An Introduction to the Risks and Challenges of AI

An Introduction to the Risks and Challenges of AI

- by Mark Diamond, Expert in Artificial Intelligence

Introduction: Pressure for companies to use generative Artificial Intelligence (AI)-assisted applications to gain a competitive advantage (or at least not fall behind versus competitors) is steadily rising, and in 2024, CEOs will push their IT, Legal, Compliance, and Privacy Teams to deploy AI applications now, not later.

While AI promises tremendous innovation and productivity gains, the emerging compliance challenges and risks can feel overwhelming: Seemingly every week, new AI regulations are announced, the copyright and IP issues are only beginning to be addressed by the courts, and companies need to ensure they are using new AI technology both ethically and correctly. This business pressure to deploy AI countered by compliance, risks and accuracy issues is creating a tug-of-war within organizations.

There is a middle route that avoids deploying risky, non-compliant solutions or sitting on the sidelines as their competitors deploy AI. Smart companies today are developing AI Governance programs that drive compliance, identify, and minimize risks, and ensure the ethical, correct, and safe use of these technologies.

Risks and Challenges

AI’s tremendous benefits are being met with an almost equal concern on its risks. Regulators have been rushing to enact laws restricting how it can be used. Furthermore, the courts are only beginning to evaluate AI’s copyright and intellectual property impacts. Finally, AI also raises significant questions on its ethical use as well as correctness and safety.

In examining the risks and challenges of AI, it is important to understand its true capabilities. One analogy for generative AI’s capabilities and limitations is as an intern. Imagine hiring a bright, hardworking, knowledgeable but sometimes naïve intern in the legal department. A risk with our AI intern is that they always want to please, and sometimes will fabricate information. With limited experience, this intern would not be given large or complex tasks.

Rather, she or he would be given finite, specific assignments. Because the intern is new and inexperienced all their work product would need to be reviewed by their (human) manager. The manager would provide feedback, which the intern would use to continually improve their work.

Once one intern has been successful, many more interns can be brought on board, scaling up the work on the given tasks, or having them tackle adjacent areas. Interns could even be hired to check the work of other interns.

While much of the work would still need to be reviewed, a single human assisted by many interns would be much more productive, and even — with the right processes and quality feedback — higher quality work product.

Read the full white paper Creating an AI Governance Program on Insight Jam now.