{'fraction_non_alphanumeric': 0.017453798767967144, 'fraction_numerical': 0.0, 'mean_word_length': 5.210191082802548, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Reducing False Alarms in Automated Target Recognition by Lowering the Level of Automation

Automated target recognition (ATR) technologies are designed to help operators detect and identify potential threats. However, ATRs can generate a high number of false alarms (FA), resulting in low operator trust and potential automation disuse. In this study, we examined whether lowering the level of automation (LOA) of ATR could reduce FAs and support automation compliance and reliance. Participants performed a visual search for a target with or without the help of ATR. The ATR operated at three LOAs and two levels of reliability. The results suggested that the lowest LOA resulted in greater automation compliance and reliance. Operators also performed better in one measure of diagnostic accuracy under the low automation condition. The findings suggest that operating ATR at lower LOAs provide an effective method for improving ATR use and for detecting potential threats.