They can be used to decide everything from which video we're recommended to watch next on YouTube to who should be arrested based on facial recognition software. But these algorithms, and the data used to train them, often replicate the harmful social biases of the engineers who build them. Eliminating this bias from technology is the focus of Code Shift, a new data science lab at Texas A&M University that brings together faculty members and researchers from a variety of disciplines across campus. It's an increasingly critical initiative, said Lab Director Srividya Ramasubramanian, as more of the world becomes automated. Machines, rather than humans, are making many of the decisions around us, including some that are high-risk.

Artificial intelligence (AI) is helping people achieve incredible outcomes. Whether it's improving healthcare or helping achieve sustainability goals, emerging AI technologies are unlocking innovation and economic growth. As AI is used in ways that will have increasingly consequential impacts on people's lives, there is an urgent need for policymakers and industry leaders to align around best practices for mitigating the potential risks of AI bias. Today, BSA is calling on governments to pass legislation to require private sector companies to perform impact assessments on high-risk uses of AI technologies. To aid governments in this effort, BSA today released Confronting Bias: BSA's Framework to Build Trust in AI. The framework details how organizations can perform impact assessments to identify and then mitigate risks of bias that may emerge throughout an AI system's lifecycle.

Now is the time for industry to step forward and work with policymakers to pass legislation to address risks of AI bias, and BSA will help lead this effort. Companies and governments alike should use BSA's AI Risk Management Framework as a playbook for building trust and transparency at every point in the AI lifecycle, from design to deployment,