AI readiness is a longtime precedence for the Division of Protection workforce, together with preparation of the workforce to make use of and combine knowledge applied sciences and synthetic intelligence capabilities into skilled and warfighting practices. One problem with figuring out staff skilled in knowledge/AI areas is the dearth of formal certifications held by staff. Employees can develop related data and abilities utilizing non-traditional studying paths, and in consequence civilian and federal organizations can overlook certified candidates. Employees might select to domesticate experience on their very own time with on-line sources, private tasks, books, and so on., in order that they’re ready for open positions even after they lack a level or different conventional certification.
The SEI’s Synthetic Intelligence Division is working to deal with this problem. We lately partnered with the Division of the Air Power Chief Knowledge and AI Workplace (DAF CDAO) to develop a technique to establish and assess hidden workforce expertise for knowledge and AI work roles. The collaboration has had some vital outcomes, together with (1) a Knowledge/AI Cyber Workforce Rubric (DACWR) for evaluation of abilities recognized inside the DoD Cyberworkforce Framework, (2) prototype assessments that seize a knowledge science pipeline (knowledge processing, mannequin creation, and reporting), and (3) a proof-of-concept platform, SkillsGrowth, for staff to construct profiles of their experience and evaluation efficiency and for managers to establish the info/AI expertise they want. We element beneath the advantages of those outcomes.
A Knowledge/AI Cyber Workforce Rubric to Enhance Usability of the DoD Cyber Workforce Improvement Framework
The DoD Cyber Workforce Framework (DCWF) defines knowledge and AI work roles and “establishes the DoD’s authoritative lexicon based mostly on the work a person is performing, not their place titles, occupational sequence, or designator.” The DCWF supplies consistency when defining job positions since totally different language could also be used for a similar knowledge and AI educational and trade practices. There are 11 knowledge/AI work roles, and the DCWF covers a variety of AI disciplines (AI adoption, knowledge analytics, knowledge science, analysis, ethics, and so on.), together with the data, abilities, talents, and duties (KSATs) for every work function. There are 296 distinctive KSATs throughout knowledge and AI work roles, and the variety of KSATs per work function varies from 40 (knowledge analyst) to 75 (AI check & analysis specialist), the place most KSATs (about 62 p.c) seem in a single work function. The KSAT descriptions, nevertheless, don’t distinguish ranges of efficiency or proficiency.
The info/AI cyber workforce rubric that we created builds on the DCWF, including ranges of proficiency, defining primary, intermediate, superior, and professional proficiency ranges for every KSAT.
Determine 1: An Excerpt from the Rubric
Determine 1 illustrates how the rubric defines acceptable efficiency ranges in assessments for one of many KSATs. These proficiency-level definitions help the creation of information/AI work role-related assessments starting from conventional paper-and-pencil checks to multimodal, simulation-based assessments. The rubric helps the DCWF to offer measurement choices {of professional} observe in these work roles whereas offering flexibility for future adjustments in applied sciences, disciplines, and so on. Measurement in opposition to the proficiency ranges can provide staff perception into what they’ll do to enhance their preparation for present and future jobs aligned with particular work roles. The proficiency-level definitions can even assist managers consider job seekers extra persistently. To establish hidden expertise, it is very important characterize the state of proficiency of candidates with some cheap precision.
Addressing Challenges: Confirming What AI Employees Know
Potential challenges emerged because the rubric was developed. Employees want a method to display the power to use their data, no matter the way it was acquired, together with by non-traditional studying paths akin to on-line programs and on-the-job ability improvement. The evaluation course of and knowledge assortment platform that helps the evaluation should respect privateness and, certainly, anonymity of candidates – till they’re able to share info relating to their assessed proficiency. The platform ought to, nevertheless, additionally give managers the power to find wanted expertise based mostly on demonstrated experience and profession pursuits.
This led to the creation of prototype assessments, utilizing the rubric as their basis, and a proof-of-concept platform, SkillsGrowth, to offer a imaginative and prescient for future knowledge/AI expertise discovery. Every evaluation is given on-line in a studying administration system (LMS), and every evaluation teams units of KSATs into at the least one competency that displays day by day skilled observe. The aim of the competency groupings is pragmatic, enabling built-in testing of a associated assortment of KSATs reasonably than fragmenting the method into particular person KSAT testing, which may very well be much less environment friendly and require extra sources. Assessments are supposed for basic-to-intermediate stage proficiency.
4 Assessments for Knowledge/AI Job Expertise Identification
The assessments comply with a primary knowledge science pipeline seen in knowledge/AI job positions: knowledge processing, machine studying (ML) modeling and analysis, and outcomes reporting. These assessments are related for job positions aligned with the info analyst, knowledge scientist, or AI/ML specialist work roles. The assessments additionally present the vary of evaluation approaches that the DACWR can help. They embrace the equal of a paper-and-pencil check, two work pattern checks, and a multimodal, simulation expertise for staff who will not be snug with conventional testing strategies.
