Generative Ai Models the Risks and Potential Rewards in Business · Page 11

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Generative AI models — the risks and potential rewards in business
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Questions to consider: 
1. 
How can you ensure confidentiality and accuracy are
maintained while using generative AI models? 
2. 
How can you ensure your generative AI models 
comply with growing global regulations? 
3. 
How can you automate reviewing and managing 
compliance policies? 
4. 
What should your workforce know about generative 
AI in terms of its risks and benefits?
 
Talent implications
High-quality, expert output can only be achieved with 
high-quality, expert queries. Therefore, your organization 
will need to upskill its workforce and retain proprietary 
knowledge to contextualize the query and provide the right 
prompts. At KPMG, for example, we’ve made generative AI 
training available to all our people through our Digital and 
Data Foundations program, which provides foundational 
content on the evolution of AI and how to build, implement 
and engage with trustworthy AI. 
Professionals need to be made aware that they’re not just 
using a solution — they’re training and evolving it.
In a generative future, we anticipate that the role of 
professionals will shift from problem solving to problem 
defining as teams work alongside machines to create new 
approaches. Generative AI tools are an interface, not an oracle. 
The human in the loop brings unique insights and 
understanding to the process that generative AI alone can’t 
replicate. They provide critical feedback to refine and improve 
the model over time and ensure the output’s accurate, fair and 
meets the desired goals.
Great things can happen when people and technology are 
in harmony, and we strongly believe there can be no lasting 
change without human ingenuity.
External risks and considerations 
Misinformation, bias and discrimination
As we discuss above, LLMs and LMMs have shared false, 
out-of-date and discriminatory information, but presented with 
such authority in a way that even the most skeptical reader 
could be fooled.
Generative AI can — and has — been used to create 
deepfake images and videos (when visual content is altered 
to make it seem that someone said or did something 
they didn’t do or say). These images and videos often look 
extremely realistic and lack forensic traces left behind in 
edited digital media, making them difficult for humans or 
even machines to detect.21 
Copyright
Questions abound around who owns content once it’s run 
through generative AI applications, and there’s no one-size-fits-
all answer. Terms and conditions vary from tool to tool, and how
you use the materials also plays a part.
If content is cut and pasted or mostly unchanged from text 
copyrighted to someone else, this could be considered 
plagiarism. It’s difficult to say definitively how much information
obtained via a generative AI tool would need to be changed for 
it to legitimately be called your own.
Claiming AI-generated content as your own could raise a host 
of ethical issues. For starters, acting this way isn’t responsible 
or trustworthy and, if it came to light, would likely make clients 
and consumers doubt your honesty on all fronts. Further,