A team of researchers at USC is helping Artificial Intelligence (AI) imagine the unseen, a technique that could also lead to fairer AI, new medicines and increased autonomous vehicle safety. Now, imagine the same cat, but with coal-black fur. Now, imagine the cat strutting along the Great Wall of China. Doing this, a quick series of neuron activations in your brain will come up with variations of the picture presented, based on your previous knowledge of the world. In other words, as humans, it's easy to envision an object with different attributes.

A team of researchers at USC is helping AI imagine the unseen, a technique that could also lead to fairer AI, new medicines and increased autonomous vehicle safety. Now, imagine the same cat, but with coal-black fur. Now, imagine the cat strutting along the Great Wall of China. Doing this, a quick series of neuron activations in your brain will come up with variations of the picture presented, based on your previous knowledge of the world. In other words, as humans, it's easy to envision an object with different attributes.

An important consideration for data scientists, businesses, and society as a whole, today centres on how we might establish AI as an indisputable and indispensable force for good in the world. For years, we have seen stories of bots and machines taking over the job market, discriminatory facial recognition technology, and few of us will forget the turn of events with Tay. In most instances where AI has fallen foul, however, the appropriate response is simple and follows a long tradition of noble scientific endeavour. Quite simply, how do we build it better? In every context, better thought aroung data and models leads to improved products and services. When machine learning models for cancer diagnoses show promise, we naturally rally around this positive step and rejoice in the vision of a brighter future because it's a victory that touches us all in some way.

Humans have many kinds of biases. To name just a few, we suffer from confirmation bias, which means that we tend to focus on information that confirms our preconceptions about a topic; from anchoring bias, where we make decisions mostly relying on the first piece of information we receive on that subject; and from gender bias, where we tend to associate women with certain traits, activities, or professions, and men with others. When we make decisions, these types of biases often creep in unconsciously, resulting in decisions that are ultimately unfair and unobjective. These same types of bias can show up in artificial intelligence (AI), especially when using machine learning techniques to program an AI system. A commonly-used technique called "supervised machine learning" requires that AI systems be trained with a large number of examples of problems and solutions.

Increasingly, AI is adopted by our banks and our bosses, by our cars and our courts. Across the board, implicit bias remains a significant and complex problem. Several examples have become emblematic of the ways in which implicit bias can channel AI in a prejudiced direction. The Nikon camera that kept asking whether Taiwanese American blogger Joz Wang and her family members were "blinking" while they were taking photographs, for instance, or the time when Google Photos tagged two black friends as "gorillas." Or take the example of Google search results.