| Recurring |
one_organization, multiple_organization |
(a) In the provided articles, the software failure incident related to biased image-cropping algorithms on Twitter's platform has happened again within the same organization. Users highlighted potential problems with the algorithm, which favored white individuals over black people and women over men. The incident was first noticed by a university employee in September last year, and further experiments by other users confirmed the bias in image cropping [114612, 104789].
(b) The incident of biased image-cropping algorithms, although not explicitly mentioned in the articles, could potentially have happened at other organizations or with their products and services. This type of bias in algorithms is a known issue in the tech industry, with other companies facing similar challenges in ensuring fairness and accuracy in their AI systems. |
| Phase (Design/Operation) |
design, operation |
(a) The software failure incident related to the design phase can be seen in the Twitter image-cropping AI issue. Twitter's automatic cropping algorithm exhibited racial bias, favoring white individuals over black people and women over men. The problem was with the "saliency algorithm" released in 2018 to crop images, which was trained on human eye-tracking data. Despite testing for bias before release, the algorithm showed a 4% difference favoring white people over black people of both sexes and an 8% difference favoring women over men [114612, 104789].
(b) The software failure incident related to the operation phase can be observed in how users began to spot flaws in Twitter's image-cropping algorithm. Users highlighted issues where the algorithm automatically focused on white faces over black ones, erasing black individuals from images. This failure was due to the operation of the algorithm itself, which led to biased image cropping results [104789]. |
| Boundary (Internal/External) |
within_system |
(a) within_system: The software failure incident related to Twitter's image-cropping AI was primarily due to contributing factors that originated from within the system. Twitter's automatic cropping algorithm had underlying issues that favored white individuals over black people and women over men [114612]. The problem was specifically with its "saliency algorithm" released in 2018 to crop images, which was trained on human eye-tracking data. The algorithm did not always work perfectly and exhibited biases in image cropping based on race and gender. Twitter acknowledged the issue and mentioned that more work needed to be done internally to address these biases [114612].
(b) outside_system: The software failure incident related to Twitter's image-cropping AI did not have significant contributing factors that originated from outside the system. The bias in the image-cropping algorithm was primarily attributed to how the algorithm was designed and trained internally by Twitter. Users discovered the bias within the system itself, prompting Twitter to acknowledge the issue and work on fixing it [104789]. |
| Nature (Human/Non-human) |
non-human_actions, human_actions |
(a) The software failure incident in the articles was primarily due to non-human_actions. Twitter's automatic image-cropping algorithm exhibited racial bias favoring white individuals over black people and women over men. The bias was inherent in the algorithm itself, as it was trained on human eye-tracking data but exhibited preferences that were not intended by the developers. The issue was identified through testing and analysis of the algorithm's behavior [114612, 104789].
(b) Human actions also played a role in the software failure incident. Twitter had tested the image-cropping algorithm for bias before deploying it but acknowledged that the testing did not go far enough to detect the racial and gender biases present in the algorithm. The company accepted responsibility for not conducting thorough testing to identify and address these biases before the algorithm was put into use [104789]. |
| Dimension (Hardware/Software) |
software |
(a) The software failure incident related to hardware:
- There is no specific mention of the software failure incident being caused by hardware-related factors in the provided articles.
(b) The software failure incident related to software:
- The software failure incident in the articles is primarily related to issues originating in the software itself, specifically in Twitter's automatic image-cropping algorithm.
- Twitter's algorithm had underlying issues that favored white individuals over black people and women over men, indicating a bias in the software [114612].
- The problem was identified with Twitter's "saliency algorithm" released in 2018 to crop images, which was trained on human eye-tracking data but exhibited bias in its cropping decisions [114612].
- Twitter acknowledged that it had tested the service for bias before using it but later accepted that it didn't go far enough, indicating a failure in the software's testing and validation processes [104789].
