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Identify verbs commonly used on females' resumes to help and include those in training the AI model.

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create concept maps that equate male-dominant keywords with female-dominant ones

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create concept maps that equate male-dominant keywords with female-dominant ones

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Create filters that recognize reasons for career gaps for women such as maternity leave

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I would include verbiage commonly used on women's resumes, but I would also bring in a more diverse group of resumes across multiple industries as well, knowing that you can still filter for industry-specific keywords.

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Expand the interface's vocabulary to provide a more neutral ground when selecting candidates for the position.

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If a recruiter were to utilize this, they could ask the AI to search for certain words only. This would create a way for the recruiter to choose what they want from an applicant. However, this is still open to bias.

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I would add training data that included women's resumes and diversify what the model was looking at in order to make more choices that included women

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I would create filters that equate all potential employees and do not account for gender, cultural background, or any other personal bias'. 

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I would create filters that equate all potential employees and do not account for gender, cultural background, or any other personal bias'. 

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since amazon's own hiring data skewed the learning, identify companies w/ more desired hiring outcomes and use that hiring data to train AI

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Filter for words pertaining to specific job responsibilities that applicants mention of expertise or related experience.

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I would specify the positive qualities of workers in gender-neutral ways. For example, equate a women's chess club to a men's chess club, if either term were on a resume.

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To prevent any future discriminatory practices in AI, we could: implement more neutral sounding words and phrases, be specific in what education level is needed- regardless of the origins or the primary gender population of said school, and (if possible) include a guardrail pertaining to hiring solely on gender and race/ethnicity.

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Train the AI model to place the most weight on skills applicable to the job and less weight on gender. Also, training the AI model with more resumes of women (even if they weren't necessarily applying for a job at Amazon) will allow the AI to see how skills can be relevant across the board as opposed to having such a narrow focus.

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One thing you could do is to train the ai to ignore gendered language as a deciding factor, or alternatively teach it to recognize the language used by women in their resumes as equally desirable as the language used by men.

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Adding a feature that will take the time to regularly audit that same question, but monitor the different answers that are given throughout different demographic scenarios.

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One improvement would be to train the AI on a more diverse and balanced dataset that includes equal representation of women and men. The system should also remove gender-identifying words from resumes so decisions are based on skills and experience rather than gender-related patterns.

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Let marginalized communities act as the "discriminator" when reviewing AI output to counteract existing AI biases

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Rather than going more neutral, there could be an explicit hiring call for women or men (by field). Although that is discrimination, this seems to be the most direct way of dealing with the inequalities represented.

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Rather than going more neutral, there could be an explicit hiring call for women or men (by field). Although that is discrimination, this seems to be the most direct way of dealing with the inequalities represented.

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A solution to this for Amazon would be to remove any terms or key points like names, organizations, or just any gendered language. This concept of "blind recruiting" would help train the AI into combing through resumes without having any pre-conceived gender bias because those specific points of information are left out. Have the AI model only look at things like experience, academic background, etc. only.

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Ensure the resumes used to train the AI are diverse. Including an equal number of diverse resumes and training the bot to look for those styles should help. 

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Ensure the resumes used to train the AI are diverse. Including an equal number of diverse resumes and training the bot to look for those styles should help. 

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I would have a separate program proof the resumes and remove any language that specifies gender, then have the second program read through and select candidates without any indication of what gender the applicant is.

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I would expose the AI to a larger category of information that trains against skewed hiring aspects. The information could increase the data/probality of skills, education, and resume.

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Potentially implementing features that watches for discriminatory language from the tool.

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I would consider filtering for bias that may impact the data. By doing this, personal bias can be avoided.