Please describe your proposed solution.
Do you know why employers and HR pass candidates and applicants through different stages of applications, CV screening, interviews, post-interviews, capstone projects, etc before giving a job or gig to a talent? In fact, this process is so strenuous that research established that nearly 60% of job seekers quit online job applications mid-way due to their length and complexity. Despite this, one would kind of wonder why HRs and employers still insist on such a strenuous process just to give jobs to an applicant. The simple answer to this is that Employers are looking for capacity and trust.
Competence is a combination of capacity and trust. Trust is such a critical factor in the gig and hiring industry that talent teams rank employee referrals as the most important source of hire. This means that employers trust the capacity of other talents that the employees refer to, this we can call referred trust and shows that most employers will prefer hiring people they can trust and are sure of the capacity of the person.
Where and when trust is lacking employers are left with no option but to use the following approaches
APPROACH 1: Resort to using different stages and long processes to build and test the capacity of the potential employees
APPROACH 2: Undertake the process of verifying and validating the individual
APPROACH 3: Collect relevant and irrelevant data of the candidate with the hope of using it to track the trustworthiness and capacity of the talent
APPROACH 4: Give capstone sample projects to potential hires.
PROBLEMS WITH THE ABOVE APPROACHES
All these approaches have been with mard with serious challenges, when looked at critically let's examine these approaches one after the other, to see all the issues with these approaches.
APPROACH 1: As already established in the introduction, research shows that 60% of talents drop out or give up on the process, in there are probably good hires that would have been helpful to the employer.
APPROACH 2: Verifying and validating individual applicants can be a very long and tasking process, in addition to this the employer may not have the capacity to even do this.
APPROACH 3: This speaks directly to the issue of privacy and data breach, talents are forced due to desperation to give out vital and private information in order to get jobs. The most annoying part is that employers get access to data that do not directly contribute to a talent's competence, data like the father's date of birth, mother's maiden name, etc, imagine this info in the hand of a malicious actor and what they can possibly do. Not only this approach also opens doors for bias, both implicit and explicit as employers gravitate towards certain demographics at the expense of others, especially when it comes to certain jobs. This bias can come as a preference towards certain gender or ethnicity when it comes to certain jobs, the issue of bias is so much in hiring that a recent Havard report states that Blacks and persons of color stand a 46% chance of getting job callbacks if their backgrounds are excluded from the CV, unfortunately, Approach 3 only reinforces this bias.
APPROACH 4: The problem with this approach is how exploitative it has become, many bad actors see this as an avenue to get free and unpaid labor in the name of sample projects, the desperate talents work on the samples unknown to them these employers are not willing to employ but are looking for free skills to execute their projects under the guise of sample projects. Unfortunately, there are no regulations against this such of exploration and one can as well argue about its necessity to protect the employer against bad actors on the talent side as well, who lack competence but want the job. Another problem with this approach can be likened to the problem of double spending in blockchain, where talent has to keep doing these sample projects for every employer he is applying to, this double spend problem finds itself even with other approaches, the talent has to keep trying to prove his/her competence every time by going through all of these approaches very single time and even the employer has to go through these long processes every single time.
WHY ZERO KNOWLEDGE PROOF OF COMPETENCE?
This analysis of the approaches establishes the fact that trust at the center is what the employer and the hire need and both are skeptical of each other's intention. It's a game where you either catch or become the catch. But apart from these approaches is there no way to prove competence? This is what this proposal Zero knowledge proof of competence is all about.
At Remostart we help startups/organizations hire inclusively, and we have seen firsthand the exploitation and bias that exists in the hiring industry, we feel the pulse and fears of both the employer and the talents and this is why we have decided to be of help to them.
As the founder of Remostart, while completing my Pioneer program, I noticed how critical the topic of Trust was as well as the topic of self-sovereign identity and decentralized identifiers. I realized that what the employers are trying to do by validating and verifying their employees are actually SSI-related concepts and I figured out there's a way Remostart can become issuers and verifiers on behalf of the organizations, reducing the responsibility of the employer to only the role of one who checks the candidates' competence over the blockchain, as such allowing the employers to validate the talents competence without bureaucracies, continuous repetition, and redundancy.
