Navigating Trustworthiness: A Focus on Ethical Background Verification Practices
Piyush Peshwani is passionate about technology that creates a level-playing field for people to access oppor- tunities and achieve upward mobility in society. Through eLockr, he is envisions the creation of a modern working world where organizations issue digital credentials to people that can be verified instantly with their electronic consent. Piyush was a part of the team that built and rolled out the Aadhaar platform, with close to 1.4 billion people enrolled, and millions of authentication and KYC transactions happening daily.
In a conversation with Charulatha, a correspondent in Siliconindia magazine, Piyush discussed ethical concerns in using advanced tech for background checks and predicted technology's role in verification evolving in the next five years.
What are the potential ethical considerations associated with the use of advanced techno- logies in background verification, and how can they be addressed?
Primary concerns among these are related to privacy, consent, and the potential perpetuation of biases. To address this, a proactive approach is crucial.
Consent can be ensured with implementation of a strong policy. No matter what the mode is for data exchange (HR team providing information and documents, or self-registration by candidate, or BGV API integration with HRMS), a strong policy and trained teams can ensure consent of the candidate in 100% cases.
Regular audits of algorithms for biases, accompanied by continuous training for those overseeing these systems, are imperative to minimize discriminatory outcomes. Adhering to stringent data protection regulations like ISO, DPDP AND GDPR demonstrates a commitment to privacy. Incorporating ethical guidelines into technology development not only fosters responsible practices but also helps build trust in the use of advanced technologies for background verification.
With the rise of remote work, how can background verification processes adapt to ensure the security of virtual workplaces?
Remote onboarding can create risks as individuals with fraudulent intent may take advantage of the scenario, and can damage the organization, or create data security, cyber-theft risks. In such an environment, which has become commonplace after COVID-19, background checks become even more critical.
The first step must be to avoid ID fraud, i.e. the same person who has interviewed is joining the organization. That can be ensured through ID verification, face match, etc. This can be followed by comprehensive background checks such as criminal record checks, previous employment verification, highest education checks, reference checks, address checks, etc.
How might the integration of machine learning algorithms improve the efficiency and effectiveness of background verification processes over time?
By automating tasks like data analysis and validation, ML enhances the speed and precision of checks, significantly reducing processing time. These algorithms excel in pattern recognition, enabling the identification of anomalies or discrepancies that might elude manual scrutiny.
The streamlined automation not only expedites the verification process but also minimizes the potential for human biases, ensuring fair evaluations.
There are checks like identity verification, face match, liveness check, criminal and court record verification (CCRV) that can significantly benefit from technology, and higher efficiency and effectiveness can be achieved.
As technology evolves, the integration of ML stands as a pivotal advancement, offering a data-driven approach that enhances the effectiveness of background verification while maintaining ethical standards.
Considering the dynamic nature of work, how can background verification adapt to accommodate individuals with non-linear career paths and varied experiences?
Instead of relying exclusively on traditional benchmarks like linear employment histories, background verification processes can embrace a more comprehensive approach. This involves assessing skills, accomplishments, and project-based experiences, enabling a thorough evaluation of an individual's capabilities.
Background checks are generally industry agnostic, and can cater extremely well to candidates with non-linear career paths. Even if someone has had an extremely unusual career path, there are checks like CV validation (CVV), professional reference checks (PRC), eLockr reference checks (EREF), or instant employment history checks (EHC) that can be deployed. For PRC, modern BGV systems like OnGrid have a provision for customized questionnaires. Credit history checks (CREC) and social media checks (SMC) can be an instrumental check for assessing candidates who have not been employed consistently, or have taken long breaks.
As global workforces become more common, how can background verification processes be standardized or adapted to meet international standards and regulations?
Standardization requires aligning policies with international regulations such as GDPR, SOC 2 or other geographic-specific norms to maintain uniformity in procedures and compliance measures worldwide. At OnGrid, as we expand our Background Verification (BGV) services globally, we prioritize standardization as well as incorporation of local expertise.
There is a set of checks that are categorized as a standard set of checks for all candidates, irrespective of geography. These include passport verification (PPV), employment verification (EMPV), education verification (EDUV), CV validation (CVV), professional reference checks (PRC) and global database checks (GDC) for legal sanctions, negative media reports, etc. Similarly, digital address checks (LADV /PADV) and social media checks (SMC) can have a completely global reach.
The integration of advanced technologies, customizable to regional requirements, ensures adaptability. Continuous monitoring and regular policy updates are essential to keep our verification processes in sync with evolving international standards. Our commitment to robust data security, encompassing encryption and strict adherence to data residency requirements, addresses concerns associated with cross-border data transfers.
"Blockchain technology will see more practical use cases, adding an extra layer of security for authenticating sensitive candidate information"
How do you foresee the role of technology evolving in background verification processes over the next five years?
The landscape is set for a dynamic evolution in background verification processes. AI and machine learning will play a pivotal role, boosting the efficiency and precision of checks. Automation tools are poised to streamline data collection, analysis, and risk assessment, expediting the overall verification process.
A very significant development would be the usage of lockers like DigiLocker, or eLockr (based on verifiable credentials). Verifiable credentials will be issued by previous employers or education institutions to their alumni, and that will make verifications instant with explicit and electronic consent of the candidates.
Blockchain technology will see more practical use cases, adding an extra layer of security for authenticating sensitive candidate information. Enhanced biometric verification and facial recognition stand to provide robust identity confirmation. With the entrenched nature of remote work, cybersecurity measures will advance to counter digital threats. These converging technological trends herald a transformative era in background verification, ensuring efficiency and adaptability in sync with the evolving modern workforce.
