| Recurring |
one_organization |
(a) The software failure incident related to the anti-shoplifting technology used at Walmart's self-checkout kiosks has happened again within the same organization. The system, designed by Everseen, has faced continuous complaints from Walmart employees since its implementation in 2017. The employees have raised concerns about the system's flaws, including frequent failures to identify unscanned items and incorrectly identifying personal items potentially shoplifted [100945].
(b) The software failure incident related to the Everseen system used at Walmart's self-checkout kiosks has not been reported to have happened at other organizations. The focus of the articles is primarily on the concerns raised by Walmart employees regarding the flaws and failures of the anti-shoplifting technology within Walmart stores [100945]. |
| Phase (Design/Operation) |
design, operation |
(a) The software failure incident related to the design phase can be seen in the article where it mentions that the anti-theft technology system at Walmart's self-checkout kiosks, developed by Everseen, had flaws in its design. The system, which relied on overhead cameras and cloud-based object detection AI, frequently failed to identify unscanned items and incorrectly flagged personal items as potentially shoplifted. This design flaw led to frequent false positives, irritating customers, and putting workers at risk of COVID-19 exposure [100945].
(b) The software failure incident related to the operation phase is evident in the article where it describes how the Everseen system, during operation at Walmart stores, had issues with accurately detecting scanned items. For example, the system failed to detect when a customer stacked two items on top of each other and scanned only one barcode, or when a customer moved two items across the scanner with one hand, causing only one barcode to register due to the viewing angle. These operational issues led to the system flagging items incorrectly, requiring manual intervention by Walmart workers to resolve the discrepancies [100945]. |
| Boundary (Internal/External) |
within_system, outside_system |
(a) within_system: The software failure incident related to the anti-shoplifting technology used at Walmart's self-checkout kiosks can be categorized as a within_system failure. The flaws in the system, such as frequent failures to identify unscanned items and incorrectly identifying personal items potentially shoplifted, were attributed to the system's design and functionality [100945].
(b) outside_system: The incident also involved factors originating from outside the system, such as the impact on customers and workers. The group of anonymous Walmart workers raised concerns about the system's frequent false positives irritating customers and putting workers at greater risk of COVID-19 exposure by unnecessarily having to verify customer's purchases at unsafe distances [100945]. |
| Nature (Human/Non-human) |
non-human_actions, human_actions |
(a) The software failure incident occurring due to non-human actions:
The software failure incident in the Walmart self-checkout system was primarily due to flaws in the anti-shoplifting technology provided by Everseen, an AI and technology firm. The system, which relied on overhead cameras and a cloud-based object detection AI, had inherent flaws that led to frequent false positives and failures to accurately identify scanned items. These issues were non-human in nature, stemming from the design and functionality of the technology itself [100945].
(b) The software failure incident occurring due to human actions:
While the primary cause of the software failure incident was related to the flaws in the anti-shoplifting technology, there were instances where human actions contributed to the failures. For example, customers were able to trick the system by stacking items or manipulating the scanning process in a way that the AI system couldn't accurately detect. These actions by customers exploited the limitations of the technology, leading to further failures in the system [100945]. |
| Dimension (Hardware/Software) |
hardware, software |
(a) The software failure incident related to hardware:
- The article mentions that the anti-shoplifting technology used at Walmart's self-checkout kiosks relies on overhead cameras, or 'digital eyes,' to film customers as they scan objects into the register [Article 100945].
- The system uses overhead cameras connected to a cloud-based object detection AI to identify individual items and flag customers who may not have properly scanned an item [Article 100945].
- The previous antitheft system at Walmart's self-checkout used sensors to compare the weight of the goods in a customer's bag against a tally of what it should have weighed based on what had been scanned in [Article 100945].
(b) The software failure incident related to software:
- The article highlights that the group of anonymous Walmart workers raised concerns about the flaws in the anti-shoplifting technology, including frequent failures to identify unscanned items and incorrectly identifying personal items potentially shoplifted [Article 100945].