THE CONCEPT
Zero-knowledge proof of Competence seeks to identify and prove an applicant's level of competence on any skillset while preserving the applicant's sensitive data and allowing employers to check the candidate's competence and fit for the job/gig. The project leverages the principles of self-sovereign identity (SSI) to empower talents with control over their digital identities.
The project will consist of the following identity concepts
(I) Remostart as an Issuer
(ii) Employers as Verifiers
(iii) Talents as Holders
<u>TECHNICAL IMPLEMENTATION OF PROPOSAL</u>
Credential Issuance
Remostart, acting as the issuer, will verify the talent's qualifications, skills, and competence through a trusted and secure process. This process involves testing the talent against a very robust and integrative adaptive test process which helps determine the exact competence of the talent. Once verified, Remostart generates a verifiable credential containing the necessary qualifications, signed using digital signatures. The credential includes specific attributes related to the talent's competence, such as skill, competence, other related parameters like problem-solving skills, technicalities, etc.
The credential will also have metadata that can be validated, these meta-data contain info like the version of the credential, time is taken, independent scores, type of tests done etc This way, the problem of double spend is removed because talents only do these test once and use it to verify ain't other employers.
Zero Knowledge Proofs
To protect privacy, the talent employs zero-knowledge proofs to verify their credentials without disclosing the underlying sensitive information. Zero-knowledge proofs allow the talent to demonstrate possession of specific attributes without revealing the actual data itself. This ensures that only relevant information is disclosed while protecting personal privacy.
Verification Process
When applying for a job, the talent presents their verifiable credential to the employer or validator. The employer initiates the verification process by validating the authenticity and integrity of the credential. Through cryptographic techniques, the employer can verify the issuer's signature, ensuring the credential's legitimacy.
Zero-Knowledge Proof Validation
To assess the talent's competence, the employer requests the talent to provide zero-knowledge proof related to specific attributes or qualifications required for the job. The talent generates and presents the proof without disclosing the sensitive information itself, thereby preserving privacy while satisfying the employer's verification requirements. Imagine this use case for a gig, the confidence and trust on both parties will increase and this is what we seek to address.
Tentative Prototype design
▶ Remoforce - Web - High Fidelity Desing (figma.com)
How does your proposed solution address the challenge and what benefits will this bring to the Cardano ecosystem?
Directly from the challenge setting page, this is what success looks like for this challenge
What does success look like?
Establish key relationships and learnings early in the development cycle, leading to faster and more successful business outcomes.
This is achieved through our proposal as we have the opportunity to put this approach directly to the test in our business, the learnings from both the development and utilization of our proposal will be key in helping this solution.
Also from the challenge setting this is one of the key metric
Key Metrics to measure
- Develop a Proof of Concept, Pilot, or commercial launch
This is exactly in line with our proposal which seeks to develop a prototype and commercially launch this solution for use at Remostart and open-source it to any and every business or person who wants to use it in their own product.
To the Cardano community we will benefit from this project by the number of DID addresses which will be created considering that we are using Atala prism framework for development this goes directly as a benefit to the Cardano community. Also currently we do have about 6000 talents and 100 businesses in our platform this new feature will get between 20-30% of our customers using it, these 20-30% are directly using a Cardano-based product, increasing the utility of our ecosystem.
As the CEO of Remostart, I have completed the Atala PRISM Pioneer Program. And Remostart will hold regular meetings with stakeholders, holders, and verifiers, to work towards adopting Ecosystem Governance and publishing a Governance Framework.
How do you intend to measure the success of your project?
To us the success of this project will be measured by
(I) Deployment of the successful code into github
(ii) Broader adoption of SSI: Every user both the holder and the validator are directly adopting SSI, we will be measuring how many users adopt SSI base on this project
(iii) Cardano community Adoption: We will measure how many developers, talents and businesses specifically in Cardano who will be using this our solution, for talent verification and validation.
(iv) Enhanced privacy and streamlined hiring processes: We will love take stakeholder surveys to measure how much privacy has been enhanced through using our solution and how better streamlined the hiring process has become.
Please describe your plans to share the outputs and results of your project?
Phase 1: Deployment of Issuer and all its dependencies
Time period: 4 months
Phase 2: Deployment of Holder and all its dependencies
Time Period: 2 Months
Phase 3: Deployment of Validator and all its dependencies
Time Period: 2 Months
Phase 4: Test and Deployment
Time Period: 1 month