experiment, pilots mastered a set of tasks and maneuvers using a GPS navigation computer, autopilot, and flight director system installed in a small training airplane Students were then tested on their ability to complete a similar set of tasks using the cockpit automation system found in a popular jet transport aircraft. Pilot were able to successfully complete 77% of all tasks in the jet transport on their first attempt. An analysis of a control group suggests that the pilot's success was attributable to the application of automation principles they had learned in the small airplane. A second experiment looked at two different ways of delivering small-aeroplane cockpit automation training: a self-study method, and a dual instruction method. The results showed a slight advantage for the self-study method. Overall, the results of the two studies cast a strong vote for the incorporation of cockpit automation training in curricula designed for pilot who will later transition to the jet fleet.

Casner, Stephen M.↗

Science Goal Monitor: Science Goal Driven Automation for NASA Missions

Infusion of automation technologies into NASA s future missions will be essential because of the need to: (1) effectively handle an exponentially increasing volume of scientific data, (2) successfully meet dynamic, opportunistic scientific goals and objectives, and (3) substantially reduce mission operations staff and costs. While much effort has gone into automating routine spacecraft operations to reduce human workload and hence costs, applying intelligent automation to the science side, i.e., science data acquisition, data analysis and reactions to that data analysis in a timely and still scientifically valid manner, has been relatively under-emphasized. In order to introduce science driven automation in missions, we must be able to: capture and interpret the science goals of observing programs, represent those goals in machine interpretable language; and allow spacecrafts onboard systems to autonomously react to the scientist's goals. In short, we must teach our platforms to dynamically understand, recognize, and react to the scientists goals. The Science Goal Monitor (SGM) project at NASA Goddard Space Flight Center is a prototype software tool being developed to determine the best strategies for implementing science goal driven automation in missions. The tools being developed in SGM improve the ability to monitor and react to the changing status of scientific events. The SGM system enables scientists to specify what to look for and how to react in descriptive rather than technical terms. The system monitors streams of science data to identify occurrences of key events previously specified by the scientist. When an event occurs, the system autonomously coordinates the execution of the scientist s desired reactions. Through SGM, we will improve om understanding about the capabilities needed onboard for success, develop metrics to understand the potential increase in science returns, and develop an operational prototype so that the perceived risks associated with increased use of automation can be reduced.

Koratkar, Anuradha↗

Intelligent Automation Approach for Improving Pilot Situational Awareness

Automation in the aviation domain has been increasing for the past two decades. Pilot reaction to automation varies from highly favorable to highly critical depending on both the pilot's background and how effectively the automation is implemented. We describe a user-centered approach for automation that considers the pilot's tasks and his needs related to accomplishing those tasks. Further, we augment rather than replace how the pilot currently fulfills his goals, relying on redundant displays that offer the pilot an opportunity to build trust in the automation. Our prototype system automates the interpretation of hydraulic system faults of the UH-60 helicopter. We describe the problem with the current system and our methodology for resolving it.

Spirkovska, Lilly↗

Automated Conflict Resolution For Air Traffic Control

The ability to detect and resolve conflicts automatically is considered to be an essential requirement for the next generation air traffic control system. While systems for automated conflict detection have been used operationally by controllers for more than 20 years, automated resolution systems have so far not reached the level of maturity required for operational deployment. Analytical models and algorithms for automated resolution have been traffic conditions to demonstrate that they can handle the complete spectrum of conflict situations encountered in actual operations. The resolution algorithm described in this paper was formulated to meet the performance requirements of the Automated Airspace Concept (AAC). The AAC, which was described in a recent paper [1], is a candidate for the next generation air traffic control system. The AAC's performance objectives are to increase safety and airspace capacity and to accommodate user preferences in flight operations to the greatest extent possible. In the AAC, resolution trajectories are generated by an automation system on the ground and sent to the aircraft autonomously via data link .The algorithm generating the trajectories must take into account the performance characteristics of the aircraft, the route structure of the airway system, and be capable of resolving all types of conflicts for properly equipped aircraft without requiring supervision and approval by a controller. Furthermore, the resolution trajectories should be compatible with the clearances, vectors and flight plan amendments that controllers customarily issue to pilots in resolving conflicts. The algorithm described herein, although formulated specifically to meet the needs of the AAC, provides a generic engine for resolving conflicts. Thus, it can be incorporated into any operational concept that requires a method for automated resolution, including concepts for autonomous air to air resolution.

