Many a clients have recently reached out to me asking about risks related to AI, risks if an organization does NOT invest in responsible AI, quantification of said risks (if any), examples of AI related mistakes (and any incurred losses), costs for investing in responsible AI solutions, approaches to calculate ROI, etc.
Risks Related to AI
Figure 1: Risks related to AI
As the picture above shows, many of the risks related to AI are the same as in “traditional” data science — poor accuracy of prediction, uncertainty within the
stochastic models, lack of explainability, vulnerability to a series of attacks (including poisoning, extraction & evasion attacks).
With generative AI some new risks are introduced, which are now well documented. From the most popular risk of hallucination, to lack of factuality or faithfulness, and the emerging risks from prompt injection attacks to extract data, jailbreaking of LLMs, etc.
Finally, there is emerging regulation and the demands it puts on enterprises to ensure appropriate oversight before any AI solutions are moved into production.
Read the fulll article on Medium.
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