Recent advances in artificial intelligence and machine-learning technologies have fuelled fears.

The objective is to estimate the automation and the sexes risks faced by women and men based on an existing methodology applied to the data. The approach also uses expert consultations in the automatability of occupations, taking into account a wide range of tasks typically associated with those occupations (thus allowing automation risks to vary within occupations). The study finds that 44.4% of women in the paid workforce faced a moderate to high risk of job transformation as a result of automation (50% probability or above), compared with only 34.8% of men. Overall, the gap remains about the same, when comparing women and men with similar characteristics, such as age, education, industry and occupation.

However, several characteristics are associated with greater automation and the sexes risks faced by women relative to men, including being aged 55 or older, having no post-secondary qualifications or post-secondary qualifications other than a degree, having low levels of literacy or numeracy proficiency, having a disability, being a part-time worker, not being in a union or covered by a collective bargaining agreement, and being employed in a small to mid-sized firm.

Literature Review:

Advances in automation and the sexes technology have been at the forefront of recent discussions about the future of work. Generally, the literature review focuses on the implications of this technology on workers’ jobs. Although much of the attention has been focused on the extent to which jobs will be eliminated by automation technology, many researchers note that these technological developments will also create new job tasks and occupations.

Studies that apply the task-based approach have found, that routine tasks are most likely to be replaced by automation technology, resulting in an increased demand for workers who can perform non-routine tasks that complement automated tasks.

Furthermore, jobs that require more abstract and non-routine tasks, such as problem solving, persuasion or caring for others, may be less susceptible to automation than those that consist primarily of routine manual tasks that can be automated easily, such as bookkeeping, clerical work and repetitive tasks in production occupations.

Methods:

The automation and the sexes , include measures of cooperating or collaborating, sharing information, instructing, making speeches, selling products or services, advising people, planning and organizing own activities, planning and organizing activities of others, planning and organizing own time, persuading or influencing people, negotiating with people, solving problems of less than 5 minutes, solving problems of less than 30 minutes, performing physical work for a long period of time, using skill or accuracy with hands or fingers, reading directions or instructions, reading journals or scholarly publications, reading books, reading manuals or reference materials, writing articles for newspapers or newsletters, filling in forms, using advanced mathematics, using the Internet for work-related issues, using a programming language, and participating in real-time discussions on the Internet

As noted, the automation risk probabilities were adjusted only for the 25 tasks, and not by individual and workplace characteristics. Adjusting for tasks provides a conceptually clear measure of automation risk, that is based solely on technological feasibility.

Following these adjustments, the automation risks were estimated for women and men separately. Note: The sample was limited to paid workers aged 18 and older with valid responses for all of the variables used in the analysis.

Results:

The automation risk index is determined entirely by the specific job held by the worker, which is defined as the occupation type and job tasks involved. As a result, gender differences in the risk of automation may reflect, in part, gender differences in occupations.

In general, the occupational distributions of women and men were very different. In fact, it is easier to identify the one major occupational group with similar shares of both sexes—sales and service occupations. Men were more likely to hold jobs classified as trades, transport and equipment operators and related occupations, as well as natural and applied science-related occupations.

In contrast, almost one-third of women worked in business, finance and administration occupations, compared with only men. Similarly, women were employed in occupations in education, law and social, community and government services, compared with only men.