- The software incident involved flaws in the image-cropping algorithm, leading to biased cropping decisions based on race and gender, highlighting a software failure [104789]. |
| Objective (Malicious/Non-malicious) |
non-malicious |
(a) The software failure incident related to the bias in Twitter's image-cropping AI was non-malicious. The incident was caused by underlying issues in the algorithm that favored white individuals over black people and women over men. Twitter had tested the service for bias before using it but did not find evidence of racial or gender bias initially. However, users discovered the bias in the algorithm, leading to Twitter acknowledging the problem and committing to further analysis and improvements [114612, 104789]. |
| Intent (Poor/Accidental Decisions) |
poor_decisions |
(a) The intent of the software failure incident:
- The incident involving Twitter's image-cropping AI was not due to accidental decisions but rather poor decisions made during the development and implementation of the algorithm. The algorithm exhibited racial bias favoring white individuals over black people and women over men. Twitter had tested the algorithm for bias before releasing it but did not go far enough in identifying and addressing the underlying issues [114612, 104789].
- Twitter acknowledged that more work needed to be done to address the bias in the algorithm and promised a fix. The company realized that not everything on Twitter is a good candidate for an algorithm, and in this case, the decision of how to crop an image is best made by people rather than relying solely on automated systems [114612]. |
| Capability (Incompetence/Accidental) |
development_incompetence, accidental |
(a) The software failure incident related to development incompetence is evident in the Twitter image-cropping AI issue. Twitter's algorithm for cropping images showed racial bias, favoring white individuals over black people and women over men. Despite Twitter's testing for bias before releasing the model, the algorithm exhibited clear racial and gender biases, indicating a failure due to contributing factors introduced by the development team's lack of professional competence [114612, 104789].
(b) The software failure incident related to accidental factors is seen in Twitter's apology for the "racist" image cropping algorithm. Twitter had tested the service for bias before using it but later acknowledged that it didn't go far enough, leading to unintended racial bias in the image cropping algorithm. This indicates a failure due to contributing factors introduced accidentally [104789]. |
| Duration |
temporary |
(a) The software failure incident related to Twitter's image-cropping algorithm bias was temporary. The incident was not a permanent failure but rather a temporary issue caused by certain circumstances. Twitter acknowledged the bias in its algorithm and took steps to address the problem by phasing out the older system and updating its mobile apps to provide more accurate image previews [114612, 104789]. The company also mentioned that more work needed to be done to fix the bias in the algorithm and improve the cropping of images, indicating that the failure was not permanent but rather a situation that required corrective actions. |
| Behaviour |
value, other |
(a) crash: The software failure incident reported in the articles does not involve a crash where the system loses state and does not perform any of its intended functions. The incident is related to bias in Twitter's image-cropping algorithm, where the system is still functioning but with unintended biases in how it crops images [114612, 104789].
(b) omission: The incident does not involve the system omitting to perform its intended functions at an instance(s). Instead, the issue lies in the biased behavior of the image-cropping algorithm, where it focuses on certain demographics over others when cropping images [114612, 104789].
(c) timing: The incident is not related to the system performing its intended functions correctly but too late or too early. The issue with Twitter's image-cropping algorithm is not about timing but about the biased selection of which parts of an image to display [114612, 104789].
(d) value: The software failure incident is related to the system performing its intended functions incorrectly. Specifically, the algorithm was biased in favoring white individuals over black people and women over men in image cropping, leading to incorrect and biased image previews [114612, 104789].
(e) byzantine: The incident does not involve the system behaving erroneously with inconsistent responses and interactions. The issue with Twitter's image-cropping algorithm is more about the biased selection of image content rather than inconsistent responses or interactions [114612, 104789].
(f) other: The behavior of the software failure incident can be categorized as a bias in the system's decision-making process. The algorithm's bias towards certain demographics in image cropping is a unique type of failure that falls outside the traditional categories of crash, omission, timing, or byzantine behavior [114612, 104789]. |