Incident: Amazon's AI Recruiting Tool Discriminates Against Women

Published Date: 2018-10-10

Postmortem Analysis
Timeline 1. The software failure incident involving Amazon's recruiting engine happened in 2015 [76625, 76486, 77009]. 2. The incident occurred in 2014 based on the information provided in the articles.
System 1. Amazon's experimental hiring tool using artificial intelligence to review job applicants' resumes [76625, 76486, 76456, 77009, 76530]
Responsible Organization 1. Amazon.com Inc's machine-learning specialists [76625, 76486, 76456, 77009, 76530]
Impacted Organization 1. Amazon.com Inc [76625, 76486, 76456, 77009, 76530]
Software Causes 1. The software failure incident was caused by the machine learning algorithm used in Amazon's recruiting engine, which exhibited gender bias by favoring male candidates over female candidates [76625, 76486, 76456, 77009, 76530]. 2. The algorithm was trained on historical data that predominantly consisted of resumes from male applicants, leading to the system teaching itself that male candidates were preferable, penalizing resumes with terms like "women's," and downgrading graduates from all-women's colleges [76625, 76486, 76456, 77009, 76530]. 3. The software failure was also attributed to the algorithm's inability to rate candidates for technical positions in a gender-neutral way, as it was not designed to handle gender biases in the data it was trained on [76625, 76486, 76456, 77009, 76530]. 4. The system's discriminatory behavior towards female candidates led to its disbandment by Amazon due to executives losing hope in the project and the inability to rely on the algorithm for unbiased hiring decisions [76625, 76486, 76456, 77009, 76530].
Non-software Causes 1. Gender bias in the data used to train the algorithm, with most resumes coming from men, leading to a preference for male candidates [76625, 76486, 77090, 77009, 76530] 2. Lack of gender-neutral training data resulting in discrimination against female candidates [76625, 76486, 77090, 77009, 76530]
Impacts 1. The software failure incident at Amazon's recruiting engine resulted in gender bias, as the system showed a preference for male candidates over female candidates [76625, 76486, 76456, 77009, 76530]. 2. The system penalized resumes that included the word "women's" and downgraded graduates of all-women's colleges, leading to discriminatory outcomes in the hiring process [76625, 76486, 76456, 77009, 76530]. 3. The incident highlighted the limitations of machine learning and the challenges in ensuring fairness, interpretability, and explainability of algorithms in automated hiring processes [76625, 76486, 76456, 77009, 76530]. 4. Amazon ultimately disbanded the team working on the project and did not rely solely on the recommendations generated by the tool for hiring decisions [76625, 76486, 76456, 77009, 76530]. 5. The failure of the AI recruiting tool led to concerns about algorithmic fairness and transparency in AI systems, with activists raising issues about potential discrimination in automated hiring processes [76625, 76486, 76456, 77009, 76530].
Preventions 1. Conducting a more thorough analysis of the training data to ensure it is diverse and representative of all demographics [76625, 76486, 77090, 77009, 76530]. 2. Implementing regular audits and checks on the algorithm's decision-making process to detect and address biases early on [76625, 76486, 77090, 77009, 76530]. 3. Involving diverse teams in the development and testing of the AI system to bring different perspectives and prevent unconscious biases from influencing the technology [76625, 76486, 77090, 77009, 76530]. 4. Establishing clear guidelines and protocols for handling discriminatory outcomes or biases detected in the AI system [76625, 76486, 77090, 77009, 76530]. 5. Ensuring transparency and explainability of the algorithm's decision-making process to understand how it reaches its conclusions and to address any potential biases [76625, 76486, 77090, 77009, 76530].
Fixes 1. Implementing more diverse and unbiased training data to avoid gender bias in the algorithm [76625, 76486, 77090, 77009, 76530]. 2. Conducting thorough testing and validation of the algorithm to ensure it is fair and interpretable [76625, 76486, 77090, 77009]. 3. Enhancing transparency in AI systems to address concerns about discrimination and bias [76625, 76486, 77090, 77009]. 4. Continuing to refine the algorithm and ensuring it is ready for making hiring decisions independently [76625, 76486, 77090, 77009]. 5. Focusing on diversity and inclusivity in the development of automated employment screening tools [76625, 76486, 77090, 77009].
References 1. Reuters [76625, 76486, 77009, 76530] 2. MIT [76530] 3. Massachusetts Institute of Technology [76530] 4. CareerBuilder [77009]