In a conversation with Charulatha, a correspondent in Siliconindia magazine, Piyush discussed ethical concerns in using advanced tech for background checks and predicted technology's role in verification evolving in the next five years.
Remote onboarding can create risks as individuals with fraudulent intent may take advantage of the scenario, and can damage the organization, or create data security, cyber-theft risks
What are the potential ethical considerations associated with the use of advanced techno- logies in background verification, and how can they be addressed?
Primary concerns among these are related to privacy, consent, and the potential perpetuation of biases. To address this, a proactive approach is crucial.
Consent can be ensured with implementation of a strong policy. No matter what the mode is for data exchange (HR team providing information and documents, or self-registration by candidate, or BGV API integration with HRMS), a strong policy and trained teams can ensure consent of the candidate in 100% cases.
Regular audits of algorithms for biases, accompanied by continuous training for those overseeing these systems, are imperative to minimize discriminatory outcomes. Adhering to stringent data protection regulations like ISO, DPDP AND GDPR demonstrates a commitment to privacy. Incorporating ethical guidelines into technology development not only fosters responsible practices but also helps build trust in the use of advanced technologies for background verification.
With the rise of remote work, how can background verification processes adapt to ensure the security of virtual workplaces?
Remote onboarding can create risks as individuals with fraudulent intent may take advantage of the scenario, and can damage the organization, or create data security, cyber-theft risks. In such an environment, which has become commonplace after COVID-19, background checks become even more critical.
The first step must be to avoid ID fraud, i.e. the same person who has interviewed is joining the organization. That can be ensured through ID verification, face match, etc. This can be followed by comprehensive background checks such as criminal record checks, previous employment verification, highest education checks, reference checks, address checks, etc.
How might the integration of machine learning algorithms improve the efficiency and effectiveness of background verification processes over time?
By automating tasks like data analysis and validation, ML enhances the speed and precision of checks, significantly reducing processing time. These algorithms excel in pattern recognition, enabling the identification of anomalies or discrepancies that might elude manual scrutiny.
The streamlined automation not only expedites the verification process but also minimizes the potential for human biases, ensuring fair evaluations.
There are checks like identity verification, face match, liveness check, criminal and court record verification (CCRV) that can significantly benefit from technology, and higher efficiency and effectiveness can be achieved.
As technology evolves, the integration of ML stands as a pivotal advancement, offering a data-driven approach that enhances the effectiveness of background verification while maintaining ethical standards.
Considering the dynamic nature of work, how can background verification adapt to accommodate individuals with non-linear career paths and varied experiences?
Instead of relying exclusively on traditional benchmarks like linear employment histories, background verification processes can embrace a more comprehensive approach. This involves assessing skills, accomplishments, and project-based experiences, enabling a thorough evaluation of an individual's capabilities.
Background checks are generally industry agnostic, and can cater extremely well to candidates with non-linear career paths. Even if someone has had an extremely unusual career path, there are checks like CV validation (CVV), professional reference checks (PRC), eLockr reference checks (EREF), or instant employment history checks (EHC) that can be deployed. For PRC, modern BGV systems like OnGrid have a provision for customized questionnaires. Credit history checks (CREC) and social media checks (SMC) can be an instrumental check for assessing candidates who have not been employed consistently, or have taken long breaks.
As global workforces become more common, how can background verification processes be standardized or adapted to meet international standards and regulations?
Standardization requires aligning policies with international regulations such as GDPR, SOC 2 or other geographic-specific norms to maintain uniformity in procedures and compliance measures worldwide. At OnGrid, as we expand our Background Verification (BGV) services globally, we prioritize standardization as well as incorporation of local expertise.
There is a set of checks that are categorized as a standard set of checks for all candidates, irrespective of geography. These include passport verification (PPV), employment verification (EMPV), education verification (EDUV), CV validation (CVV), professional reference checks (PRC) and global database checks (GDC) for legal sanctions, negative media reports, etc. Similarly, digital address checks (LADV /PADV) and social media checks (SMC) can have a completely global reach.
The integration of advanced technologies, customizable to regional requirements, ensures adaptability. Continuous monitoring and regular policy updates are essential to keep our verification processes in sync with evolving international standards. Our commitment to robust data security, encompassing encryption and strict adherence to data residency requirements, addresses concerns associated with cross-border data transfers.
"Blockchain technology will see more practical use cases, adding an extra layer of security for authenticating sensitive candidate information"
How do you foresee the role of technology evolving in background verification processes over the next five years?
The landscape is set for a dynamic evolution in background verification processes. AI and machine learning will play a pivotal role, boosting the efficiency and precision of checks. Automation tools are poised to streamline data collection, analysis, and risk assessment, expediting the overall verification process.
A very significant development would be the usage of lockers like DigiLocker, or eLockr (based on verifiable credentials). Verifiable credentials will be issued by previous employers or education institutions to their alumni, and that will make verifications instant with explicit and electronic consent of the candidates.
Blockchain technology will see more practical use cases, adding an extra layer of security for authenticating sensitive candidate information. Enhanced biometric verification and facial recognition stand to provide robust identity confirmation. With the entrenched nature of remote work, cybersecurity measures will advance to counter digital threats. These converging technological trends herald a transformative era in background verification, ensuring efficiency and adaptability in sync with the evolving modern workforce.
