Open source data science: How to reduce bias in AI

Bias is an inherent human trait and can be reflected and embedded in everything we create, particularly when it comes to technology.
Open source data science could help address the issue of bias in the development of artificial intelligence (AI).
Taking an open and collaborative approach to data science can pave the way for fairer and more equitable world.

In his book Don’t Think of an Elephant, the linguist and philosopher George Lakoff argues that: “Frames are mental structures that shape the way we see the world. As a result they shape the goals we seek, the plans we make, the way we act, and what counts as a good or bad outcome of our actions.”

In this light, biases can act as an inherent frame for human beings and are present in real life. Here are some examples:

A recent study identified bias in the dataset of pulse oximetry sensors, that estimate the amount of oxygen in a person’s blood. In Black patients, the sensors did not accurately measure and detect low blood oxygenation, leading to a potential increased risk for hypoxemia – a below-normal level of oxygen. Indeed, the study showed that Black patients had nearly three times the frequency of occult hypoxemia that was not detected by pulse oximetry as White patients.
In a novel loan application experiment conducted by the World Bank in 77 Turkish banks, 35% of the loan officers were biased against female applicants, with women receiving loan amounts of $14,000 lower on average compared with men.

The “nature vs nurture” argument is an age-old debate, especially when it comes to the subject of human bias. And while the answer is not simple, one thing is for certain: we are all biased and we embed those very same biases in almost everything we create.

Bias can even be found in technology, specifically, in artificial intelligence (AI). Look at what DALL-E mini gives you when asked to represent a “painting of a CEO founding a start-up in Europe” and count how many women you see.

AI-generated image illustrating a “painting of a CEO founding a start-up in Europe”. Image: DALL·E mini (Source: weforum.org)

Lakoff continues: “To change our frames is to change all of this. Reframing is social change.”

Following the frame-bias model, addressing bias is therefore key to change society via a new, ethical and responsible approach to technology. It is then necessary to point out the big ‘elephant in the room’ – that has always been there, though ignored – and deal with it.[…]

Bias is an inherent human trait and can be reflected and embedded in everything we create, particularly when it comes to technology.

Open source data science could help address the issue of bias in the development of artificial intelligence (AI).

Taking an open and collaborative approach to data science can pave the way for fairer and more equitable world.