Software Taxonomy of Faults

Category Option Rationale
Recurring one_organization (a) The software failure incident related to bias against women in the recruiting engine at Amazon has happened again within the same organization. The incident involved an algorithm developed by Amazon's machine-learning specialists that exhibited gender bias, penalizing resumes with the word "women" and favoring male candidates. This incident led to the project being abandoned due to the discriminatory outcomes it produced [Article 76625], [Article 76486], [Article 77090], [Article 77009], [Article 76530]. (b) There is no information in the provided articles about a similar incident happening at other organizations or with their products and services.
Phase (Design/Operation) design, operation (a) In the reported software failure incident at Amazon related to the recruiting engine, the failure can be attributed to the design phase. The algorithm used for reviewing job applicants' resumes was trained on historical data that showed a bias towards male candidates. The system penalized resumes with terms like "women's" and downgraded graduates from all-women's colleges, reflecting a gender bias in the training data. This bias was a result of the design and development of the algorithm, which led to discriminatory outcomes in the recruitment process [76625, 76486, 76456, 77009, 76530]. (b) The failure can also be linked to the operation phase as the AI recruiting tool was used by Amazon's recruiters for a period, although they did not solely rely on its recommendations. The operation of the system involved human interaction and decision-making based on the tool's outputs. Despite the biases in the system, it was operated within the recruitment process, indicating issues in the operational use of the software [76625, 76486, 76456, 77009, 76530].
Boundary (Internal/External) within_system, outside_system (a) The software failure incident reported in the articles is primarily within the system. Amazon's recruiting engine, which used artificial intelligence to review job applicants' resumes, exhibited bias towards male candidates and discriminated against women. The system was trained on historical data that predominantly consisted of resumes from men, leading to a preference for male candidates and penalizing resumes with terms like "women's." The issue stemmed from how the algorithm was developed and the data it was trained on, indicating an internal system failure [76625, 76486, 76456, 77009, 76530]. (b) The software failure incident also had implications outside the system. The biased AI system used by Amazon for recruitment reflects broader societal issues related to gender bias and discrimination. The incident highlights the challenges of using AI in hiring processes and the potential for algorithms to perpetuate existing biases present in the data they are trained on. This external context of societal biases influencing the AI system's behavior is a contributing factor to the failure incident [76625, 76486, 76456, 77009, 76530].
Nature (Human/Non-human) non-human_actions, human_actions (a) The software failure incident occurred due to non-human actions, specifically the bias and discrimination introduced by the algorithm itself. The AI system developed by Amazon to review job applicants' resumes was trained on historical data that predominantly came from male applicants, leading the system to favor male candidates and penalize resumes with terms like "women's" or graduates from all-women's colleges [76625, 76486, 77090, 77009, 76530]. (b) The software failure incident also involved human actions as the team of developers and engineers at Amazon were responsible for creating and training the algorithm. Despite efforts to edit the program to be neutral to certain terms, the system still exhibited discriminatory behavior. Additionally, recruiters at Amazon used the recommendations generated by the tool, although they did not rely solely on those rankings [76625, 76486, 77090, 77009, 76530].
Dimension (Hardware/Software) software (a) The articles do not mention any hardware-related failures that contributed to the software failure incident. (b) The software failure incident reported in the articles is related to a recruiting engine developed by Amazon's machine-learning specialists. The software failure occurred due to the algorithm being trained on biased data that led to gender discrimination in the recruitment process. The system taught itself to prefer male candidates over females, penalizing resumes with terms like "women's" and downgrading graduates from all-women's colleges. This bias in the software's decision-making process ultimately led to the project being abandoned by Amazon [76625, 76486, 76456, 77090, 77009, 76530].
Objective (Malicious/Non-malicious) non-malicious (a) The software failure incident described in the articles is non-malicious. The incident involved Amazon's machine-learning specialists developing a recruiting engine that exhibited gender bias towards women. The system was trained on historical data that predominantly consisted of resumes from men, leading to a bias in favor of male candidates. The system penalized resumes containing the word "women" and downgraded graduates from all-women's colleges. Despite these biases, there is no indication in the articles that the system was intentionally designed to harm or discriminate against women ([76625], [76486], [76456], [77009], [76530]).
Intent (Poor/Accidental Decisions) poor_decisions (a) The intent of the software failure incident was to develop a machine learning tool for automating the recruitment process at Amazon. The tool was designed to review job applicants' resumes and give them scores ranging from one to five stars, similar to how shoppers rate products on Amazon. The goal was to create an engine that could quickly analyze resumes and select the top candidates for hiring [76625, 76486, 76456, 77009, 76530]. However, the software failure incident occurred due to poor decisions made during the development and training of the algorithm. The algorithm was trained on historical data that predominantly consisted of resumes from male candidates, leading to a bias towards male applicants. The system taught itself to prefer male candidates, penalizing resumes with terms like "women's" and downgrading graduates from all-women's colleges. Despite attempts to edit the program to remove bias, the system could not be relied upon and was ultimately abandoned [76625, 76486, 76456, 77009, 76530].
Capability (Incompetence/Accidental) development_incompetence (a) The software failure incident occurred due to development incompetence. The incident involved Amazon's machine-learning specialists developing a recruiting engine that exhibited gender bias towards women. The system was trained on historical resumes, mostly from men, leading it to prefer male candidates over females. Despite attempts to edit the program to be neutral, the system still exhibited discriminatory behavior, penalizing resumes with the word "women" and downgrading graduates from all-women's colleges. This bias was a result of the system teaching itself that male candidates were preferable, showcasing a lack of professional competence in developing a gender-neutral algorithm [76625, 76486, 76456, 77009, 76530]. (b) The software failure incident was not accidental but rather a result of the system being trained on biased data and developing discriminatory patterns. The incident was a consequence of the development process and the data used, leading to unintended gender bias in the recruiting engine. Despite efforts to address the bias, the system's discriminatory behavior persisted, ultimately resulting in the project being abandoned due to the inability to rely on the algorithm for fair candidate evaluations [76625, 76486, 76456, 77009, 76530].
Duration temporary (a) The software failure incident in the articles was temporary. The incident involved Amazon's experimental hiring tool that used artificial intelligence to review job applicants' resumes and give them scores. The tool was found to be biased against women as it was trained on data mostly from men, leading to discriminatory outcomes in the hiring process. Despite the initial intention to use the tool for recruitment, Amazon ultimately had to disband the team working on the project and stop relying on the tool for hiring decisions [76625, 76486, 76456, 77009, 76530].
Behaviour value, other (a) crash: The software failure incident did not involve a crash where the system lost state and did not perform any of its intended functions. The incident was related to bias in the recruiting engine developed by Amazon ([76625], [76486], [77090], [77009], [76530]). (b) omission: The software failure incident did not involve omission where the system omitted to perform its intended functions at an instance(s). The incident was related to bias in the recruiting engine developed by Amazon ([76625], [76486], [77090], [77009], [76530]). (c) timing: The software failure incident did not involve timing issues where the system performed its intended functions correctly but too late or too early. The incident was related to bias in the recruiting engine developed by Amazon ([76625], [76486], [77090], [77009], [76530]). (d) value: The software failure incident was related to the system performing its intended functions incorrectly. The AI recruiting tool developed by Amazon showed bias against women, penalizing resumes with terms like "women's" and downgrading graduates of all-women's colleges ([76625], [76486], [77090], [77009], [76530]). (e) byzantine: The software failure incident did not involve a byzantine behavior where the system behaved erroneously with inconsistent responses and interactions. The incident was related to bias in the recruiting engine developed by Amazon ([76625], [76486], [77090], [77009], [76530]). (f) other: The software failure incident involved bias in the AI recruiting tool developed by Amazon, leading to discriminatory behavior against women in the hiring process. The system showed a preference for male candidates and penalized resumes with terms associated with women, ultimately leading to the project being abandoned ([76625], [76486], [77090], [77009], [76530]).