Erzberger, Heinz↗

Transforming Our SMEX Organization by Way of Innovation, Standardization, and Automation

NASA's Small Explorer (SMEX) Flight Operations Team (FOT) is currently tackling the challenge of supporting ground operations for several satellites that have surpassed their designed lifetime and have a dwindling budget. At Goddard Space Flight Center (GSFC), these missions are presently being reengineered into a fleet-oriented ground system. When complete, this ground system will provide command and control of four SMEX missions, and will demonstrate fleet automation and control concepts as a pathfinder for additional mission integrations. A goal of this reengineering effort is to demonstrate new ground-system technologies that show promise of supporting longer mission lifecycles and simplifying component integration. In pursuit of this goal, the SMEX organization has had to examine standardization, innovation, and automation. A core technology being demonstrated in this effort is the GSFC Mission Services Evolution Center (GMSEC) architecture. The GMSEC architecture focuses on providing standard interfaces for ground system applications to promote application interoperability. Building around commercial Message Oriented Middleware and providing a common messaging standard allows GMSEC to provide the capabilities necessary to support integration of new software components into existing missions and increase the level of interaction within the system. For SMS, GMSEC has become the technology platform to transform flight operations with the innovation and automation necessary to reduce operational costs. The automation technologies supported in SMEX are built upon capabilities provided by the GMSEC architecture that allows the FOT to further reduce the involvement of the console, operator. Initially, SMEX is automating only routine operations, such as safety and health monitoring, basic commanding, and system recovery. The operational concepts being developed here will reduce the need for staffed passes and are a necessity for future fleet management. As this project continues to evolve, additional innovations beyond GMSEC and automation have, and will continue to be developed. The team developed techniques for migrating ground systems of existing on-orbit assets. The tools necessary to monitor and control software failures were integrated and tailored for operational environments. All this was done with a focus of extending fleet operations to mission beyond SMU. The result of this work is the foundation for a broader fleet-capable ground system that will include several missions supported by the Space Science Mission Operations Project.

Madden, Maureen↗

The Orion GN and C Data-Driven Flight Software Architecture for Automated Sequencing and Fault Recovery

The Orion Crew Exploration Vehicle (CET) is being designed to include significantly more automation capability than either the Space Shuttle or the International Space Station (ISS). In particular, the vehicle flight software has requirements to accommodate increasingly automated missions throughout all phases of flight. A data-driven flight software architecture will provide an evolvable automation capability to sequence through Guidance, Navigation & Control (GN&C) flight software modes and configurations while maintaining the required flexibility and human control over the automation. This flexibility is a key aspect needed to address the maturation of operational concepts, to permit ground and crew operators to gain trust in the system and mitigate unpredictability in human spaceflight. To allow for mission flexibility and reconfrgurability, a data driven approach is being taken to load the mission event plan as well cis the flight software artifacts associated with the GN&C subsystem. A database of GN&C level sequencing data is presented which manages and tracks the mission specific and algorithm parameters to provide a capability to schedule GN&C events within mission segments. The flight software data schema for performing automated mission sequencing is presented with a concept of operations for interactions with ground and onboard crew members. A prototype architecture for fault identification, isolation and recovery interactions with the automation software is presented and discussed as a forward work item.

