Arguably the most notable example of AI bias is the COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) algorithm used in US court systems to predict the likelihood that a defendant would become a recidivist.
In October 2019, researchers found that an algorithm used on more than 200 million people in US hospitals to predict which patients would likely need extra medical care heavily favored white patients over black patients. While race itself wasn’t a variable used in this algorithm, another variable highly correlated to race was, which was healthcare cost history. The rationale was that cost summarizes how many healthcare needs a particular person has. For various reasons, black patients incurred lower healthcare costs than white patients with the same conditions on average.
Thankfully, researchers worked with Optum to reduce the level of bias by 80%. But had they not been interrogated in the first place, AI bias would have continued to discriminate severely.
2. COMPAS
Arguably the most notable example of AI bias is the COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) algorithm used in US court systems to predict the likelihood that a defendant would become a recidivist.
Due to the data that was used, the model that was chosen, and the process of creating the algorithm overall, the model predicted twice as many false positives for recidivism for black offenders (45%) than white offenders (23%).
3. Amazon’s hiring algorithm
Amazon’s one of the largest tech giants in the world. And so, it’s no surprise that they’re heavy users of machine learning and artificial intelligence. In 2015, Amazon realized that their algorithm used for hiring employees was found to be biased against women. The reason for that was because the algorithm was based on the number of resumes submitted over the past ten years, and since most of the applicants were men, it was trained to favor men over women.
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Arguably the most notable example of AI bias is the COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) algorithm used in US court systems to predict the likelihood that a defendant would become a recidivist.
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Three Real-Life Examples of AI Bias
1. Racism embedded in US healthcare
In October 2019, researchers found that an algorithm used on more than 200 million people in US hospitals to predict which patients would likely need extra medical care heavily favored white patients over black patients. While race itself wasn’t a variable used in this algorithm, another variable highly correlated to race was, which was healthcare cost history. The rationale was that cost summarizes how many healthcare needs a particular person has. For various reasons, black patients incurred lower healthcare costs than white patients with the same conditions on average.
Thankfully, researchers worked with Optum to reduce the level of bias by 80%. But had they not been interrogated in the first place, AI bias would have continued to discriminate severely.
2. COMPAS
Arguably the most notable example of AI bias is the COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) algorithm used in US court systems to predict the likelihood that a defendant would become a recidivist.
Due to the data that was used, the model that was chosen, and the process of creating the algorithm overall, the model predicted twice as many false positives for recidivism for black offenders (45%) than white offenders (23%).
3. Amazon’s hiring algorithm
Amazon’s one of the largest tech giants in the world. And so, it’s no surprise that they’re heavy users of machine learning and artificial intelligence. In 2015, Amazon realized that their algorithm used for hiring employees was found to be biased against women. The reason for that was because the algorithm was based on the number of resumes submitted over the past ten years, and since most of the applicants were men, it was trained to favor men over women.
Explanation:
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