Industrial Use of Generative AI : Opportunities and Risks
responsible AI usage is well-documented 19 , 20 , 21 , the nuances associated with generative AI , particularly large language models , demand specific consideration .
One of the most discussed issues surrounding generative AI is the accuracy of the generated content . Generative AI , despite its impressive capabilities , can produce misleading or wholly fabricated information , often termed hallucination 22 . Relying on such output without verification can lead to misinformation , incorrect decisions , or even physical harm . It is essential for users to approach the outputs of generative AI with a critical mindset , assessing the content for its relevance , accuracy , and appropriateness before acting upon or disseminating it . This cautious approach can help avoid the pitfalls of acting on or spreading false information .
Another concern is the risk of biased responses 23 . Many large language models , trained on vast and diverse external data set , may contain biases . These biases can inadvertently perpetuate or even amplify stereotypes or misrepresent groups , which can have detrimental effects when deployed in real-world scenarios . Companies using generative AI should establish robust controls and protocols to identify and rectify these biased outputs . Such measures will ensure that employees address these situations in line with organizational ethics and guidelines , enabling AI to fulfill its role without inducing harm or misrepresentation .
Furthermore , privacy and intellectual property remain critical considerations . The vast amount of data used to train these models can sometimes lead them to inadvertently reproduce private or sensitive information . Such occurrences could breach individual privacy , and organizations need to be vigilant about potential leaks of confidential data . Additionally , the use of copyrighted or proprietary data in training these models can give rise to intellectual property issues . The generated content could infringe on copyrights or blur the lines of content ownership . This necessitates a comprehensive understanding of the data sources used in training and the legal implications of the generated content 24 .
5 CLOSING
Generative AI is rapidly reshaping the industrial landscape . It offers unprecedented opportunities for automation , efficiency , and innovation , propelling industries into a new era of production and design . From ideation and design to production and after-service , generative AI tools are creating
19
https :// www . iec . ch / blog / new-isoiec-report-offers-guidance-responsible-adoption-ai
20
https :// www . accenture . com / us-en / services / applied-intelligence / ai-ethics-governance
21
Baxter , Kathy and Schlesinger , Yoav . Managing Risks of Generative AI . s . l . : Harvard Business Review , 2023
22
https :// techcrunch . com / 2023 / 09 / 04 / are-language-models-doomed-to-always-hallucinate /
23
Biases in Large Language Models : Origins , Inventory , and Discussion . Navigli , Roberto , Conia , Simone and Ross , Björn . 2 , s . l . : Journal of Data and Information Quality , 2023 , Vol . 15
24 https :// mitsloan . mit . edu / ideas-made-to-matter / legal-issues-presented-generative-ai
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