Digital credentialing is the process of issuing, storing, and verifying electronic credentials that represent an individual’s achievements or skills. Digital credentials can take various forms, such as certificates, diplomas, certifications, or other relevant achievements, and can be shared online through social media, websites, or email. Digital badges represent a popular way to wrap a common language around these different types of credentials along with portability and verification. Digital credentialing has many benefits, such as increasing access to education, enhancing learner motivation, and providing evidence of learning outcomes. However, as artificial intelligence (AI) becomes more involved in the creation and evaluation of digital credentials, some ethical challenges may arise. In this blog post, we will explore some of these challenges and suggest some possible solutions.
Transparency and Explainability
One of the ethical issues of AI is the lack of transparency and explainability of its decisions and actions. AI systems often use complex algorithms and large amounts of data to generate outputs that are not always intelligible to humans. This can pose a problem for digital credentialing, especially when AI is used to assess learners’ performance, assign grades, or award credentials. For example, how can we ensure that the AI system is fair and accurate in its evaluation? How can we provide feedback and justification to the learners and the credential recipients? How can we appeal or correct the AI system’s errors or mistakes?
“How can we ensure that the AI system is fair and accurate in its evaluation?”
One possible solution is to adopt standards and guidelines for the design and development of AI systems for digital credentialing. These standards and guidelines should ensure that the AI system is transparent about its goals, methods, data sources, assumptions, limitations, and outcomes. They should also ensure that the AI system is explainable, meaning that it can provide clear and understandable reasons for its decisions and actions. Moreover, they should ensure that the AI system is accountable, meaning that it can be monitored, audited, evaluated, and regulated by human authorities.
Bias and Discrimination
Another ethical issue of AI is the potential for bias and discrimination in its outputs. AI systems are not neutral; they reflect the values, preferences, and prejudices of their creators, users, and data sources. AI-based decisions are susceptible to inaccuracies, discriminatory outcomes, embedded or inserted bias. This can have negative consequences for digital credentialing, especially when AI is used to influence access to education, employment, or other opportunities. For example, how can we prevent the AI system from discriminating against certain groups of learners based on their gender, race, ethnicity, age, disability, or other characteristics? How can we ensure that the AI system respects the diversity and inclusion of learners and credential recipients? How can we protect the privacy and security of learners’ personal data?
“How can we ensure that the AI system respects the diversity and inclusion of learners and credential recipients?”
One possible solution is to adopt ethical principles and practices for the use and governance of AI systems for digital credentialing. These principles and practices should ensure that the AI system is fair and equitable in its decisions and actions. They should also ensure that the AI system is respectful and inclusive of learners’ rights, dignity, autonomy, and diversity. Furthermore, they should ensure that the AI system is privacy-preserving and secure in its handling of learners’ personal data.
Human Judgment and Agency
A third ethical issue of AI is the impact on human judgment and agency in relation to digital credentialing. As AI systems become more autonomous and intelligent, they may challenge or replace human roles and responsibilities in the creation and evaluation of digital credentials. This can raise questions about the value and validity of human expertise, creativity, and intuition in education. For example, how can we balance the role of human teachers and mentors with that of AI tutors and coaches? How can we preserve the human element in learning and assessment? How can we empower learners to take control of their own learning paths and credentials?
“How can we preserve the human element in learning and assessment?”
One possible solution is to adopt a human-centered approach for the integration of AI systems in digital credentialing. This approach should ensure that the AI system is complementary and supportive of human goals, needs, interests, and abilities. It should also ensure that the AI system is collaborative and interactive with human partners, such as learners, teachers, employers, or other stakeholders. Moreover, it should ensure that the AI system is empowering and enabling for human learning and development.
AI has great potential to enhance digital credentialing by improving efficiency, quality, scalability, and innovation. However, it also poses some ethical challenges that need to be addressed carefully and responsibly. By adopting standards, guidelines, principles, and practices that promote transparency, explainability, accountability, fairness, equity, respect, inclusion, privacy, security, human-centeredness, collaboration, and empowerment, we can harness the power of AI for digital credentialing while minimizing the risks and harms.
About the author:
Jim Daniels has more than 25 years of combined experience in education and credential program development. He is broadly recognized for his significant work and contribution in developing, operationalizing, and managing IBM’s award-winning digital credentialing program, which has issued over 6 million digital credentials worldwide. Since March of 2022, Jim has worked in a consulting and advisory capacity to bring the voice of experience to numerous other global enterprise organizations in their quest to develop high-impact digital credentialing programs. Jim can be contacted directly at email@example.com, or through LinkedIn – www.linkedin.com/in/danielsje