Hello everyone, I am Anandita Aggarwal, and I am excited to present our work
which was a collaboration between the university of Lausanne and the university of Michigan.
OECD countries have experienced growth in high-skilled versus low-skilled jobs with job
seekers earning less than a tertiary education and young people experiencing the most difficulties,
especially those transitioning to high-skilled jobs.
It was predicted that there would be additional risks of unemployment and underemployment for both
men and women despite the fact that unemployment risks for women in most countries is higher.
A failure to address these disparities would lead to deeper social divisions, and thus
negative well-being, social cohesion, and overall growth.
Historically, skills have been central to the discourse around capitalist production and
the labor market. In today’s workplace, skills are no longer limited to technical knowledge;
instead, skills have become “almost anything that can be tested or ranked”
in terms of their capacity to achieve desirable outcomes and profits for employers.
Job seekers without a college degree and ethnic minorities face more difficulty securing jobs
in the current job market changes. Past HCI work calls out the urgency
to support these job seekers and those traditionally excluded from the labor market.
These “underrepresented job seekers” who we refer to as those without university degrees,
low-income job seekers, and immigrant populations were hit hardest by the COVID-19 pandemic.
Indeed, past research suggests that crises exacerbate income inequality.
Thus, interventions to support these job seekers are needed at this time.
Our study took place in Switzerland to understand how a novel technology intervention
focused on skills can support job seekers. In the past few years, the Swiss job market has undergone
important changes leading to growing unemployment rates. The watchmaking industry, which was the 3rd
largest export, was affected by the advent of smartwatches. As a result, the industry
suffered from export reduction and removed a significant portion of the workforce. Thus, many
job seekers needed to transfer their skills to new industries and what reskilling may be required.
SkillsIdentifier was designed as a tool to highlight how a job seeker’s current skill
sets overlap with those needed for their targeted job. The design of SkillsIdentifier
relied on one of the most popular employment databases for job analysis—the U.S. Department
of Labor’s O*NET database. The O*NET database outlined a number of standardized competencies
or skills necessary for each job position. This dataset contained about one thousand occupations
and numerical scores that matched these to skills. Imagine Anna, an immigrant currently working
part-time, and who is looking for a full time position. She lands on SkillsIdentifier’s
home page. The tool allows Anna to enter up to four previous jobs. She enters: security guard,
and janitor as past jobs and nursing assistant as target job. Then she clicks on “continue”.
By way of using the tool, she learns that `active listening’, a skill listed across her past jobs,
was associated with her target job as well. Although she realized that her past jobs
fostered active listening, she did not realize that this skill was also important for nursing.
Our work first aimed to answer the following question: “How do underrepresented job
seekers integrate SkillsIdentifier into their long-term job-seeking practices?”
The past SkillsIdentifier study in the U.S. was a one-time evaluation in a controlled environment.
To extend our understanding of SkillsIdentifier, we conducted a field-deployment to understand
how job seekers use it longitudinally. We designed a three-week deployment to
understand job seekers’ use of SkillsIdentifier. After participants enrolled in the study,
we conducted a virtual pre-treatment session which included a semi-structured interview and tutorial
of SkillsIdentifier. We asked participants about their employment status, job search strategies,
and recent job search experience in the session. We collaborated with the regional employment
center and recruited 16 job seekers who were predominantly women only three job seekers had
bachelor's degree and 11 of them were immigrants. In the next few slides, we will briefly go through
SkillsIdentifier’s benefits and spend more time describing its limitations.
We found that SkillsIdentifier benefited job seekers in three ways:
1. First the tool supported job seekers in developing skill awareness
fostering self reflection and encouraging career exploration. These findings echo the past work
that studied SkillsIdentifier in the U.S. 2. Second, SkillsIdentifier assisted skill
appropriation and adaptation for job seekers to rephrase their skills for different occupations.
3. Lastly, SkillsIdentifier was helpful for immigrant job seekers
to identify technical vocabularies to precisely express competencies in their resumes.
We identified two limitations of SkillsIdentifier, not identified in the prior work. First,
participants mentioned that the most basic skills listed by the tool were too generic and that these
would require additional explanation. For instance, P1 was currently working as an
administrative assistant but looking for a new position as a HR assistant. He described
how the basic skills would need to be further explained in the situation of her previous jobs.
We also found that the tool provided limited guidance on explaining soft skills. A majority
of the listed skills were soft skills that were related to inter-personal communication
or high-level problem-solving, such as active listening and public speaking.
Several participants felt these skills were more relevant in a resume than technical skills.
Participants wanted to have a more diversified palette of skills provided by the tool.
Based on our findings, we offer four design implications to better improve SkillsIdentifier
in supporting job seekers. I’ll discuss 2 of the 4 design implications in this presentation.
Please refer to the paper for the third and fourth implications.
First, tools that highlight job seekers’ skills should take
different occupations’ contexts into accounts and provide context-specific details.
In this revised mockup of SkillsIdentifier, when a user is looking for occupations the tool would
show a pop-up window with additional information on occupations to help them understand the job.
Our participants wished the tool could provide additional information that could help describe
fit with the target occupation. Thus, the tool should provide a wider range of competencies.
In the revised mockup, we added a dashboard that presents a report of competencies based
on a job seeker’s prior and targeted jobs. The competencies are organized in the four categories
provided by O*NET (i.e., the US Department of Labor dataset). Each category is color coded.
The job seeker can then visualize their competencies by visiting the “Journey”
page. On the Journey page, past and target occupations are displayed in a timeline
and the key competencies for each occupation are listed.
Job seekers can explore more details of their competencies by hovering on a competency.
In addition to design implications, our findings reveal several broader challenges
faced by underrepresented job seekers within the current labor market structure. Please read
our paper for detail about these challenges’ implications for the post-COVID labor market.
Lastly, we want to call attention to the ethical considerations with the rise of data-driven
hiring technologies. Such technologies perpetuate inequalities based on gender,
race and ethnicity, nationality, disability, and more. Future research should study the seemingly
“invisible” discrimination against underrepresented job seekers.
Our work was funded by the Swiss National Science Foundation, Swiss National Centre of Competence in
Research “Lives”, and the U.S. National Science Foundation. Feel free to contact us if you have
any questions. Thank you