AI Bias

Artificial intelligence is slowly taking over the modern world and economy. Today, more than 37% of businesses and organizations around the world use AI in their activities, a number which will keep increasing given the vast amount of research being done in this area. As we’ve discussed before, AI is already becoming part of our daily lives, by helping the decision making behind loan lending, vehicle driving, and even by improving the performance of small businesses. In a context of rapid expansion, misuse is inevitable. What can we do about AI misuse? What are its consequences?

Hundreds of people gathered for the first lecture at what had become the world’s most important conference on artificial intelligence — row after row of faces. Some were East Asian, a few were Indian, and a few were women. But the vast majority were white men. More than 5,500 people attended the meeting, five years ago in Barcelona, Spain.

Civil liberties activists are suing a company that provides facial recognition services to law enforcement agencies and private companies around the world, contending that Clearview AI illegally stockpiled data on 3 billion people without their knowledge or permission.

Facial recognition technology is increasingly being used by transportation companies to mitigate passenger fears about COVID-19 transmission by offering contactless identity verification. Though the potential benefits of this technology are robust, its implementation comes with some significant worries. The widespread use of facial recognition tech raises concerns over privacy, runaway surveillance, and racial profiling. In this article, the World Economic Forum outlines a framework for how to regulate this technology, including self-assessment questionnaires and the creation of an audit framework that would help validate the effectiveness of the process.

In this video, AI ethics researcher Joy Buolamwini speaks at the 2019 Stanford Institute for Human-Centered Artificial Intelligence.

When MIT researcher, poet, and computer scientist Joy Buolamwini uncovers racial and gender bias in artificial intelligence systems sold by big tech companies, she embarks on a journey alongside pioneering women sounding the alarm about the dangers of unchecked artificial intelligence that impacts us all. Through Joy’s transformation from scientist to steadfast advocate and the stories of everyday people experiencing technical harms, Coded Bias sheds light on the threats A.I. poses to civil rights and democracy.

It's well-established that AI-driven systems are subject to the biases of their human creators - we unttingly write biases into systems by training them on biased data or with rules created by experts with implicit biases. The good news is that more strategic use of AI systems can give us a fresh chance to identify and remove decision biases from the underlying algorithmsf, even if we can't remove them completely from our own habits of mind.

Although AI algorithms seem objective, they may contain biases. The biases can be inadvertent, such as a computer vision program that works less well at identifying minorities or women due to skewed sets of training photos. They can also be insidious, such as programs that reinforce racist home lending practices based on redlined historic data.

In this article, the New York Times speaks to three prominent women in the artificial intelligence industry to hear how they approach bias in their work. Daphne Koller is a co-founder of the online education company Coursera, and the founder and chief executive of Insitro, a company using machine learning to develop new drugs. Olga Russakovsky is an assitant professor in the Department of Computer Science at Princeton University who specializes in computer vision and a co-founder of the AI4ALL foundation that works to increase diversity and inclusion within AI. Timnit Gebru is a former research scientist at Google on the ethical AI team and a co-founder of Black in AI, which promotes people of color in the field.