| Recurring |
one_organization, multiple_organization |
(a) The software failure incident related to Google's Flu Trends system has happened again within the same organization. The system has been overestimating the number of influenza cases in the US for a significant period, indicating a recurring issue within Google's own product [25033].
(b) The software failure incident related to the flawed prediction model of Google's Flu Trends system serves as a broader lesson about the use of "big data" in forecasting. The incident highlights the challenges and potential pitfalls associated with relying on big data analytics for predictive purposes, not just limited to Google but applicable to other organizations as well [25033]. |
| Phase (Design/Operation) |
design, operation |
(a) The software failure incident related to the design phase is evident in the case of Google's Flu Trends (GFT) system. The failure was attributed to tweaks made by Google to its search algorithm, particularly the introduction of its "autosuggest" feature in November 2009. These changes in the system design, without proper recalibration, led to inaccuracies in predicting flu trends. The researchers highlighted that the system was not adjusted even when it started missing predictions, indicating a lack of recalibration in the design phase [25033].
(b) The software failure incident related to the operation phase can be seen in how Google's own autosuggest feature may have influenced more people to make flu-related searches, potentially misleading the Flu Trends forecasting system. Changes in how Google serves up health-related information likely resulted in more searches for terms related to flu cures, impacting the accuracy of the predictions. This aspect of the operation of the system, in terms of user behavior and search patterns, contributed to the failure of the Flu Trends system [25033]. |
| Boundary (Internal/External) |
within_system, outside_system |
(a) within_system: The software failure incident related to Google's Flu Trends system was primarily due to contributing factors that originated from within the system itself. The failure was attributed to tweaks made by Google to its search algorithm, particularly the introduction of the "autosuggest" feature in November 2009. These internal changes led to the system overestimating the number of influenza cases in the US and deviating from accurate predictions [Article 25033].
(b) outside_system: The failure of Google's Flu Trends system was also influenced by factors originating from outside the system. For example, the increase in flu-related searches driven by Google's autosuggest feature may have misled the forecasting system. Additionally, changes in how Google serves up health-related information likely resulted in more searches for terms related to flu cures, affecting the accuracy of the predictions. The evolving behavior of users and external factors impacting search patterns were external contributors to the software failure incident [Article 25033]. |
| Nature (Human/Non-human) |
non-human_actions, human_actions |
(a) The software failure incident related to non-human actions:
The failure of Google's Flu Trends system was attributed to tweaks made by Google to its search algorithm and the introduction of its "autosuggest" feature, which may have misled the forecasting system. The system was built on correlation with CDC's reported figures and was intended to forecast CDC data rather than any absolute number of flu cases. The failure was described as a slow loosening of a spring in a bathroom scale that was never recalibrated, indicating a gradual drift in the system's accuracy over time [25033].
(b) The software failure incident related to human actions:
The researchers highlighted the issue of "big data hubris" where organizations or companies give too much weight to flawed analyses. They pointed out that Google's failure to disclose the search terms it uses or how it weights them to generate forecasts hindered the understanding of why the system failed. The lack of transparency from Google regarding its algorithms and data was seen as a barrier to scientific research and improvement of the system. The researchers emphasized the importance of constantly recalibrating models based on the assumption that relationships in data are elastic and not constant [25033]. |
| Dimension (Hardware/Software) |
software |
(a) The software failure incident related to hardware:
- The article does not mention any hardware-related issues contributing to the failure of Google's Flu Trends system. It primarily focuses on the software aspects such as the search algorithm tweaks, autosuggest feature, and the methodology used in the prediction model [25033].