King, Ellis↗

ISS Operations Cost Reductions Through Automation of Real-Time Planning Tasks

In 2008 the Johnson Space Center s Mission Operations Directorate (MOD) management team challenged their organization to find ways to reduce the costs of International Space station (ISS) console operations in the Mission Control Center (MCC). Each MOD organization was asked to identify projects that would help them attain a goal of a 30% reduction in operating costs by 2012. The MOD Operations and Planning organization responded to this challenge by launching several software automation projects that would allow them to greatly improve ISS console operations and reduce staffing and operating costs. These projects to date have allowed the MOD Operations organization to remove one full time (7 x 24 x 365) ISS console position in 2010; with the plan of eliminating two full time ISS console support positions by 2012. This will account for an overall 10 EP reduction in staffing for the Operations and Planning organization. These automation projects focused on utilizing software to automate many administrative and often repetitive tasks involved with processing ISS planning and daily operations information. This information was exchanged between the ground flight control teams in Houston and around the globe, as well as with the ISS astronaut crew. These tasks ranged from managing mission plan changes from around the globe, to uploading and downloading information to and from the ISS crew, to even more complex tasks that required multiple decision points to process the data, track approvals and deliver it to the correct recipient across network and security boundaries. The software solutions leveraged several different technologies including customized web applications and implementation of industry standard web services architecture between several planning tools; as well as a engaging a previously research level technology (TRL 2-3) developed by Ames Research Center (ARC) that utilized an intelligent agent based system to manage and automate file traffic flow, archiving f data, and generating console logs. This technology called OCAMS (OCA (Orbital Communication System) Management System), is now considered TRL level 9 and is in daily use in the Mission Control Center in support of ISS operations. These solutions have not only allowed for improved efficiency on console; but since many of the previously manual data transfers are now automated, many of the human error prone steps have been removed, and the quality of the planning products has improved tremendously. This has also allowed our Planning Flight Controllers more time to focus on the abstract areas of the job, (like the complexities of planning a mission for 6 international crew members with a global planning team), instead of being burdened with the administrative tasks that took significant time each console shift to process. The resulting automation solutions have allowed the Operations and Planning organization to realize significant cost savings for the ISS program through 2020 and many of these solutions could be a viable

Hall, Timothy A.↗

Automation of Vapor-Diffusion Growth of Protein Crystals

Some improvements have been made in a system of laboratory equipment developed previously for studying the crystallization of proteins from solution by use of dynamically controlled flows of dry gas. The improvements involve mainly (1) automation of dispensing of liquids for starting experiments, (2) automatic control of drying of protein solutions during the experiments, and (3) provision for automated acquisition of video images for monitoring experiments in progress and for post-experiment analysis. The automation of dispensing of liquids was effected by adding an automated liquid-handling robot that can aspirate source solutions and dispense them in either a hanging-drop or a sitting-drop configuration, whichever is specified, in each of 48 experiment chambers. A video camera of approximately the size and shape of a lipstick dispenser was added to a mobile stage that is part of the robot, in order to enable automated acquisition of images in each experiment chamber. The experiment chambers were redesigned to enable the use of sitting drops, enable backlighting of each specimen, and facilitate automation.

Hamrick, David T.↗

Automated Test Case Generation for an Autopilot Requirement Prototype

Designing safety-critical automation with robust human interaction is a difficult task that is susceptible to a number of known Human-Automation Interaction (HAI) vulnerabilities. It is therefore essential to develop automated tools that provide support both in the design and rapid evaluation of such automation. The Automation Design and Evaluation Prototyping Toolset (ADEPT) enables the rapid development of an executable specification for automation behavior and user interaction. ADEPT supports a number of analysis capabilities, thus enabling the detection of HAI vulnerabilities early in the design process, when modifications are less costly. In this paper, we advocate the introduction of a new capability to model-based prototyping tools such as ADEPT. The new capability is based on symbolic execution that allows us to automatically generate quality test suites based on the system design. Symbolic execution is used to generate both user input and test oracles user input drives the testing of the system implementation, and test oracles ensure that the system behaves as designed. We present early results in the context of a component in the Autopilot system modeled in ADEPT, and discuss the challenges of test case generation in the HAI domain.

Giannakopoulou, Dimitra↗

Varying Levels of Automation on UAS Operator Responses to Traffic Resolution Advisories in Civil Airspace

