AI in Higher Education
Artificial intelligence (AI) has emerged as a truly transformative force in higher education, offering unprecedented opportunities for personalized student engagement, sophisticated data analysis, and crucial resource optimization. However, the rapid evolution of AI technology brings with it significant concerns about ethical implications, bias, and data privacy, prompting the immediate need for a strategic framework that prioritizes responsible AI governance.
In 2023, RNL took a significant step forward by investing heavily in a robust strategic framework that includes comprehensive AI governance. RNL has since established a dedicated AI team committed to implementing ethical and responsible AI practices across our organization and for our partners. This commitment is guided by established standards, drawing inspiration from the principles of the National Institute of Standards and Technology (NIST) AI Risk Management Framework to ensure rigor and accountability.
The F.E.A.T Principles
As advocates for responsible AI usage, RNL adheres to the F.E.A.T. principles* —Fairness, Empathy, Accountability, and Transparency.
Fairness
Recognizing the pervasive issue of bias in AI systems, RNL prioritizes fairness by meticulously addressing bias in data and algorithms. By expanding our perspective beyond the machine learning pipeline, we aim to mitigate bias originating from human, systemic, and societal factors.
Empathy
Understanding the impact of AI on various stakeholders is paramount. RNL emphasizes the ethical considerations of AI deployment, ensuring that digital assistants and other AI tools uphold standards of empathy and respect in interactions with students, families, alumni, and colleagues.
Accountability
RNL advocates for accountability by upholding the highest standards of integrity and accuracy in information dissemination. Regular auditing and assessments of AI models and tools are conducted to prevent misuse and ensure compliance with regulatory standards.
Transparency
Transparency is key to fostering trust in AI systems. RNL is committed to providing comprehensive documentation, training data, and root cause analysis in case of discrepancies or errors, thereby promoting transparency and traceability in AI governance.
As AI continues to shape the landscape of higher education, RNL remains steadfast in its commitment to responsible AI usage.
*Adopted from the Monetary Authority of Singapore
RNL’s AI Governance Framework
AI governance ensures that AI technologies align with the organizational goals of an institution using it. At RNL, our AI governance framework includes:
RNL’s approach to responsible AI is built on a governance framework and it is used for every idea that we think might make sense. Our process is:
- Build experiments to test the idea, under the leadership of a dedicated research scientist.
- Validate the idea through the results of the experimentation.
- Measure the results and use them as the basis to talk about the potential drawbacks.
- Decide on whether or not we’re going to move forward.
The framework is overseen by the RNL AI & Product Council that is committed to integrating and advocating ethical AI. The Council also champions AI awareness within RNL and higher education. Our goal at RNL is clear: to be an innovative leader in the AI landscape while staying informed and compliant with evolving legislation.
RNL also has a partnership with a company called Credo.AI which brings in a governance framework and risk management tool to track all our AI use cases, the models we’re using for each use case, the data that’s behind each of those, and the risks that they pose. They also help us use their general models to do risk assessment.
RNL is using this expertise and experience to develop a consulting service around AI governance. We can go into a university and help them build responsible AI practice, build an AI council, establish methods for evaluating usage of AI models, and track all these efforts.
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