| Recurring |
multiple_organization |
<Article 66920> reports on the failure of the Compas algorithm used for bail and sentencing decisions. The analysis conducted by Hany Farid and Julia Dressel compared the accuracy of the software against untrained workers and found that the software's predictions were not significantly more accurate than those made by individuals with limited information. This raises concerns about the reliability and effectiveness of the software in making critical decisions in the criminal justice system. Additionally, the article highlights the potential for racial biases and disparities in the outputs of such software, indicating the possibility of magnifying existing biases within the criminal justice system. This incident serves as a cautionary tale about the limitations and potential risks associated with relying on algorithmic decision-making tools in sensitive contexts like the criminal justice system.
Therefore, the software failure incident related to multiple organizations, as it raises concerns about the broader implications of using such algorithms in decision-making processes beyond a single organization or system [66920]. |
| Phase (Design/Operation) |
design |
<Article 66920> The article discusses a software failure incident related to the design phase of the system. The failure is attributed to the accuracy and effectiveness of the Compas algorithm used for bail and sentencing decisions. The analysis revealed that the software's predictions were not significantly more accurate than judgments made by untrained individuals based on limited information. This failure in the design phase raises concerns about the reliability and justification of using such algorithms in critical decision-making processes within the criminal justice system. Additionally, the article highlights the potential for racial biases and inequalities introduced by the software, indicating a failure in addressing fairness and transparency in the design of the algorithm [66920].
Regarding the operation phase, the article mentions that despite the potential biases and limitations of the software, experts like Seena Fazel suggest that these algorithms still hold value in providing gradations of risk and identifying areas of vulnerability. This indicates that while there are concerns about the operation and potential misuse of such software in the criminal justice system, there is recognition of the utility of these tools in certain contexts [66920]. |
| Boundary (Internal/External) |
within_system |
(a) within_system: The software failure incident related to the Compas algorithm used for bail and sentencing decisions is primarily within the system. The failure is attributed to the accuracy and effectiveness of the algorithm itself in predicting the risk of reoffending. The analysis conducted compared the software's predictions against those made by untrained workers, revealing that the software's accuracy was not significantly better than predictions based on basic factors like age and number of prior convictions [66920]. This indicates that the failure lies within the system's design and functionality, raising questions about the algorithm's reliability and suitability for making critical decisions in the criminal justice system. |
| Nature (Human/Non-human) |
non-human_actions, human_actions |
(a) The software failure incident occurring due to non-human actions:
The article discusses the failure of the Compas algorithm, a computer program used for bail and sentencing decisions, in accurately predicting the risk of reoffending. The analysis conducted by Farid and Dressel compared the accuracy of the software against untrained workers and found that the software's predictions were not significantly more accurate than those made by humans with limited information. The study revealed that the software's predictions could be matched using a simple calculation involving only an offender's age and the number of prior convictions, indicating that the algorithm's complexity did not necessarily lead to improved accuracy [66920].
(b) The software failure incident occurring due to human actions:
The article raises concerns about the potential biases introduced by algorithms like Compas into the criminal justice system. It highlights the issue of racial asymmetries in the outputs of such software, where the false positive rate was higher for black defendants compared to white defendants. This disparity was attributed to the underlying rate of reoffending in different populations, but it nonetheless raised questions about the fairness of the algorithm and how it could reinforce existing biases within the system. The article also emphasizes the importance of transparency in the inner workings of risk assessment tools to allow for scrutiny and understanding by judges, courts, and prosecutors [66920]. |
| Dimension (Hardware/Software) |
software |
(a) The articles do not mention any hardware-related failures that contributed to the software failure incident. Therefore, there is no information available regarding hardware-related factors in this context.
