CHAPTER 3: METHODOLOGICAL APPROACH
3.3. Michael Webb’s Method
To calculate an occupation’s exposure to a particular technology Webb studied the text of registered patent titles, which he gathered from the Google Patent Public Database, to determine what a given technology can do, and then quantified the extent to which each occupation listed in the O*NET database involves performing tasks of that nature. The method Webb proposed is depicted in Figure 3-1. The approach Webb undertook to calculate occupational exposure measures is discussed in this section.
45
Figure 3-1: Depiction of the Process for the Construction of Occupations’ Exposure to Technology Measures
Source: Webb (2019)
3.3.1. Extracting Verb-Noun Pairs from Patent Titles
Webb opted to make use of registered patent titles exclusively as they displayed a significantly higher signal-to-noise ratio relative to that of other patent text fields, i.e., the title of a patent expresses its main application, whereas other text fields, such as the abstract and description and claims, contains details that were not relevant to goals of the study Webb had undertaken.
Patent texts are further useful as they provide timely projections of a technological application’s commercial relevance. Furthermore, applicants pay a fee for filing a patent, which increases a patent’s predictive value (Maxim et al., 2019:8).
By making use of the Google Patent Public Database (which is provided by the IFI Claims Patent Services), Webb compiled, extracted, and listed the text of registered patent titles which correspond to the relevant automation technologies. Such patent titles might, for example, include “Method for Diagnosing Diseases”, or “Method for Recognising Aircraft”, etc.
46
From the patent titles list that Webb gathered he identified and extracted all verb-noun pairs, e.g., “diagnose, disease”, or “recognise, aircraft”. To extract verb-noun pairs from a text sentence, Webb executed the following sequence of steps:
Firstly, he made use of a natural language processing (NLP) algorithm, specifically a dependency parsing algorithm, to determine the syntactic relations between the words of patent titles (Webb, 2019).
Secondly, he selected the direct object for each verb (if it existed), extracted, and subsequently stored the resulting pair (Webb, 2019).
Thirdly, Webb lemmatised the extracted nouns and verbs of each sentence respectively, so that, for example, the words “playing”, “plays”, and “played” are all stored as “play”. It should be noted that this is an entirely automated process (Webb, 2019)..
The first column in Table 3-1 depicts numerous examples of AI patent titles, while the second column depicts their respective extracted verb-noun pairs.
Table 1-1: Extracted Patent Capabilities in the Form of Verb-Noun Pairs
Text Extracted Pairs
Adaptive system and method for predicting response times in a service environment
predict, time
Method of and apparatus for determining optimum delivery route for articles
determine, route
Device for forecasting total power demand forecast, demand
47
Method and device for classifying images on basis of convolutional neural network
classify, image
A method for diagnosing food allergy diagnose, allergy
Source: Webb (2019) 3.3.2. Extracting Verb-Noun Pairs from Occupation Task Descriptions
Each occupation listed within the O*NET database consists of a combination of different tasks, with each task being described in free-form text. Webb followed the same process to extract verb-noun pairs from these occupational task descriptions. Most occupations delivered between 20 and 40 verb-noun pairs. Ninety-two percent of occupations delivered more than 15 extracted verb-noun pairs, while 76 delivered more than 20 extracted verb-noun pairs (Webb, 2019:16).
3.3.3. Calculating Occupation Exposure Scores
To calculate an occupation’s final exposure score, Webb first calculated an average of its extracted verb-noun pairs task description exposure scores.
This process is depicted in Table 3-2 below, which displays five of the 22 recorded tasks for a Precision Agriculture Technician, an occupation which Webb found to have a relatively high exposure to AI at 1,274 694 (Webb, 2019:14).
Table 2-2: AI Task and Exposure Scores for a Precision Agriculture Technician
Task Weight in
Occupation
Extracted Pairs AI exposure score x 100 Use geospatial technology to
develop soil sampling grids or
0.050 develop, grid 0.050
48 identify sampling sites for testing
characteristics such as nitrogen, phosphorus, or potassium content, ph., or micronutrients.
identify, site test, characteristic
0.234 0.084
Document and maintain records of precision agriculture
information.
0.049 maintain, record 0.000
Analyse geospatial data to determine agricultural
implications of factors such as soil quality, terrain, field
productivity, fertilizers, or weather conditions.
0.048 analyse, datum determine, implication
0.469 0.837
Apply precision agriculture information to specifically reduce the negative environmental impacts of farming practices.
0.048 apply, information reduce, impact
0.000 0.151
Identify areas in need of pesticide treatment by analysing geospatial data to determine insect
movement and damage patterns.
0.038 identify, area analyse, datum determine, movement
0.234 0.469 0.502
Source: Webb (2019)
The second column in Table 3-2 displays the weight of each task in an occupation, which is calculated as an average of the frequency, importance, and relevance of the task. The individual task weights are scaled to sum to a total of one. The third column displays the verb- noun pairs that Webb had extracted from each verb-noun pair, while the fourth column displays each of the extracted pairs’ respective exposure scores (in this case, AI).
49
3.3.4. Measuring Overlap Between Extracted Verb-Noun Pairs
Webb converted the degree of overlap between the extracted verb-noun pairs of patent titles and occupation task descriptions into measures for an occupation’s exposure to automation technologies. The calculated exposure measures were normalised with a mean of zero, i.e., the standardised scores presented in the analyses reflect the number of standard deviations an occupation’s calculated exposure score is above or below the average exposure measure to a certain technology (Maxim et al., 2019:19).
Furthermore, a higher exposure score does not imply that a certain technology has already made significant inroads into an occupation. Nor does it indicate whether human labour will be complimented or substituted once it does so. Instead, an occupation’s exposure score suggests that at least some kind of exposure can be expected. The final exposure score of an occupation thus reflects the intensity of patenting activity directed towards executing the tasks that occupation consists of (Webb, 2019:17). To determine whether such exposure is complimentary or substitutive in its nature, each occupational exposure measure would have to be examined on a case-by-case basis.
As such, Webb’s study merely quantifies an occupation’s potential exposure to the three discussed technological case studies, and not to what extent the adoption of these technologies has occurred, or in what manner labour market structures and the general work environment will be impacted.
Furthermore, even if Webb’s study does not address whether the extent of an occupation’s exposure is positive or negative in nature, Webb’s modelling does recall the precedents set by robots and software in recent decades. He does, however, suggest that further adoption of newly introduced AI technologies could result in major increases in income inequality and job losses, and that this time both routine and non-routine occupations will be affected (Maxim et al., 2019:23).
50