Deploying a multidisciplinary strategy with embedded responsible AI

The risk landscape of AI is broad and evolving. For instance, ML models, which are often developed using vast, complex, and continuously updated datasets, require a high level of digitization and connectivity in software and engineering pipelines. Yet the eradication of IT silos, both within the enterprise and potentially with external partners, increases the attack surface for cyber criminals and hackers. Cyber security and resilience is an essential component of the digital transformation agenda on which AI depends. A second established risk is bias. Because historical social inequities are baked into raw data, they can be codified--and magnified--in automated decisions leading, for instance, to unfair credit, loan, and insurance decisions.