Jacob Davis, A. Atchley, Hannah Smitherman, Hailey Simon, N. Tenhundfeld
{"title":"Measuring Automation Bias and Complacency in an X-Ray Screening Task","authors":"Jacob Davis, A. Atchley, Hannah Smitherman, Hailey Simon, N. Tenhundfeld","doi":"10.1109/SIEDS49339.2020.9106670","DOIUrl":null,"url":null,"abstract":"Automation is becoming ever more prevalent in industrial system designs, and the aviation security industry is no exception. Automated decision aids are regularly used in airport security procedures (as with the TSA) to assist operators scanning baggage for hazardous items. However, there exists serious concerns regarding the human-machine interactions. In order to safely design systems that rely on human oversight, it is imperative that we understand the consequences of design on overall task performance and system usability. To do this, we combined an x-ray screening research paradigm with a ‘wizard-of-oz’ automation verification feature to create a novel research paradigm for exploring monitoring behavior (complacency) and performance in a simulated x-ray screening task. The automation in the x-ray task provided participants with a reliable recommendation to search (hazardous items detected) or clear (no hazardous weapons detected) the baggage 80% of the time. Users’ level of complacency was measured by registering the frequency with which they chose to verify the automation by clicking a “Request Info” button. Monitoring behavior, or the percent of trials in which the user requested additional information from the automation, was low overall. However, it was significantly higher when the automation provided an inaccurate recommendation. These results indicate that users experienced automation bias, the tendency to agree with an automated decision aid. Users also exhibited complacency during the task such that they were no longer actively monitoring the system. Users may have noticed the system was unreliable, given an increase in monitoring behavior in unreliable recommendation trials, but still chose to agree with the automation rather than visually search the baggage for evidence. This demonstrates a unique threat to safety in these domains, wherein users may rely on imperfect automation, rather than their own abilities, even when they believe something is amiss.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Systems and Information Engineering Design Symposium (SIEDS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIEDS49339.2020.9106670","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Automation is becoming ever more prevalent in industrial system designs, and the aviation security industry is no exception. Automated decision aids are regularly used in airport security procedures (as with the TSA) to assist operators scanning baggage for hazardous items. However, there exists serious concerns regarding the human-machine interactions. In order to safely design systems that rely on human oversight, it is imperative that we understand the consequences of design on overall task performance and system usability. To do this, we combined an x-ray screening research paradigm with a ‘wizard-of-oz’ automation verification feature to create a novel research paradigm for exploring monitoring behavior (complacency) and performance in a simulated x-ray screening task. The automation in the x-ray task provided participants with a reliable recommendation to search (hazardous items detected) or clear (no hazardous weapons detected) the baggage 80% of the time. Users’ level of complacency was measured by registering the frequency with which they chose to verify the automation by clicking a “Request Info” button. Monitoring behavior, or the percent of trials in which the user requested additional information from the automation, was low overall. However, it was significantly higher when the automation provided an inaccurate recommendation. These results indicate that users experienced automation bias, the tendency to agree with an automated decision aid. Users also exhibited complacency during the task such that they were no longer actively monitoring the system. Users may have noticed the system was unreliable, given an increase in monitoring behavior in unreliable recommendation trials, but still chose to agree with the automation rather than visually search the baggage for evidence. This demonstrates a unique threat to safety in these domains, wherein users may rely on imperfect automation, rather than their own abilities, even when they believe something is amiss.