Continuing demand for the use of Unmanned Aircraft Systems (UAS) has put increasing pressure on operations in civil airspace. The need to fly UAS in the National Airspace System (NAS) in order to perform missions vital to national security and defense, emergency management, and science is increasing at a rapid pace. In order to ensure safe operations in the NAS, operators of unmanned aircraft, like those of manned aircraft, may be required to maintain separation assurance and avoid loss of separation with other aircraft while performing their mission tasks. This experiment investigated the effects of varying levels of automation on UAS operator performance and workload while responding to conflict resolution instructions provided by the Tactical Collision Avoidance System II (TCAS II) during a UAS mission in high-density airspace. The purpose of this study was not to investigate the safety of using TCAS II on UAS, but rather to examine the effect of automation on the ability of operators to respond to traffic collision alerts. Six licensed pilots were recruited to act as UAS operators for this study. Operators were instructed to follow a specified mission flight path, while maintaining radio contact with Air Traffic Control and responding to TCAS II resolution advisories. Operators flew four, 45 minute, experimental missions with four different levels of automation: Manual, Knobs, Management by Exception, and Fully Automated. All missions included TCAS II Resolution Advisories (RAs) that required operator attention and rerouting. Operator compliance and reaction time to RAs was measured, and post-run NASA-TLX ratings were collected to measure workload. Results showed significantly higher compliance rates, faster responses to TCAS II alerts, as well as less preemptive operator actions when higher levels of automation are implemented. Physical and Temporal ratings of workload were significantly higher in the Manual condition than in the Management by Exception and Fully Automated conditions.

Kenny, Caitlin↗

Work Practice Simulation of Complex Human-Automation Systems in Safety Critical Situations: The Brahms Generalized berlingen Model

The transition from the current air traffic system to the next generation air traffic system will require the introduction of new automated systems, including transferring some functions from air traffic controllers to on­-board automation. This report describes a new design verification and validation (V&V) methodology for assessing aviation safety. The approach involves a detailed computer simulation of work practices that includes people interacting with flight-critical systems. The research is part of an effort to develop new modeling and verification methodologies that can assess the safety of flight-critical systems, system configurations, and operational concepts. The 2002 Ueberlingen mid-air collision was chosen for analysis and modeling because one of the main causes of the accident was one crew's response to a conflict between the instructions of the air traffic controller and the instructions of TCAS, an automated Traffic Alert and Collision Avoidance System on-board warning system. It thus furnishes an example of the problem of authority versus autonomy. It provides a starting point for exploring authority/autonomy conflict in the larger system of organization, tools, and practices in which the participants' moment-by-moment actions take place. We have developed a general air traffic system model (not a specific simulation of Überlingen events), called the Brahms Generalized Ueberlingen Model (Brahms-GUeM). Brahms is a multi-agent simulation system that models people, tools, facilities/vehicles, and geography to simulate the current air transportation system as a collection of distributed, interactive subsystems (e.g., airports, air-traffic control towers and personnel, aircraft, automated flight systems and air-traffic tools, instruments, crew). Brahms-GUeM can be configured in different ways, called scenarios, such that anomalous events that contributed to the Überlingen accident can be modeled as functioning according to requirements or in an anomalous condition, as occurred during the accident. Brahms-GUeM thus implicitly defines a class of scenarios, which include as an instance what occurred at Überlingen. Brahms-GUeM is a modeling framework enabling "what if" analysis of alternative work system configurations and thus facilitating design of alternative operations concepts. It enables subsequent adaption (reusing simulation components) for modeling and simulating NextGen scenarios. This project demonstrates that BRAHMS provides the capacity to model the complexity of air transportation systems, going beyond idealized and simple flights to include for example the interaction of pilots and ATCOs. The research shows clearly that verification and validation must include the entire work system, on the one hand to check that mechanisms exist to handle failures of communication and alerting subsystems and/or failures of people to notice, comprehend, or communicate problematic (unsafe) situations; but also to understand how people must use their own judgment in relating fallible systems like TCAS to other sources of information and thus to evaluate how the unreliability of automation affects system safety. The simulation shows in particular that distributed agents (people and automated systems) acting without knowledge of each others' actions can create a complex, dynamic system whose interactive behavior is unexpected and is changing too quickly to comprehend and control.

complex systems↗

Supporting automation in spacecraft activity planning with simulation and visualization

Automation is gaining momentum in spacecraft operations, however, at a much slower pace than comparable application domains. The reasons behind slow adoption is (1) the need for high reliability and (2) the limited interaction between human operators and the automated systems. For automated systems to be adopted and trusted by humans, humans need to gain intuition about the decision making process of the automated system and trust in its execution [1]. In this paper, we present how simulation and visualization can enhance adoption of an automated on-board activity scheduling system, specifically in the context of Mars2020 rover mission[2]. The visualization aims to communicate to the users degree of variance and uncertainty in possible schedule execution. Our preliminary validation results suggest that the proposed visualization increases operators’ confidence in—and likelihood of adopting—the automated scheduling system.

