AF 447 as a Paradigmatic Accident: The Role of Automation on a Modern Airplane

AF 447 as a Paradigmatic Accident: The Role of Automation on a Modern Airplane

Antonio Chialastri (STASA, Italy)
Copyright: © 2019 |Pages: 27
DOI: 10.4018/978-1-5225-7709-6.ch006

Abstract

In this chapter, the author presents a human factors problem for automation: why, when, and how automation has been introduced in the aviation domain; what problems arise from different ways of operating; and the possible countermeasures to limit faulty interaction between humans and machines. This chapter is divided into parts: definition of automation, its advantages in ensuring safety in complex systems such as aviation; reasons for the introduction of on-board automation, with a quick glance at the history of accidents in aviation and the related safety paradigms; ergonomics: displays, tools, human-machine interaction emphasizing the cognitive demands in high tempo and complex flight situations; illustration of the AF 447 case, a crash happened in 2009, which causes are linked to faulty human-machine interaction.
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Introduction

Human error is considered the first cause of accident. Actually, as many safety scholars affirm, the human error is only an epiphenomenon. The real cause of accident is what induces the human error, such as human performances and limitations, poor teamwork, organizational pressures on crews to obtain unreasonable performances, faulty human-machine interaction and lately some psychological upset, where the pilots posed an intentional threat to the safety of flight.

Although humans could be considered somehow a threat to safety, they are also the main resource to cope with unexpected events, unruly technology, changing environment and uncodified system failures. System designers often conceive the human being as a superman or a robot. Actually, we have psychological limitations (constant or transient), physical constraints (due to ageing, fatigue, etc.), big differences among individuals, vices and emotions. All these elements are powerful sources of variability. This brings pros and cons. Variability is often considered something negative, because aviation is based on standard procedures. On the other hand, variability provides the needed flexibility to make the entire system resilient. Resilient systems are characterized by flexibility and robustness. Automation provides the latter characteristic. It is important to point out that we cannot achieve one alone, be it flexibility or robustness. Both are needed. The problem is how making this two necessary elements work together. The aim of this chapter is to show why, when and how automation has been introduced in the aviation domain, what problems arise from different ways of operating, and the possible countermeasures to limit faulty interaction between humans and machines. This chapter is divided into four main parts:

  • 1.

    Definition of automation, its advantages in ensuring safety in complex systems such as aviation;

  • 2.

    Reasons for the introduction of on-board automation, with a quick glance at the history of accidents in aviation and the related safety paradigms;

  • 3.

    Ergonomics: Displays, tools, human-machine interaction emphasizing the cognitive demands in high tempo and complex flight situations;

  • 4.

    Illustration of the AF 447 case, a crash happened in 2009, which causes are linked to faulty human-machine interaction.

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What Is Automation

According to a shared definition of automation, it may be defined in the following way: “Automation is the use of control systems and information technologies to reduce the need for human work in the production of goods and services”. Another plausible definition, well-suited the aviation domain, could be: “The technique of controlling an apparatus, a process or a system by means of electronic and/or mechanical devices that replaces the human organism in the sensing, decision-making and deliberate output” (Webster, 1981). The Oxford English Dictionary (1989) defines automation as:

  • 1.

    Automatic control of the manufacture of a product through a number of successive stages;

  • 2.

    The application of automatic control to any branch of industry or science;

  • 3.

    By extension, the use of electronic or mechanical devices to replace human labour.

According to Parasumaran and Sheridan, “Automation can be applied to four classes of functions:

  • 1.

    Information acquisition;

  • 2.

    Information analysis;

  • 3.

    Decision and action selection;

  • 4.

    Action implementation.

Key Terms in this Chapter

Feature Subset Selection: Procedure for reduction of data dimensionality with a goal to select the most relevant set of features for a given task trying not to sacrifice the performance.

Non-Myopic Attribute Evaluation: An attribute evaluation procedure which does not assume conditional independence of attributes but takes context into account. This allows proper evaluation of attributes which take part in strong interactions.

Ordered Attribute: An attribute with nominal, but ordered values, for example, increasing levels of satisfaction: low, medium, and high.

Evaluation of Ordered Attributes: For ordered attributes the evaluation procedure should take into account their double nature: they are nominal, but also behave as numeric attributes. So each value may have its distinct behavior, but values are also ordered and may have increasing impact.

Context of Attributes: In a given problem the related attributes, which interact in the description of the problem. Only together these attributes contain sufficient information for classification of instances. The relevant context may not be the same for all the instances in given problem.

Feature Weighting: Under the assumption that not all attributes (dimensions) are equally important feature weighting assigns different weights to them and thereby transforms the problem space. This is used in data mining tasks where the distance between instances is explicitly taken into account.

Attribute Evaluation: A data mining procedure which estimates the utility of attributes for given task (usually prediction). Attribute evaluation is used in many data mining tasks, for example in feature subset selection, feature weighting, feature ranking, feature construction, decision and regression tree building, data discretization, visualization, and comprehension.

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