{"id":2524,"date":"2023-02-06T21:29:18","date_gmt":"2023-02-06T21:29:18","guid":{"rendered":"https:\/\/valencelevel.com\/?p=2524"},"modified":"2024-07-17T10:34:34","modified_gmt":"2024-07-17T17:34:34","slug":"ai-bias","status":"publish","type":"post","link":"https:\/\/kopiustech.com\/es\/insights-innovation\/ai-bias\/","title":{"rendered":"Addressing AI Bias – Four Critical Questions"},"content":{"rendered":"
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By Hayley Pike<\/em><\/p>\n\n\n\n

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As AI becomes even more integrated into business, so does AI bias. <\/p>\n\n\n\n

On February 2, 2023, Microsoft released a statement<\/a> from Vice Chair & President Brad Smith about responsible AI. In the wake of the newfound influence of ChatGPT<\/a> and Stable Diffusion<\/a>, considering the history of racial bias in AI technologies is more important than ever.<\/p>\n\n\n\n

The discussion around racial bias in AI has been going on for years, and with it, there have been signs of trouble. Google fired two of its researchers<\/a>, Dr. Timnit Gebru and Dr. Margaret Mitchell after they published research papers outlining how Google\u2019s language and facial recognition AI were biased against women of color. And speech recognition software<\/a> from Amazon, Microsoft, Apple, Google, and IBM misidentified speech from Black people at a rate of 35%, compared to 19% of speech from White people.<\/p>\n\n\n\n

In more recent news, DEI tech startup Textio analyzed ChatGPT<\/a> showing how it skewed towards writing job postings for younger, male, White candidates- and the bias increased for prompts for more specific jobs.<\/p>\n\n\n\n

If you are working on an AI product or project, you should take steps to address AI bias. Here are four important questions to help make your AI more inclusive:<\/p>\n\n\n\n

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  1. Have we incorporated ethical AI assessments into the production workflow from the beginning of the project? <\/strong>Microsoft\u2019s Responsible AI<\/a> resources include a project assessment guide.<\/li>\n\n\n\n
  2. Are we ready to disclose our data source strengths and limitations? <\/strong>Artificial intelligence is as biased as the data sources it draws from. The project should disclose who the data is prioritizing and who it is excluding. <\/li>\n\n\n\n
  3. Is our<\/strong> AI production team diverse? <\/strong>How have you accounted for the perspectives of people who will use your AI product that are not represented in the project team or tech industry?<\/li>\n\n\n\n
  4. Have we listened to diverse AI experts? <\/strong>Dr. Joy Buolamwini<\/a> and Dr. Inioluwa Deborah Raji<\/a>, currently at the MIT Media Lab, are two black female researchers who are pioneers in the field of racial bias in AI.<\/li>\n<\/ol>\n\n\n\n
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    Rediet Adebe<\/a> is a computer scientist and co-founder of Black in AI<\/a>. Adebe sums it up like this<\/a>: <\/p>\n\n\n\n

    “AI research must also acknowledge that the problems we would like to solve are not purely technical, but rather interact with a complex world full of structural challenges and inequalities. It is therefore crucial that AI researchers collaborate closely with individuals who possess diverse training and domain expertise.”<\/p>\n<\/blockquote>\n\n\n\n

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