Chien, Steve↗

Validation of an Automated System for Arrival Traffic Management

The fuel-efficiencies of arrival flights that were managed by an automated system were compared to the fuel-efficiencies of arrival flights that were managed by air traffic controllers. It was infeasible to have the automated system control arrivals in real operations, so the comparison was accomplished by setting up a fast-time simulation where the automated system could manage arrivals with the same initial conditions and flight plans as those that operated in real operations during a selected comparison period and in the same background traffic. For this study, Newark Liberty International Airport was selected as the arrival airport because its high traffic load and constrained arrival procedures were expected to highlight fuel-efficiency benefits of an automated system. In the simulation, the automated system managed Newark arrivals, and the other flights (arrivals to other airports, departures, and overflights) composed the background traffic. To match the simulation and the real operations background traffic, the other flights flew in simulation the same trajectory that they flew in real operations during the comparison period. Fuel-efficiency was measured by calculating fuel burns of the arrival trajectories. The fuel-efficiencies of arrival trajectories produced in the simulation were compared with the estimated fuel-efficiencies of arrival trajectories recorded from real operations during the comparison period. Results showed that automation managed arrivals burned 346 lbs less fuel per flight on average than controller managed arrivals.

air traffic control↗

Validation of an Automated System for Arrival Traffic Management

The fuel-efficiencies of arrival flights that were managed by an automated system were compared to the fuel-efficiencies of arrival flights that were managed by air traffic controllers. It was infeasible to have the automated system control arrivals in real operations, so the comparison was accomplished by setting up a fast-time simulation where the automated system could manage arrivals with the same initial conditions and flight plans as those that operated in real operations during a selected comparison period and in the same background traffic. For this study, Newark Liberty International Airport was selected as the arrival airport because its high traffic load and constrained arrival procedures were expected to highlight fuel-efficiency benefits of an automated system. In the simulation, the automated system managed Newark arrivals, and the other flights (arrivals to other airports, departures, and overflights) composed the background traffic. To match the simulation and the real operations background traffic, the other flights flew in simulation the same trajectory that they flew in real operations during the comparison period. Fuel-efficiency was measured by calculating fuel burns of the arrival trajectories. The fuel-efficiencies of arrival trajectories produced in the simulation were compared with the estimated fuel-efficiencies of arrival trajectories recorded from real operations during the comparison period. Results showed that automation managed arrivals burned 346 lbs less fuel per flight on average than controller managed arrivals.

air traffic control↗

Automating Surface Attitude Positioning and Pointing Operations for Mars 2020

The Surface Attitude Positioning and Pointing (SAPP) subsystem of the Mars Perseverance rover keeps track of the rover’s position and attitude on the surface of Mars. The SAPP Downlink Engineering Operations team members receive data from the rover on a daily basis. They must interpret the data to make sure the rover is staying safe and to support uplink planning. The SAPP team keeps track of the error growth in the rover’s attitude estimate due to noise in the Rover Inertial Measurement Unit’s (RIMU) gyroscopes used to propagate that attitude estimate whenever the rover is moving. Whenever this error grows to a particular threshold, SAPP is responsible for updating the onboard attitude knowledge using the RIMU’s accelerometers to estimate rover roll and pitch and sun imaging to estimate rover yaw, thereby reducing this attitude estimation error. Accurate attitude estimation is required so that the rover can successfully point its High Gain Antenna (HGA) to receive information from Earth and as a backup to the Mars orbiters used for sending data from the rover to Earth, point instruments on its Remote Sensing Mast (RSM), and support safe movement and placement of instruments by the rover’s ARM relative to the Martian surface. The Mars 2020 Engineering Operations team has been working to increase the operational efficiency of the mission and eventually move to a five-hour timeline for daily operations. In pursuit of this goal, the SAPP Engineering Operations team has automated their downlink process by developing a centralized Jupyter notebook to analyze the data received daily from the rover. The SAPP downlink Jupyter notebook automatically collects the data relevant to the SAPP subsystem and visualizes this information in plots and tables that can be easily read by downlink operators to aid them in assessing the status of the subsystem. Various Application Programming Interfaces (APIs) have been incorporated into the downlink daily notebook to automate the collection and posting of data, such as gathering and posting data products to the cloud. The SAPP team has also developed a SAPP downlink software library that includes functions to aid the notebook in processing data. In addition to assessing the SAPP subsystem on a daily basis, operators need to assess the long-term trending behavior of the subsystem over time. An automated trending process has been developed to collect information from the daily notebooks in order to plot and analyze that data in a centralized place. These daily and trending processes have expedited the SAPP downlink assessment and laid the groundwork to completely automate the SAPP downlink process so that SAPP operators are unnecessary unless something unexpected occurs. This paper will provide an overview of the functions that the SAPP subsystem carries out on a daily basis, and will then dive into the automations that have been developed for daily and trending downlink assessment. An assessment of the downlink efficiency will be provided, along with a summary of lessons learned and work to go. Finally, the authors will discuss how these types of automated spacecraft health assessments could be more broadly used within mission operations.

