Despite the many benefits AI technology can provide – for instance, AI models can detect breast cancer more accurately than radiologists – we also need to be aware of the potential negative consequences of AI, including deepfakes and nefarious uses of facial recognition. If an AI system is built in a contrived laboratory environment with data that isn't representative of the target audience, or worse, patterns in the data reflect prejudice, the AI's decisions will also be prejudiced. According to a report by AI Now Institute at New York University, the lack of diverse training data also threatens to worsen the historic underemployment of disabled people. With the right partner and making use of vetted crowdtest communities, companies can quickly access training data at scale and garner iterative feedback from users in real time. It may not be possible to have a completely unbiased human, so it will be hard to build completely unbiased AI algorithms, but by harnessing a diverse and large collection of real human interactions prior to release the industry can certainly do better than it is today.