On this subsequent part, we define a number of of the assessments for knowledge/AI job expertise identification:
- The Technical Abilities Evaluation assesses Python scripting, querying, and knowledge ingestion. It accomplishes this utilizing a piece pattern check in a digital sandbox. The check taker should test and edit simulated personnel and tools knowledge, create a database, and ingest the info into tables with particular necessities. As soon as the info is ingested, the check taker should validate the database. An automatic grader supplies suggestions (e.g., if a desk identify is inaccurate, if knowledge shouldn’t be correctly formatted for a given column, and so on.). As proven in Determine 2 beneath, the evaluation content material mirrors real-world duties which are related to the first work duties of a DAF knowledge analyst or AI specialist.
Determine 2: Making a Database within the Technical Abilities Evaluation
- The Modeling and Simulation Evaluation assesses KSATs associated to knowledge evaluation, machine studying, and AI implementation. Just like the Technical Abilities Evaluation, it makes use of a digital sandbox atmosphere (Determine 3). The principle job within the Modeling and Simulation Evaluation is to create a predictive upkeep mannequin utilizing simulated upkeep knowledge. Check takers use Python to construct and consider machine studying fashions utilizing the scikit-learn library. Check takers might use no matter fashions they need, however they need to obtain particular efficiency thresholds to obtain the very best rating. Automated grading supplies suggestions upon resolution submission. This evaluation displays primary modeling and analysis that may be carried out by staff in knowledge science, AI/ML specialist, and presumably knowledge analyst-aligned job positions.
Determine 3: Making ready Mannequin Creation within the Modeling and Simulation Evaluation
- The Technical Communication Evaluation focuses on reporting outcomes and visualizing knowledge, focusing on each technical and non-technical audiences. It is usually aligned with knowledge analyst, knowledge scientist, and different associated work roles and job positions (Determine 4). There are 25 questions, and these are framed utilizing three query varieties – a number of alternative, assertion choice to create a paragraph report, and matching. The query content material displays widespread knowledge analytic and knowledge science practices like explaining a time period or lead to a non-technical approach, choosing an applicable option to visualize knowledge, and making a small story from knowledge and outcomes.
Determine 4: Making a Paragraph Report within the Technical Communications Evaluation
- EnGauge, a multimodal expertise, is an alternate method to the Technical Abilities and Technical Communication assessments that gives analysis in an immersive atmosphere. Check takers are evaluated utilizing real looking duties in contexts the place staff should make choices about each the technical and interpersonal necessities of the office. Employees work together with simulated coworkers in an workplace atmosphere the place they interpret and current knowledge, consider outcomes, and current info to coworkers with totally different experience (Determine 5). The check taker should assist the simulated coworkers with their analytics wants. This evaluation method permits staff to indicate their experience in a piece context.
Determine 5: Working with a Simulated Coworker within the EnGauge Multimodal Evaluation
A Platform for Showcasing and Figuring out Knowledge/AI Job Expertise
We developed the SkillsGrowth platform to additional help each staff in showcasing their expertise and managers in figuring out staff who’ve needed abilities. SkillsGrowth is a proof-of-concept system, constructing on open-source software program, that gives a imaginative and prescient for the way these wants may be met. Employees can construct a resume, take assessments to doc their proficiencies, and price their diploma of curiosity in particular abilities, competencies, and KSATs. They’ll seek for roles on websites like USAJOBS.
SkillsGrowth is designed to display instruments for monitoring the KSAT proficiency ranges of staff in real-time and for evaluating these KSAT proficiency ranges in opposition to the KSAT proficiencies required for jobs of curiosity. SkillsGrowth can be designed to help use instances akin to managers looking out resumes for particular abilities and KSAT proficiencies. Managers can even assess their groups’ knowledge/AI readiness by viewing present KSAT proficiency ranges. Employees can even entry assessments, which might then be reported on a resume.
In brief, we suggest to help the DCWF by the Knowledge/AI Cyber Workforce Rubric and its operationalization by the SkillsGrowth platform. Employees can present what they know and make sure what they know by assessments, with the info managed in a approach that respects privateness considerations. Managers can discover the hidden knowledge/AI expertise they want, gauge the info/AI ability stage of their groups and extra broadly throughout DoD.
SkillsGrowth thus demonstrates how a sensible profiling and evaluative system may be created utilizing the DCWF as a basis and the CWR as an operationalization technique. Assessments inside the DACWR are based mostly on present skilled practices, and operationalized by SkillsGrowth, which is designed to be an accessible, easy-to-use system.
Determine 6: Checking Private and Job KSAT Proficiency Alignment in SkillsGrowth
Looking for Mission Companions for Knowledge/AI Job Expertise Identification
We are actually at a stage of readiness the place we’re looking for mission companions to iterate, validate, and increase this effort. We want to work with staff and managers to enhance the rubric, evaluation prototypes, and the SkillsGrowth platform. There may be additionally alternative to construct out the set of assessments throughout the info/AI roles in addition to to create superior variations of the present evaluation prototypes.
There may be a lot potential to make figuring out and creating job candidates more practical and environment friendly to help AI and mission readiness. In case you are enthusiastic about our work or partnering with us, please ship an e-mail to data@sei.cmu.edu.
Measuring data, abilities, potential, and job achievement for knowledge/AI work roles is difficult. It is very important take away obstacles in order that the DoD can discover the info/AI expertise it wants for its AI readiness targets. This work creates alternatives for evaluating and supporting AI workforce readiness to realize these targets.