IoT System Layer

Layer Option Rationale
Perception None None
Communication None None
Application None None

Other Details

Category Option Rationale
Consequence property, theoretical_consequence (a) death: There were no reports of people losing their lives due to the software failure incident in the provided articles [76625, 76486, 77090, 77009, 76530]. (b) harm: There were no reports of people being physically harmed due to the software failure incident in the provided articles [76625, 76486, 77090, 77009, 76530]. (c) basic: There were no reports of people's access to food or shelter being impacted because of the software failure incident in the provided articles [76625, 76486, 77090, 77009, 76530]. (d) property: The software failure incident did impact people's material goods, money, or data as it led to discriminatory practices in the recruitment process at Amazon [76625, 76486, 77090, 77009, 76530]. (e) delay: There were no reports of people having to postpone an activity due to the software failure incident in the provided articles [76625, 76486, 77090, 77009, 76530]. (f) non-human: Non-human entities were not directly impacted due to the software failure incident in the provided articles [76625, 76486, 77090, 77009, 76530]. (g) no_consequence: The software failure incident did have real observed consequences related to discriminatory recruitment practices at Amazon [76625, 76486, 77090, 77009, 76530]. (h) theoretical_consequence: The potential consequences discussed in the articles included issues of algorithmic fairness, transparency in AI, and the challenges of using AI for hiring decisions [76625, 76486, 77090, 77009, 76530]. (i) other: There were no other consequences of the software failure incident mentioned in the provided articles [76625, 76486, 77090, 77009, 76530].
Domain information (a) The failed system was intended to support the information industry. The system was a recruitment tool developed by Amazon using artificial intelligence to review job applicants' resumes [76625, 76486, 76456, 77090, 77009, 76530].

Sources

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