- The employees documented several system failures and easy ways to trick the AI system, indicating software-related issues in the technology [Article 100945].
- Walmart acknowledged that significant improvements had been made to the Everseen system in early 2020, leading to a drop in overall alert levels in the 2,000 stores using the technology, suggesting software updates were necessary to address the failures [Article 100945]. |
| Objective (Malicious/Non-malicious) |
non-malicious |
(a) The software failure incident described in the articles does not appear to be malicious. The issues with the anti-shoplifting technology used at Walmart's self-checkout kiosks seem to stem from flaws in the system's design and functionality rather than any intentional actions to harm the system. The concerns raised by the anonymous Walmart employees and the documented system failures point towards non-malicious factors contributing to the software failure incident [100945]. |
| Intent (Poor/Accidental Decisions) |
poor_decisions, unknown |
(a) The intent of the software failure incident related to poor_decisions:
- The software failure incident related to poor decisions is evident in the implementation of the Everseen anti-theft technology at Walmart's self-checkout kiosks. The system, designed by Everseen, has been plagued with flaws and frequent false positives, leading to customer and employee dissatisfaction [100945].
- The decision to rely on the Everseen system, which has been criticized by Walmart employees as being faulty and ineffective, can be considered a poor decision that contributed to the software failure incident [100945].
(b) The intent of the software failure incident related to accidental_decisions:
- The software failure incident related to accidental decisions is not explicitly mentioned in the articles. |
| Capability (Incompetence/Accidental) |
development_incompetence |
(a) The software failure incident related to development incompetence is evident in the case of the anti-shoplifting technology used at Walmart's self-checkout kiosks. The system, designed by Everseen, has been reported to have flaws and frequent failures, including incorrectly identifying personal items as potentially shoplifted and failing to properly detect unscanned items [100945].
(b) The software failure incident related to accidental factors is seen in the examples provided by Walmart employees demonstrating how the Everseen system could be tricked or failed to accurately detect items during the self-checkout process. For instance, the system failed to detect two stacked Reese's White Peanut Butter Cups packages as separate items and mistook two one-gallon containers of milk moved together as a single object due to the viewing angle [100945]. |
| Duration |
temporary |
The software failure incident related to the anti-shoplifting technology used at Walmart's self-checkout kiosks can be categorized as a temporary failure. The incident was temporary because the flaws in the system, such as frequent failures to identify unscanned items and incorrectly identifying personal items potentially shoplifted, were due to contributing factors introduced by certain circumstances, such as the design and implementation of the Everseen system by the company Everseen [100945]. Additionally, Walmart acknowledged that significant improvements had been made to the Everseen system in early 2020, leading to a drop in overall alert levels in the 2,000 stores using the technology, indicating that the failure was not permanent and could be addressed through updates and enhancements to the system [100945]. |
| Behaviour |
crash, omission, value, other |
(a) crash: The software failure incident in the articles can be categorized as a crash. The system experienced failures that led to it not performing its intended functions. For example, the system would lock up when it flagged an unscanned item, requiring a Walmart worker to intervene and investigate further [100945].
(b) omission: The software failure incident can also be classified as an omission. The system omitted to perform its intended functions correctly at instances, such as failing to identify unscanned items or incorrectly identifying personal items as potentially shoplifted [100945].
(c) timing: The timing of the software failure incident does not seem to be a significant factor based on the information provided in the articles.
(d) value: The software failure incident can be attributed to a failure in value. The system was performing its intended functions incorrectly, such as not detecting multiple items being scanned as one or flagging a smartphone as an unscanned item [100945].
(e) byzantine: The software failure incident does not exhibit characteristics of a byzantine failure based on the information provided in the articles.
(f) other: The software failure incident can be categorized as a combination of crash and omission behaviors, where the system crashed by locking up when an issue was detected and omitted to correctly identify scanned items, leading to incorrect behavior and customer inconvenience [100945]. |