Indian-Origin Doctors Warn Of Racial Bias In Medical Research In UK According to Public Health England (PHE), those from black, Asian and minority ethnic (BAME) backgrounds are at increased risk of poor outcomes from COVID-19.
How gender bias affects feedback and performance reviews. Gender bias takes many forms in the workplace. Women are commonly perceived to be less suitable –and less capable – in traditionally masculine roles.
After a highly publicized act of racial discrimination by a Starbucks employee against two African American men in one of its stores in 2018, the company closed its 8,000 U.S. coffee shops for a day of unconscious bias training. The company also revised store policies and employee training practices. Harvard Business School professors Francesca Gino and Katherine Coffman discuss what we can learn about unconscious bias in corporate culture.
A study found that female executives were treated less fairly than their male counterparts when it came to accessing bank loans. Male executives are 5% more likely to get a loan approved for their business than females. Of the females that did succeed in getting a loan they were subjected to on average 0.5% higher interest rate. The average female run venture backed company starts with a third less capital and achieves annual revenues 12% higher than those run by men.
Reuters reported in 2018 that an AI recruiting system designed to streamline the recruitment process for Amazon by reading resumes and selecting the best-qualified candidate was unfairly selecting men over women. It transpired that the machine learning had been designed to replicated the hiring practices that Amazon had used over the preceding years, so it inadvertently replicated these biases.
In 2019, Facebook was found to be in contravention of the US constitution, by allowing its advertisers to deliberately target adverts according to gender, race and religion, all of which are protected classes under the country’s legal system. Job adverts for roles in nursing or secretarial work were suggested primarily to women, whereas job ads for janitors and taxi drivers had been shown to a higher number of men, in particular men from minority backgrounds. The algorithm learned that ads for real estate were likely to attain better engagement stats when shown to white people, resulting in them no longer being shown to other minority groups.
In 2019 research by the University of California discovered that AI that was being used to define who got what care across a base of over 200 million patients in the US. Black patients were found to be receiving a lower standard of care. This had happened because black people were being allocated a lower risk score based on the predicted cost of care. Ability to pay had become a determining factor in the model, out weighing the medically higher health risk factors that should have ensured they received the right level of care. Model adjustments enabled the level of bias to be reduced by 84%.
COMPAS (which stands for Correctional Offender Management Profiling for Alternative Sanctions) is used to predict the liklihood of a criminal reoffending. This provides a guide to sentencing decisions. Analysis showed that it was no better than a random number generator. Black defendants were twice as likely to be misclassified in comparison to their white defendants.
Rice University’s School of Social Sciences, reviewed research on various methods for assessing risk among accused or convicted criminals. Actuarial and algorithmic models are used to assess these risks, alongside the professional judgment of parole officers, correctional officers, and psychiatrists. Findings showed that actuarial risk assessments can reduce discrepancies in how the system assesses and treats individuals but also exacerbate existing inequalities, particularly on the basis of socioeconomic status or race.
The Allegheny Family Screening Tool is a model designed to assist social workers and courts in deciding whether a child should be removed from their family because of abusive circumstances. Bias appeared in the model through use of a public dataset that reflected societal factors around middle class families having a higher ability to “hide” abuse by using private health providers. AS such the data saw referrals from non-white, lower socio economic groups over three times as often.