Artificial Intelligence has come a long way in the past years, with Google Assistant for example answering questions even more complex than before. At the same time, Artificial Intelligence is still far from perfect, especially when it concerns issues like bias. In this post I’ll guide you through the important steps to determine whether artificial intelligence is biased as well as introduce you to some of the most groundbreaking books on AI so you can keep your finger on the pulse.
There are a number of ways that bias can creep into an AI system and those responsible for creating it may or may not be aware of it.
As more and more people use Artificial Intelligence (AI) to make decisions that impact their lives, it’s important to understand how bias can creep into a decision-making process. And there are a number of ways that bias can creep into an AI system, which means that those responsible for creating it may or may not be aware of the effect it will have on the decisions they’re making.
The idea of “bias” in AI is something that some people might find unfamiliar. When we talk about the idea of bias in a real-life situation, we usually refer to confirmation bias — the tendency for people to seek out information that confirms what they already believe.
Confirmation bias is one of several biases that affect AI decision-making systems. Another is availability bias — the tendency for AI to favor data that is easy to find because it’s easier to process. The most common type of bias in AI is heuristics — shortcuts that human brain use when making fast decisions.
Artificial intelligence is everywhere, from your inbox (that’s how it knows which emails are spam) to the latest self-driving cars.
It’s also used to screen job candidates, pinpoint security risks and identify criminals. But how reliable is it?
“Machine Bias” is a series of stories by ProPublica about software that makes predictions about people — but with troubling results.
In the first story in the series, “Machine Bias,” ProPublica reports that software used in the United States to predict future criminals is biased against black people. The software, called COMPAS and made by a company called Northpointe, is widely used and predicts the likelihood that someone will reoffend based on answers to questions such as age and criminal history.
Artificial intelligence (AI) systems are smarter and more capable than ever, but they’re also vulnerable to bias. This is largely because the data that developers use to train their AI models is flawed, incomplete and full of human biases.
Simply put, this means that if you teach a computer system something that’s biased or wrong, it will make mistakes accordingly. And when machines start making decisions for us, we need to be sure they understand what they’re doing.
Developers are even more susceptible to these issues than companies using AI products because they’re actually building the systems. Software engineers need to pay careful attention to how their algorithms work and the data used to train them. They must also ensure their software works across a wide range of users, who have different perspectives and experiences.
It’s no secret that artificial intelligence can be biased. There’s been quite a bit of research done in the field, and plenty has been written on the topic — just do a search for “AI bias” to see for yourself. But it isn’t all about what machines are learning from us, it’s also about what we’re learning from them.
The researchers found that search results varied depending on the user’s gender identity — with women being presented with lower-paying jobs than men, even when they searched for the same roles. In fact, women were shown more job ads related to administrative support and service professions, versus men who were shown more ads related to sales and engineering jobs.
Facebook has had problems with ID systems reading African faces as gorillas.
Artificial intelligence (AI) is on the verge of revolutionizing our world. But like any tool AI’s power comes with the potential for harm. While AI can help make decisions fairer, it often reflects and magnifies the biases that already exist in society.
In the past few years we’ve seen multiple examples of algorithms making inaccurate conclusions about people based on their race or gender. Facebook’s face recognition system identified people as gorillas when they were African Americans and Google’s photo app labeled a couple of black people as gorillas.
But bias can also be inadvertent and a result of the way an algorithm functions.
Bias can take two forms. The first is intentional and malicious, where someone deliberately programs an algorithm to discriminate based on gender or race.
But bias can also be inadvertent and a result of the way an algorithm functions. For example, if we don’t have enough data on people of color who get sick with diabetes, our algorithm might make predictions based solely on the data it has available: white patients. Or if we’re trying to predict loan defaults, and women are less likely to default than men, but there are twice as many male applicants, our results will skew toward women because they are underrepresented in the dataset.
Everyone has biases. We can’t help it, because everyone is a product of their time period. AI has biases and these biases happen because the data is similar to the data that was used to train the system. It fails when the data is outside its training data and it will continue to fail until we fix this problem. The most important thing is knowing that AI isn’t perfect and it will create errors in the future.