(b) The software failure incident related to software:
- The failure of Google's Flu Trends system is attributed to various software-related factors. The article highlights that tweaks made by Google to its search algorithm, particularly the introduction of the autosuggest feature, played a role in misleading the Flu Trends forecasting system. Additionally, the methodology used in the prediction model, the selection and weighting of search terms, and the lack of transparency regarding the algorithm and data used by Google are mentioned as software-related contributing factors to the failure [25033]. |
| Objective (Malicious/Non-malicious) |
non-malicious |
(a) The software failure incident related to Google's Flu Trends system was non-malicious. The failure was attributed to tweaks made by Google to its search algorithm, particularly the introduction of the "autosuggest" feature, which may have misled the forecasting system. The failure was not intentional but rather a result of changes in the search algorithm and user behavior over time [Article 25033]. |
| Intent (Poor/Accidental Decisions) |
poor_decisions, accidental_decisions |
(a) The software failure incident related to Google's Flu Trends system can be attributed to poor decisions made by Google in tweaking its search algorithm and introducing the "autosuggest" feature. The researchers highlighted that Google's failure to recalibrate the system despite it starting to miss predictions years ago was a significant factor in the failure [Article 25033]. Additionally, the methodology used by Google in the initial version of GFT was described as a problematic marriage of big and small data, leading to the high chances of finding search terms that seemed to match flu incidence but were actually unrelated [Article 25033].
(b) The software failure incident can also be linked to accidental decisions or unintended consequences. For example, the introduction of Google's autosuggest feature may have inadvertently driven more people to make flu-related searches, which in turn misled the Flu Trends forecasting system [Article 25033]. The lack of disclosure by Google regarding the specific search terms used and how they were weighted to generate forecasts also contributed to the unintended consequences of the system's failure [Article 25033]. |
| Capability (Incompetence/Accidental) |
development_incompetence, accidental |
(a) The software failure incident related to development incompetence is evident in the case of Google's Flu Trends system. The researchers discovered that the system's failure to accurately predict flu trends was attributed to tweaks made by Google to its search algorithm and the introduction of the "autosuggest" feature, which misled the forecasting system [Article 25033].
(b) The software failure incident related to accidental factors is seen in the unintended consequences of Google's changes to its search algorithm and the introduction of the autosuggest feature. These changes inadvertently led to more searches for flu-related terms, affecting the accuracy of the Flu Trends forecasting system [Article 25033]. |
| Duration |
temporary |
The software failure incident related to Google's Flu Trends system can be categorized as a temporary failure. The failure was attributed to contributing factors introduced by certain circumstances, such as tweaks made by Google to its search algorithm and the introduction of the "autosuggest" feature in November 2009 [Article 25033]. The failure was not permanent as it was not solely due to inherent flaws in the system but rather specific changes and factors that affected the accuracy of the predictions. |
| Behaviour |
crash, omission, value, other |
(a) crash: The software failure incident related to Google's Flu Trends system can be categorized as a crash. The system lost its predictive power and failed to accurately forecast flu trends, leading to overestimations and deviations from actual flu cases [Article 25033].
(b) omission: The failure of the Google Flu Trends system can also be attributed to omission. The system omitted to perform its intended function of accurately predicting flu trends, leading to incorrect forecasts and unreliable data [Article 25033].
(c) timing: While the Google Flu Trends system did not exhibit a timing-related failure explicitly, its failure to predict flu trends accurately could indirectly be considered a timing issue. The system's predictions were not aligned with the actual timing of flu cases, indicating a failure in the timing of its forecasts [Article 25033].
(d) value: The software failure incident involving Google's Flu Trends system can be linked to a failure in value. The system failed to provide accurate and valuable predictions of flu trends, leading to misleading information and flawed analyses based on its forecasts [Article 25033].
(e) byzantine: The software failure incident related to Google's Flu Trends system does not align with a byzantine failure. The system did not exhibit inconsistent responses or interactions; rather, it consistently failed to predict flu trends accurately [Article 25033].
(f) other: The other behavior exhibited by the software failure incident is a failure due to flawed methodology and lack of recalibration. The system's reliance on flawed correlations, lack of recalibration, and changes in search algorithms contributed to its failure to accurately forecast flu trends, highlighting a fundamental flaw in its design and maintenance [Article 25033]. |