How Google is looking to ensure AI development is ethical and fair

Following the announcement earlier this week of Google Cloud's AI Hub and Kubeflow Pipelines tools, Rajen Sheth, director of product management for Cloud AI, has outlined how the technology giant is working to ensure that its AI work is ethical and fair. In a blog post earlier this week titled'steering the right course for AI', he outlined what is seen as the main industry challenges to be overcome in order to make AI not just a reality, but one that is for the net good of society. Engaging with each of these in turn, he first suggests that unfair, or confirmation bias must be tackled "on multiple fronts," starting with awareness. "To foster a wider understanding of the need for fairness in technologies like machine learning, we've created educational resources like ml-fairness.com Google is also encouraging thorough documentation "as a means to better understand what goes on inside a machine learning solution". Within Google this takes the form of'model cards': "a standardised format for describing the goals, assumptions, performance metrics, and even ethical considerations of a machine learning model." Embedded documentation tools from Google Cloud, like the Inclusive ML Guide, integrated throughout AutoML, and TensorFlow Model Analysis (TFMA) and the What-If Tool all help with this. "I'm proud of the steps we're taking, and I believe the knowledge and tools we're developing will go a long way towards making AI more fair," he said, before reiterating that this is an industry-wide problem to be tackled. "No single company can solve such a complex problem alone.