(b) The software failure incident discussed in the articles is related to the accuracy and effectiveness of the Compas algorithm used for bail and sentencing decisions. The analysis conducted by Farid and Dressel compared the software's predictive capabilities against untrained workers and found that the software's accuracy was not significantly better than humans provided with limited information. The study highlighted concerns about the accuracy and transparency of the algorithm, indicating a failure in the software's ability to provide reliable risk assessments for defendants [66920]. |
| Objective (Malicious/Non-malicious) |
non-malicious |
The software failure incident discussed in the articles is non-malicious. The failure is related to the accuracy and potential biases of the Compas algorithm used for bail and sentencing decisions. The analysis conducted by Farid and Dressel compared the accuracy of the software against untrained workers and found that the software's predictions were not significantly more accurate than those made by individuals with limited information. Additionally, concerns were raised about racial asymmetries in the outputs of the software, indicating potential biases that could impact decision-making in the criminal justice system [66920]. |
| Intent (Poor/Accidental Decisions) |
poor_decisions |
The intent of the software failure incident can be categorized as poor_decisions. The failure of the software, in this case, is related to the poor decision-making process surrounding the use of the algorithm Compas for bail and sentencing decisions. The article highlights concerns about the accuracy and effectiveness of the software in predicting the risk of reoffending, raising questions about whether it should be used in critical decisions that can have life-changing consequences [66920]. The analysis conducted by Farid and Dressel compared the software's performance against untrained workers and found that the software's predictions were not significantly more accurate than those made by individuals with limited information, indicating potential flaws in the decision-making process that led to the development and implementation of the software [66920]. |
| Capability (Incompetence/Accidental) |
development_incompetence, accidental |
(a) The software failure incident in the article can be attributed to development incompetence. The article highlights concerns about the accuracy and effectiveness of the Compas algorithm used for bail and sentencing decisions. The analysis conducted by Farid and Dressel compared the software's predictive abilities against untrained workers and found that the humans were slightly more accurate in predicting reoffending than the Compas algorithm. This raises questions about the justification for using such software in critical decisions within the criminal justice system, indicating a lack of professional competence in developing algorithms that are relied upon for important judgments [66920].
(b) The software failure incident can also be considered accidental. While the article does not explicitly mention any accidental factors leading to the failure, the discrepancies and biases identified in the software's predictions, particularly in terms of racial asymmetries and false positive rates, could be seen as unintended consequences of the algorithm's design and implementation. These accidental outcomes highlight the complexity and potential unintended consequences of relying on predictive algorithms in sensitive decision-making processes [66920]. |
| Duration |
temporary |
The software failure incident discussed in the articles is more related to a potential temporary failure rather than a permanent one. The incident involves questioning the accuracy and effectiveness of the Compas algorithm used for bail and sentencing decisions. The analysis conducted by Farid and Dressel compared the software's predictions against those made by untrained workers, showing that the software's accuracy was slightly lower than that of humans provided with limited information [66920]. This indicates that the failure is temporary in nature, as it is based on specific circumstances and factors related to the algorithm's design and performance rather than being inherent and permanent. |
| Behaviour |
value, other |
(a) crash: The software failure incident does not involve a crash where the system loses state and does not perform any of its intended functions. The issue here is related to the accuracy and effectiveness of the software in predicting the risk of reoffending, rather than a complete failure of the system to function [66920].
(b) omission: The software failure incident is not due to the system omitting to perform its intended functions at an instance(s). The concern is more about the accuracy and bias in the predictions made by the software, rather than instances where it fails to perform its functions [66920].
(c) timing: The software failure incident is not related to the system performing its intended functions too late or too early. The focus is on the accuracy and fairness of the predictions made by the software, rather than issues with timing [66920].
(d) value: The software failure incident is related to the system performing its intended functions incorrectly. The concern is that the software's predictions are not significantly more accurate than those made by untrained individuals using much less information, raising doubts about the justification for using the software in critical decisions [66920].
(e) byzantine: The software failure incident does not involve the system behaving erroneously with inconsistent responses and interactions. The main issue is about the accuracy, bias, and transparency of the software in predicting the risk of reoffending, rather than erratic behavior or inconsistent responses [66920].
(f) other: The software failure incident involves the software potentially magnifying existing biases within the criminal justice system. For example, the software may reinforce racial biases if certain populations are more likely to be convicted when arrested for a crime, leading to disparities in the outcomes of the software's predictions [66920]. |