A Framework of Human Systems Engineering. Группа авторов
and inference will increase. To overcome these and other challenges, SA platforms will increasingly incorporate AI to interpret information and ascertain relevance to the operator. As AI becomes increasingly pervasive, SA platforms must develop the capacity to explain nonhuman decision‐making processes and accommodate transparency and independent auditing.
The impact these changes will have on HSI concerns is equally complex. On one hand, the ability to develop and interpret derivative information will improve the efficiency of man–machine interfaces by focusing operators on the most pertinent information available. On the other, the use of AI and reliance on derivative datasets will increase the demand on auditors tasked with ensuring the SA platform is making reasoned decisions, without bias, and that traceability is maintained to primary data where appropriate.
Reference
1 Endsley, M.R. (2011). Designing for Situation Awareness. Boca Raton, FL: CRC Press, Inc.
Notes
1 1 https://www.researchgate.net/publication/285745823_A_model_of_inter_and_intra_team_situation_awareness_ Implications_for_design_training_and_measurement_New_trends_in_cooperative_activities_Understanding_system_dynamics_in_complex_environments.
2 2 https://www1.nyc.gov/site/nypd/about/about‐nypd/equipment‐tech/technology.page.
3 3 https://asia.nikkei.com/Business/China‐tech/China‐s‐sharp‐eyes‐offer‐chance‐to‐take‐surveillance‐ industry‐global.
4 4 https://www.prnewswire.com/news‐releases/the‐global‐video‐surveillance‐market‐is‐expected‐to‐grow‐over‐ 77‐21‐billion‐by‐2023‐808999313.html.
5 5 https://www.fbi.gov/file‐repository/cjis‐security‐policy‐v5_6_20170605.pdf.
6 6 https://doi.org/10.1145/3290605.3300768.
7 7 https://eur‐lex.europa.eu/eli/reg/2016/679/oj.
8 8 https://leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_id=201720180AB375.
9 9 http://www.ilga.gov/legislation/ilcs/ilcs3.asp?ActID=3004&ChapterID=57.
10 10 https://www.securetechalliance.org/mobile‐drivers‐license‐initiative.
11 11 www.perpetuallineup.org.
3 Utilizing Artificial Intelligence to Make Systems Engineering More Human*
Philip S. Barry1 and Steve Doskey2
1 George Mason University, Fairfax, VA, USA
2 The MITRE Corporation, McLean, VA, USA
3.1 Introduction
Systems engineering (SE) can be broken into several major phases since its inception during World War II. These major phases can be binned into four epochs, where each ensuing epoch builds on the knowledge and insights of the previous epochs as shown in Figure 3.1.
Figure 3.1 Systems engineering evolution.
Epoch 1 began with the origins of SE being driven by the advent of large systems being developed such as the telephone system and operations research concepts employed during World War II. Epoch 2 picked up in the mid‐1940s as Bell Labs (Fagen, 1978), DoD, and universities begin to formalize engineering development processes. While great strides were made, SE remained a methodology to maintain control and enforce stability in large programs. Epoch 3 changed SE by introducing technology as a force multiplier allowing industry to build ever more powerful SE tools that extended and leveraged the traditional processes developed in Epoch 2.
SE is now entering Epoch 4 that will integrate artificial intelligence (AI) and sociotechnical integration into the development and deployment of systems and systems of systems. AI has permeated our society in such diverse areas: improving mobile phone reception, spam filters, ride‐sharing apps, autopilot systems, and fighting fraud. Epoch 4 will integrate AI, not only in the deployed systems, but change how we engineer these systems as part of human systems engineering (HSE). Furthermore, AI coupled with advances in sociotechnical system design will change SE tools and methods used to develop systems.
There is increasing recognition of the importance of considering stakeholders as part of the system development ecosystem. Epoch 4 explicitly recognizes the “human” as a part of the system and requires development environments to take into consideration human behavior and humans’ proclivity for making choices not aligned with traditional statistical optimization. As the development of system capabilities evolve, it is apparent that a static interpretation of human integration into the system is insufficient. AI will enable real‐time interpretation of continuous human integration and will be interdependent, collaborative, and cooperate between people and systems (Kevin Reilly, 2020). To both identify existing risks and project into the future, AI‐based models mimicking human behavior and learning will be necessary, as part of the development ecosystem. As HSE matures, these models will be a common component of HSE to design and build highly adaptive and resilient systems needed to keep pace with the velocity of change in business. This chapter presents a framework for defining and identifying the elements for the next evolution of SE, Epoch 4.
3.2 Changing Business Needs Drive Changes in Systems Engineering
SE, and more recently HSE, seeks to deliver successful systems that realize programs’ targeted outcomes and the value derived from realizing those outcomes. Early in the evolution of SE, complexity drove the need for stability and control in engineering practices in order to reduce development risks and improve quality in operation that led to the definition of a normative set of processes,