Generative Ai Models the Risks and Potential Rewards in Business · Table (p.11)
From generative-ai-models-the-risks-and-potential-rewards-in-business.pdf · page 11
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| Copyright | |
| Questions to consider: | |
| Questions abound around who owns content once it’s run | |
| 1 . How can you ensure confidentiality and accuracy are | through generative AI applications, and there’s no one-size-fits- |
| maintained while using generative AI models? | all answer. Terms and conditions vary from tool to tool, and how |
| you use the materials also plays a part. | |
| 2. How can you ensure your generative AI models | |
| comply with growing global regulations? | |
| If content is cut and pasted or mostly unchanged from text | |
| 3. How can you automate reviewing and managing | copyrighted to someone else, this could be considered |
| compliance policies? | |
| plagiarism. It’s difficult to say definitively how much information | |
| 4. What should your workforce know about generative | obtained via a generative AI tool would need to be changed for |
| AI in terms of its risks and benefits? | 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 | |
| Talent implications | |
| and consumers doubt your honesty on all fronts. Further, if | |
| High-quality, expert output can only be achieved with | |
| clients or consumers were to discover you’re simply passing | |
| high-quality, expert queries. Therefore, your organization | |
| along AI-generated information, what’s to stop them from doin g | |
| will need to upskill its workforce and retain proprietary | |
| the same and cutting the middleperson (your organization) out | |
| knowledge to contextualize the query and provide the right | |
| entirely? | |
| prompts. At KPMG, for example, we’ve made generative AI | |
| training available to all our people through our Digital and | In the next subsection we delve more into the reputational risk s |
| Data Foundations program, which provides foundational | associated with generative AI. |
| content on the evolution of AI and how to build, implement | |
| and engage with trustworthy AI. | |
| Financial, brand and reputational risk | |
| Professionals need to be made aware that they’re not just | |
| If you or someone in your organization were to copy AI- | |
| using a solution — they’re training and evolving it. | |
| produced information or code into any deliverable or product, | |
| In a generative future, we anticipate that the role of | it may constitute copyright or other intellectual property |
| professionals will shift from problem solving to problem | infringement. This could potentially cause your organization |
| defining as teams work alongside machines to create new | legal and reputational harm. |
| approaches. Generative AI tools are an interface, not an oracle. | |
| Though many of these tools specifically tell users not to enter | |
| The human in the loop brings unique insights and | confidential client information, users with a lack of training |
| understanding to the process that generative AI alone can’t | and understanding of them may inadvertently risk exposing |
| replicate. They provide critical feedback to refine and improve | intellectual property or trade secrets to the public or even a |
| the model over time and ensure the output’s accurate, fair and | competitor. This may lead to lawsuits and could negatively |
| meets the desired goals. | impact your company’s bottom line if current or prospective |
| clients and consumers question whether you can be trusted | |
| Great things can happen when people and technology are | |
| with their sensitive information. | |
| in harmony, and we strongly believe there can be no lasting | |
| change without human ingenuity. | Lack of transparency when using generative AI content can |
| also create reputational issues. Tech publisher CNET was | |
| criticized for quietly using the technology to write more | |
| External risks and considerations | |
| than 70 articles since November 202222 — some of which | |
| Misinformation, bias and discrimination | contained errors — even though the publisher said on its |
| website that a team of editors is involved in the content | |
| As we discuss above, LLMs and LMMs have shared false, | “from ideation to publication.” |
| out-of-date and discriminatory information, but presented with | |
| such authority in a way that even the most skeptical reader | |
| Questions to consider: | |
| could be fooled. | |
| Generative AI can — and has — been used to create | 1 . How can you ensure generative AI applications |
| are managed effectively to avoid financial penalty | |
| deepfake images and videos (when visual content is altered | |
| due to not complying with regulations? | |
| to make it seem that someone said or did something | |
| they didn’t do or say). These images and videos often look | |
| Can you trust the applications you use? 2. | |
| extremely realistic and lack forensic traces left behind in | |
| edited digital media, making them difficult for humans or | 3. How can you proactively manage your |
| applications and be aware of and watching for | |
| even machines to detect.21 | |
| potential bias or discrimination? | |
| Is using generative AI applications in line with 4. | |
| your ethics, values and brand? |
Source Metadata