- Validity and reliability
- Safety
- Security and resiliency
- Accountability and transparency
- Explainability and interpretability
- Privacy
- Fairness with mitigation of harmful bias
To research the present landscape of responsible AI across the enterprise, MIT Technology Review Insights surveyed 250 business leaders about how they’re implementing principles that ensure AI trustworthiness. The poll found that responsible AI is vital to executives, with 87% of respondents rating it a high or medium priority for his or her organization.
A majority of respondents (76%) also say that responsible AI is a high or medium priority specifically for making a competitive advantage. But relatively few have discovered learn how to turn these ideas into reality. We found that only 15% of those surveyed felt highly prepared to adopt effective responsible AI practices, despite the importance they placed on them.
Putting responsible AI into practice within the age of generative AI requires a series of best practices that leading firms are adopting. These practices can include cataloging AI models and data and implementing governance controls. Firms may profit from conducting rigorous assessments, testing, and audits for risk, security, and regulatory compliance. At the identical time, they must also empower employees with training at scale and ultimately make responsible AI a leadership priority to make sure their change efforts stick.
“Everyone knows AI is essentially the most influential change in technology that we’ve seen, but there’s an enormous disconnect,” says Steven Hall, chief AI officer and president of EMEA at ISG, a world technology research and IT advisory firm. “Everybody understands how transformative AI goes to be and needs strong governance, however the operating model and the funding allocated to responsible AI are well below where they have to be given its criticality to the organization.”
