Can you imagine a just and equitable world where everyone, regardless of age, gender or class, has access to excellent healthcare, nutritious food and other basic human needs? Are data-driven technologies such as artificial intelligence and data science capable of achieving this – or will the bias that already drives real-world outcomes eventually overtake the digital world, too?
Copyright by www.weforum.org
Bias represents injustice against a person or a group. A lot of existing human bias can be transferred to machines because technologies are not neutral; they are only as good, or bad, as the people who develop them. To explain how bias can lead to prejudices, injustices and inequality in corporate organizations around the world, I will highlight two real-world examples where bias in artificial intelligence was identified and the ethical risk mitigated.
In 2014, a team of software engineers at Amazon were building a program to review the resumes of job applicants. Unfortunately, in 2015 they realized that the system discriminated against women for technical roles. Amazon recruiters did not use the software to evaluate candidates because of these discrimination and fairness issues. Meanwhile in 2019, San Francisco legislators voted against the use of facial recognition, believing they were prone to errors when used on people with dark skin or women.
The National Institute of Standards and Technology (NIST) conducted research that evaluated facial-recognition algorithms from around 100 developers from 189 organizations, including Toshiba, Intel and Microsoft. Speaking about the alarming conclusions, one of the authors, Patrick Grother, says: “While it is usually incorrect to make statements across algorithms, we found empirical evidence for the existence of demographic differentials in the majority of the algorithms we studied.”
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To list some of the source of fairness and non-discrimination risks in the use of artificial intelligence, these include: implicit bias, sampling bias, temporal bias, over-fitting to training data, and edge cases and outliers.
Implicit bias
Implicit bias is discrimination or prejudice against a person or group that is unconscious to the person with the bias. It is dangerous because the person is unaware of the bias – whether it be on grounds of gender, race, disability, sexuality or class.
Sampling bias
This is a statistical problem where random data selected from the population do not reflect the distribution of the population. The sample data may be skewed towards some subset of the group. […]
Read more: www.weforum.org
Can you imagine a just and equitable world where everyone, regardless of age, gender or class, has access to excellent healthcare, nutritious food and other basic human needs? Are data-driven technologies such as artificial intelligence and data science capable of achieving this – or will the bias that already drives real-world outcomes eventually overtake the digital world, too?
Copyright by www.weforum.org
In 2014, a team of software engineers at Amazon were building a program to review the resumes of job applicants. Unfortunately, in 2015 they realized that the system discriminated against women for technical roles. Amazon recruiters did not use the software to evaluate candidates because of these discrimination and fairness issues. Meanwhile in 2019, San Francisco legislators voted against the use of facial recognition, believing they were prone to errors when used on people with dark skin or women.
The National Institute of Standards and Technology (NIST) conducted research that evaluated facial-recognition algorithms from around 100 developers from 189 organizations, including Toshiba, Intel and Microsoft. Speaking about the alarming conclusions, one of the authors, Patrick Grother, says: “While it is usually incorrect to make statements across algorithms, we found empirical evidence for the existence of demographic differentials in the majority of the algorithms we studied.”
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
To list some of the source of fairness and non-discrimination risks in the use of artificial intelligence, these include: implicit bias, sampling bias, temporal bias, over-fitting to training data, and edge cases and outliers.
Implicit bias
Implicit bias is discrimination or prejudice against a person or group that is unconscious to the person with the bias. It is dangerous because the person is unaware of the bias – whether it be on grounds of gender, race, disability, sexuality or class.
Sampling bias
This is a statistical problem where random data selected from the population do not reflect the distribution of the population. The sample data may be skewed towards some subset of the group. […]
Read more: www.weforum.org
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