AI Bias Could Put Women’s Lives at Risk –A Challenge for Regulators

AI bias could put women’s lives at risk and can also contribute to setbacks in women’s empowerment

AI (Artificial intelligence) bias could put women’s lives at risk can be described as a system that has a certain kind of intelligence, a behavior to analyze its environment, and being able to take actions with some degree of autonomy. The learning mechanism in the background of the system can be set up in different ways. 


The easiest way of learning is the process of trial and error, in which the system learns by memorizing different solutions, which of those to recall for which problem. A more challenging learning method is the so-called generalization. By doing so, those past experiences are applied to analogous new situations.


If something is biased, it means ‘preferring or disliking someone or something more than someone or something else, in a way that means that they are treated unfairly or ‘giving results that are not accurate because the information has not been collected correctly’, as by the Cambridge Dictionary. 


 First, cognitive biases were either by the designers unknowingly introduced in the model or because a training data set includes those biases. The second possibility of a bias in AI is a lack of complete data, meaning to not be representative. AI bias could put women’s lives at risk is a human-made problem, as ‘humans carry (un)conscious biases and behave accordingly’ and by that ‘human biases find their way into the historical data that is used to train algorithms.


An example of AI bias that could put women’s lives at risk concerning gender inequality would be Amazon’s recruiting algorithm that learned by historical data of technical roles being filled by males, that resumes including the word ‘women’ are to be penalized. Detection of those differences can show ‘long-standing stereotyped views and prejudice in society, which might end up in discrimination.


Gender bias in human-made systems is a well-known problem, and several examples are showing that it may harm women. For example, car seatbelts, headrests, and airbags are all designed according to the male’s physique and seat position. This measurement is used as a ‘standard’ that does not fit for women’s breasts and pregnant bodies. Resulting, ‘women are 47% more likely to be seriously injured and 17% more likely to die than a man in a similar accident. AI bias could put women’s lives at risk for various reasons:


Care delays and errors:


Doctors tend to misdiagnose, underdiagnose, or under-treat diseases that predominantly affect women or in situations where women present with different symptoms for common diseases. For example, heart attacks are more likely to be misdiagnosed in women, because they don’t always experience the typical (male) symptom of chest pain. It takes four to 10 years to diagnose endometriosis, a gynecological disease that affects one in 10 women, partly because many physicians dismiss or doubt reports of severe or chronic pain by female patients. Compared to men, women who report chronic pain are prescribed less, and less effective, pain medication.


Adverse drug reactions: 


Typically, women access health care more than men and are prescribed more drugs; they also experience more adverse effects and, hence, comply less with treatment plans. Women’s risk of adverse effects is 1.5-1.7 times higher than that of men, and eight out of 10 prescription drugs taken off the market during 1997-2001 were more dangerous for women. One risk is that the standard (male) dose may turn out to be too high for women. 


For example, the recommended dose of the sleeping pill Ambien was halved because women metabolize it more slowly. 


Lower survival rates:


Women outlive men on average but experience more disease and disability. They also have lower survival rates for certain diseases, for example, heart disease, which is the leading cause of death in women as well as men 

globally. More women than men die within a year of a heart attack in the U.S. 


The same pattern persists within five years perhaps because women are less likely to be diagnosed correctly or prescribed medications to lower the risk of heart attacks. After a heart attack, caregivers who are typically female are more likely to suffer another heart attack or die within a year.

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