Zarifian, Anais↗

Physiological and Subjective Responses of Pilots during Advanced Air Mobility Flight Testing with Automated Systems

Aviation is constantly evolving, mostly due to the integration of automated systems into the National Airspace System. This presents a host of challenges and opportunities. NASA’s Advanced Air Mobility (AAM) project has taken a significant leap forward with a research flight test led by the Integration of Automated Systems (IAS) sub-project. This effort focused on assessing automated flight deck algorithms essential for supporting high-density Urban Air Mobility (UAM) operations. Carrying out a two-ship flight test in UAM Maturity Level 4 scenarios, the team evaluated state-of-the-art algorithms, including Hazard Perception and Avoidance (HPA) and flight path management (FPM) systems. This paper investigates into pilots’ physiological and subjective responses during the flight scenarios conducted. In collaboration with Lockheed Martin Advanced Technology Laboratories (ATL), we collected and analyzed live-flight biometric data using eye tracking, mobile brain imaging, and heart rate sensors. The study provides insights into pilots’ workload and cognitive engagement while navigating automated tools, leveraging both biometric data and post-encounter subjective assessments. The research highlights the interaction between human operators and automated systems, contributing valuable lessons learned about gathering human data in live-flight environments. The knowledge acquired from this study enhances our understanding of human factors in automated flight and informs future studies attempting to undertake similar feats.

Kevin J. Monk↗

A Safety and Management Framework to Enable Automated Mobility Districts in Urban Areas

Automated mobility technology is beginning to emerge as a viable means to create sustainable and effective public mobility systems within denser urban environments. Automated mobility districts (AMDs) describe major urban districts or activity centers in which deployments of multiple automated vehicle (AV) transit and ride-hailing fleets are supported to meet public mobility needs. The authors put forward a framework to enable AMDs and their governing and management jurisdictional authorities to manage safety of AV operations based on lessons learned from the last century of automated guideway transit and roadway intersection traffic control systems. The essential concept is that of operational management and safety-critical control of multiple AV fleets using a “system-of-systems” approach to system safety analysis. The safety analysis would focus on safe passage of the AV fleet vehicles through complex roadway intersections and junctions, especially in the presence of other non-automated modes such as pedestrians and manually operated vehicles.

47 OTHER INSTRUMENTATION↗

Improving Automation Transparency: Addressing Some of Machine Learning’s Unique Challenges

Abstract. A variety of factors can affect one’s reliance on an automated aid. Some of these factors include one’s per-ception of the system’s trustworthiness, such as perceived reliability of the system or one’s ability to understand the system’s underlying reasoning. A mismatch between the operator’s perception and the true capabilities and charac-teristics of the system can lead to inappropriate reliance on the tool. This improper use of the system can manifest as either underutilization of the technology or complacency resulting from over-trusting the system. Increasing an au-tomated tool’s transparency is one approach that enables the operator to more appropriately rely on the technology. Transparent automated systems provide additional infor-mation that allows the user to see the system’s intent and understand its underlying processes and capabilities. Sev-eral researchers have developed frameworks to support the design of more transparent automation. However, these frameworks may not fully consider the particular challeng-es to transparency design introduced by automation that leverages machine learning. Like all automation, these systems can benefit from transparency. However, artificial intelligence poses new challenges that must be considered when designing for transparency. Unique considerations must be made in terms of the type, and amount or level of transparency information conveyed to the user.

Transparency, Machine Learning, Automation↗