| Recurring |
one_organization |
a) The software failure incident related to Google's image recognition algorithm misidentifying black people as gorillas happened again within the same organization. Google faced backlash in 2015 when its Photos app tagged images of a computer programmer and his friend as primates, leading to the company blocking identification of gorillas, chimpanzees, and monkeys [66881].
b) There is no specific information in the provided article indicating that a similar incident has happened at other organizations or with their products and services. |
| Phase (Design/Operation) |
design, operation |
(a) The software failure incident related to the design phase can be seen in the case of Google's image recognition algorithm. The incident occurred due to the algorithm misidentifying black people as gorillas, leading to outrage and criticism. Instead of addressing the root cause of the issue by developing a more diverse model for the algorithm, Google opted to simply ban the identification of gorillas, chimpanzees, and monkeys [66881].
(b) The software failure incident related to the operation phase is evident in how the misidentification of individuals as gorillas occurred during the operation of Google Photos. Users like Jacky Alcine discovered that photos of him and a female friend were tagged as gorillas by the image recognition software, highlighting a failure in the operational aspect of the system [66881]. |
| Boundary (Internal/External) |
within_system |
(a) The software failure incident involving Google's image recognition algorithm can be categorized as within_system. The incident occurred due to the algorithm's inability to correctly identify and categorize images, leading to offensive and inaccurate labeling of individuals as gorillas [66881]. The issue was acknowledged by Google, and efforts were made internally to address the problem by blocking the identification of certain terms like 'gorilla,' 'chimp,' 'chimpanzee,' and 'monkey' within the algorithm [66881]. The company's response to the incident and the subsequent actions taken to rectify the problem indicate that the failure originated from within the system itself. |
| Nature (Human/Non-human) |
non-human_actions, human_actions |
(a) The software failure incident in the article was primarily due to non-human actions. Google's image recognition algorithm misidentified images of a computer programmer and his friend as gorillas, leading to outrage and criticism. Google responded by blocking the identification of gorillas, chimpanzees, and monkeys to prevent such misidentifications in the future [66881].
(b) Human actions also played a role in addressing the software failure incident. After the incident was reported, Google's chief architect of social, Yonatan Zunger, acknowledged the problem and mentioned that engineers were working on fixes to prevent similar issues in the future. He also mentioned that the error could occur in photographs where the image recognition software failed to detect a face at all, and efforts were being made to address this issue [66881]. |
| Dimension (Hardware/Software) |
software |
(a) The software failure incident reported in the articles was not due to hardware issues. The incident was related to Google's image recognition algorithm misidentifying images and causing outrage among users [66881].
(b) The software failure incident was primarily due to contributing factors originating in the software itself. Google's image recognition algorithm had a flaw that led to images of individuals being incorrectly tagged as gorillas, prompting the company to block the identification of gorillas, chimpanzees, and monkeys to address the issue [66881]. |
| Objective (Malicious/Non-malicious) |
non-malicious |
(a) The software failure incident related to the Google Photos image recognition algorithm was non-malicious. The incident occurred when the algorithm incorrectly tagged images of a computer programmer and his friend as gorillas, causing outrage among users. Google's response to the incident was to block the identification of gorillas, chimpanzees, and monkeys, rather than fixing the underlying issue with the algorithm. This action was criticized by users and experts who suggested that Google should have developed a more diverse model for the algorithm instead of simply banning certain terms like 'gorilla' [66881].
(b) The incident was not a result of malicious intent but rather a failure in the image recognition technology that led to incorrect labeling of individuals in the photos. The chief architect of social at Google, Yonatan Zunger, acknowledged the mistake and mentioned that engineers were working on fixes to prevent similar issues in the future. The response from Google indicated that the error was unintentional and efforts were being made to address the underlying problems in the software [66881]. |
| Intent (Poor/Accidental Decisions) |
poor_decisions |
(a) The intent of the software failure incident was related to poor_decisions. Google faced criticism for "fixing" its racist image recognition algorithm by simply removing the word 'gorilla' from its auto-tag tool instead of developing a more diverse model for the algorithm [66881]. The decision to just ban the identification of gorillas and black people was seen as a poor choice by many users and experts, highlighting a lack of foresight and proactive measures in addressing the underlying issue of bias in the algorithm. |
| Capability (Incompetence/Accidental) |
development_incompetence |
(a) The software failure incident related to development incompetence is evident in the case of Google's image recognition algorithm misidentifying black people as gorillas. Instead of addressing the root cause of the issue and improving the algorithm's accuracy and diversity, Google opted to simply ban the identification of gorillas, chimpanzees, and monkeys [66881]. This decision was criticized by users and experts who highlighted the need for a more comprehensive and diverse model base for the algorithm to avoid such discriminatory misidentifications in the future. The incident reflects a failure in addressing the underlying professional competence and diversity aspects of the software development process.
(b) The accidental nature of the software failure incident is demonstrated by the initial misidentification of individuals as gorillas by Google's image recognition algorithm. This misidentification was not intentional but rather a result of the algorithm's limitations and biases in its image labeling technology, as acknowledged by Google [66881]. The company expressed its appall and genuine sorry for the mistake, indicating that the misidentification was unintentional and not a deliberate action. The incident highlights how accidental factors, such as algorithmic limitations and biases, can lead to software failures. |
| Duration |
permanent, temporary |
The software failure incident related to Google's image recognition algorithm misidentifying black people as gorillas was initially temporary as Google "fixed" the issue by blocking identification of gorillas, chimpanzees, and monkeys [66881]. However, the incident also highlighted a more permanent issue with the software's image labelling technology, which the company acknowledged as being "nowhere near perfect" [66881]. |
| Behaviour |
crash, omission, value, other |
(a) crash: The software failure incident in the article can be categorized as a crash. The Google Photos image recognition algorithm crashed in the sense that it misidentified images of a computer programmer and his friend as gorillas, leading to outrage and criticism [66881].
(b) omission: The software failure incident can also be categorized as an omission. The system omitted to perform its intended functions correctly by failing to correctly identify and categorize images of gorillas, chimpanzees, and monkeys, leading to the decision to block the identification of these animals altogether [66881].
(c) timing: The software failure incident does not seem to be related to timing issues. The issue was not about the system performing its intended functions too late or too early but rather about the incorrect identification of images [66881].
(d) value: The software failure incident can be categorized as a value failure. The system performed its intended functions incorrectly by mislabeling images of individuals as gorillas, which led to the decision to block the identification of gorillas, chimpanzees, and monkeys [66881].
(e) byzantine: The software failure incident does not exhibit characteristics of a byzantine failure. The system did not display inconsistent responses or interactions but rather consistently misidentified certain images, leading to the decision to block the identification of specific terms [66881].
(f) other: The other behavior exhibited by the software failure incident is the company's decision to address the issue by simply banning the identification of gorillas, chimpanzees, and monkeys instead of developing a more robust and diverse model for the algorithm. This approach was criticized for not addressing the root cause of the problem and for potentially avoiding the underlying issues in the image recognition technology [66881]. |