CML Blog

Overview

These are turbulent times for higher education.  There are many accelerators and disruptors that are driving change, especially transformative change.  These disruptors include the use of technology; overcoming educational, economic, and social inequities; new ecosystems for work; large-scale change efforts that impact the entire organization; financial distress and declining public support; climate change; and pandemics.  While each of these serves as a catalyst for change, taken together, they provide major challenges. Transforming for Turbulent Times: An Action Agenda for Higher Education Leaders: Norris, Donald, Gilmour, Joseph (Tim), Baer, Linda: 9781794856974: Amazon.com: Books

Causal AI

Artificial Intelligence (AI) is undoubtedly one of the most transformative technologies of the 21st century. From ChatGPT to self-driving cars, AI has become an integral part of our daily lives. But while AI has made remarkable progress in areas like image recognition and natural language processing, there is still one significant hurdle it has yet to overcome – understanding causality.

Trustworthy ML/AI in Higher Education

Q: What’s CML Insight and its charter? 

Machine learning (ML) has become popular in many industries as a way to improve business outcomes. As machine learning is becoming commoditized, it is important to understand potential downside risks of improperly using machine learning and to proactively design ML/AI systems to improve equity and effectiveness in the real world of heterogeneities. 

For too long, risk predictive modeling has represented the core of machine learning analytics, leaving real-world evidence (RWE) of treatment effectiveness untouched. As many have found out, predictions alone do not lead to student success outcomes, often being used to discourage students. Further, their opaque and nonlinear nature can lead to human suspicions and more often an exercise of explaining scores instead of taking actions. Randomized controlled trials (RCTs) are slow, expensive, and sometimes unethical. Furthermore, population heterogeneities can make such RCT results difficult to replicate. That is, treatments need to be personalized to various student segments and measuring a single treatment across all student groups can dull the true effects of interventions designed for heterogeneous student populations, including students who are likely to experience equity gaps.