| Recurring |
one_organization, multiple_organization |
<Article 91720> The article discusses a software failure incident related to bias in the Apple Card algorithm, which led to allegations of gender discrimination. This incident highlights the potential for bias in algorithms, even when they are designed to be "blind" to certain variables like gender. The article also mentions other instances where algorithms have exhibited bias, such as Amazon's hiring algorithm, Google's autocomplete feature, and facial recognition algorithms from IBM and Microsoft. These examples show that bias in algorithms is a widespread issue affecting multiple organizations and their products and services. Therefore, the incident at Apple with the Apple Card is not an isolated case but rather part of a broader trend of algorithmic bias affecting various companies and industries [91720]. |
| Phase (Design/Operation) |
design |
(a) The article discusses a software failure incident related to the design phase. The incident with the Apple Card involved a controversy where users noticed gender bias in the credit lines offered by the card. The article highlights how the algorithm used by the Apple Card, developed during the design phase, was under scrutiny for potentially being biased against women. Despite claims by the issuing bank, Goldman Sachs, that the algorithm was vetted for bias by a third party and did not use gender as an input, concerns were raised about the possibility of bias creeping in through proxies or correlated variables related to gender [91720].
(b) The article does not provide information about a software failure incident related to the operation phase. |
| Boundary (Internal/External) |
within_system |
(a) The software failure incident related to the Apple Card's gender bias issue can be categorized as within_system. The failure was attributed to the algorithm used by the Apple Card, which was designed to determine credit limits for users. The algorithm, developed by Goldman Sachs, was under scrutiny for potentially offering smaller lines of credit to women compared to men. Despite claims that the algorithm did not use gender as an input, concerns were raised about how the algorithm could still exhibit bias based on other correlated variables. The incident highlights the importance of auditing algorithms to detect and prevent biases that may be embedded within the system itself [91720]. |
| Nature (Human/Non-human) |
non-human_actions, human_actions |
(a) The software failure incident in the Apple Card case was not directly attributed to non-human actions but rather to the algorithm used in determining credit lines. The algorithm was criticized for potentially exhibiting gender bias even though it was claimed to be gender-blind. This highlights the issue of bias creeping into algorithms without direct human intervention, leading to unintended consequences [91720].
(b) On the other hand, human actions played a role in the failure incident as well. The design and implementation of the algorithm, which was created and maintained by humans, were under scrutiny for potentially introducing bias. Additionally, the response from Apple and Goldman Sachs, the issuing bank, to the allegations of gender bias also involved human actions in terms of communication and decision-making [91720]. |
| Dimension (Hardware/Software) |
software |
(a) The software failure incident related to hardware: The article does not mention any software failure incident related to hardware [91720].
(b) The software failure incident related to software: The article discusses a software failure incident related to the Apple Card algorithm. Users noticed that the algorithm seemed to offer smaller lines of credit to women than to men, leading to accusations of gender bias. The article highlights the challenges of auditing algorithms to detect and prevent bias, emphasizing the importance of actively measuring protected attributes like gender and race to ensure algorithms are not biased [91720]. |
| Objective (Malicious/Non-malicious) |
non-malicious |
(a) The software failure incident related to the Apple Card involved allegations of gender bias in the credit lines offered to women compared to men. Users and tech experts accused the algorithm behind the Apple Card of being "fucking sexist" and "beyond f’ed up" [91720]. The incident led to a Wall Street regulator announcing an investigation into the card to determine if it breached any financial rules. The response from Apple and Goldman Sachs, the issuing bank, added confusion and suspicion as they struggled to explain how the algorithm worked and justify its output. Despite claims that the algorithm was vetted for bias by a third party and did not use gender as an input, concerns were raised about the potential for unintended discrimination based on proxies or correlated variables [91720].
(b) The incident also highlighted the challenges of auditing algorithms for bias and unintended discrimination. The fact that customers' gender is not collected made it difficult to effectively audit the algorithm for bias. The Equal Credit Opportunity Act prohibits financial businesses from using information such as gender or race in algorithmic decisions, which may deter them from collecting important information needed to monitor and mitigate bias effectively [91720]. |
| Intent (Poor/Accidental Decisions) |
poor_decisions, accidental_decisions |
The software failure incident related to the Apple Card involved both poor decisions and accidental decisions:
1. Poor Decisions:
The incident involved poor decisions made by the company in implementing the algorithm for the Apple Card. The company failed to provide a clear explanation of how the algorithm worked and could not justify its output. Additionally, the company's response added confusion and suspicion, leading to further scrutiny and investigation by regulators [91720].
2. Accidental Decisions:
Accidental decisions or unintended consequences were also evident in this incident. Despite claims that the algorithm did not use gender as an input and had been vetted for bias, the lack of transparency and oversight led to potential biases creeping into the system. The failure to actively measure protected attributes like gender and race, as recommended by experts, contributed to the accidental biases that could have affected the credit lines offered to women [91720]. |
| Capability (Incompetence/Accidental) |
development_incompetence |
(a) The article discusses a software failure incident related to development incompetence. The incident involved the Apple Card, where users noticed gender bias in the credit lines offered to women compared to men. The algorithm used by Goldman Sachs, the issuing bank for the Apple Card, was under scrutiny for potentially being biased against women. Despite claims that the algorithm was vetted for bias by a third party and did not use gender as an input, the lack of transparency and understanding of how the algorithm worked raised suspicions of gender discrimination. The article highlights the importance of auditing algorithms to detect and prevent biases that may be inadvertently introduced due to lack of professional competence in algorithm development [91720].
(b) The software failure incident discussed in the article does not directly relate to a failure introduced accidentally. The focus is more on the potential biases in the algorithm used for the Apple Card, which raises concerns about gender discrimination rather than accidental failures [91720]. |
| Duration |
temporary |
The software failure incident related to the Apple Card's credit algorithm issue can be considered as a temporary failure. The incident was temporary in the sense that it was caused by specific circumstances related to the design and implementation of the algorithm, particularly in how it handled gender-related data and potential biases. The incident was not a permanent failure inherent to the software itself but rather a result of the algorithm's behavior in certain conditions [91720]. |
| Behaviour |
omission, value, other |
(a) crash: The article does not mention a crash of the software system.
(b) omission: The incident described in the article is related to the Apple Card algorithm potentially offering smaller lines of credit to women compared to men, indicating an omission in performing its intended functions fairly [91720].
(c) timing: The article does not mention a timing-related failure of the software system.
(d) value: The failure in this incident is related to the system performing its intended functions incorrectly by potentially exhibiting gender bias in determining credit lines, despite claims of being gender-blind [91720].
(e) byzantine: The incident does not align with a byzantine behavior of the software system.
(f) other: The software failure incident described in the article involves potential bias in the algorithm that could lead to discrimination against certain groups, highlighting a failure related to ethical considerations and fairness in decision-making [91720]. |