Overall, the AIOE provides information on the impact that AI may have on specific tasks and skills that make up an occupation. The importance of AI to the occupation may be higher or lower, but we do not make any argument as to how it might complement or replace the occupation or relate it to productivity. The results can therefore be interpreted as the average exposure of a particular occupation to AI.

One limitation of this and other studies that map occupations to AI exposure using O*NET is that its occupation-specific tasks and abilities are based on the US labor market. A given occupation in the United States is likely to involve a more sophisticated set of tasks and abilities than the same occupation in a developing country. This is particularly the case if occupations are defined very broadly. As a result, this kind of analysis will tend to overestimate the AI exposure of workers in LMICs. We partially address this concern and improve upon previous analyses by using highly detailed 4-digit occupation codes. These provide a closer and more accurate mapping between occupations and AI exposure and reduce the tendency to over-estimate AI exposure in LMICS.

The 4-digit ISCO codes enable the analysis of AI occupational exposure information for 572 different occupations compared to 49 occupational categories if we were to use 2-digit ISCO codes or 9 categories for 1-digit ISCO codes ( Table 1 ). This provides not only more categories but also a greater depth of information as shown in a graphical representation of all AIOE values plotted by 4-digit and 2-digit ISCO codes ( Figure 12 ). The information gain from more detailed ISCO codes can expressed through entropy measures such as the Shannon or Simpson indices. Both can indicate whether a 4-digit ISCO classification is merely a "finer disaggregation" in a nominal sense or adds information on the occupational structure. The increasing values for the Shannon index and the Simpson index confirm the added value of using the 4-digit occupational information index compared to the 1-digit or 2-digit indices. For example, the extension of the occupational categories from 2 digits (49 categories) to 4 digits (572 categories) represents a 55 percent increase in the information gain as measured by the Shannon index. The information gain more than doubles when deepening the ISCO categories from a 1-digit to a 4-digit level as per the Shannon Index ( Table 1 ).

_Table 1: Entropy information gain measures_

||**1 digit ISCO**<br>**code**|**2 digit ISCO**<br>**code**|**4 digit ISCO**<br>**code**|
|---|---|---|---|
|**Number of categories**|9|49|572|
|**Shannon index**|2.01|3.16|4.90|
|**Simpson index (1/n sum of squared probabilities)**|6.54|14.92|55.32|

To get a better picture, we further explore potential concerns about the transferability of the O*NET-based AI Occupational Exposure Index by comparing occupations in the agriculture sector in the United States with occupations in the agriculture sector in low-income countries. The AIOE different between the two groups with a value of 27 for LICs and 41 for the United States. This suggests that although workers are employed in the same sector, they are actually employed in distinctly different occupations within agriculture in LIC compared to the United States. This is 9