Research & Insights
Evidence-Based AI Leadership
Articles and publications from our team on causal AI, machine learning, and evidence-based analytics
Featured Articles
Insights from our team on transforming data into intelligence
Empowering Water Utilities with Data Science for Sustainable Resource Management
At CML Insight, our mission is to empower organizations with advanced AI and machine learning solutions that drive measurable results. We specialize in partnering with businesses and government organizations, including water utilities, to elevate their data science capabilities and unlock actionable insights. Our expertise in delivering custom predictive and causal models is particularly valuable in addressing the pressing challenges faced by water utilities in today's era of increasing water scarcity and environmental concerns.
Read articleSep 13, 2024
Mike Villarreal
Change is Hard
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 for institutional leaders navigating complex organizational transformations.
Read moreSep 13, 2024
Linda Baer
Preparing for a New Intelligence
As we move forward in the rapid development of artificial intelligence (AI), especially large language models across industries and opportunities, we are being asked to recognize a change in the way knowledge is created. A new collaborative intelligence (CI) will enable humans and machines to work together to solve complex problems and create innovative solutions, but only if we determine and apply the parameters that each brings to the table. This collaborative intelligence combines human creativity, critical thinking, and problem-solving abilities with AI's ability to rapidly apply algorithms, data, and computational power to recognize patterns, suggest best-case solutions, and identify relationships not readily apparent to human processing.
Read moreSep 12, 2024
Linda Baer, Colleen Carmean, David Kil
The Causal AI/ML Revolution in Education
Artificial Intelligence (AI) and Machine Learning (ML) have become ubiquitous in our everyday lives from consumer applications to enterprise systems. While predictive analytics has matured in education, concerns remain around black-box algorithms, trust in prediction scores, and using past data to model an ever-changing future. Today, we are witnessing a new development that has quietly emerged as a proven analytics approach: Causal AI/ML. Unlike predictive analytics which imparts a sense of finality, Causal AI/ML helps students and educators improve outcomes through interventions and feedback that produce high returns on investment. This revolutionary approach offers a major advance in understanding the cause and effect of student success initiatives and the efficacy of edtech investments at scale.
Read moreSep 06, 2024
David Kil, Rupal Shah
Trustworthy ML/AI in Higher Education
CML Insight's mission is to help students do better by democratizing causal machine learning, which focuses on understanding causal relationships between treatment and its impact on student success. This conversation with Dave Kil explores how to ensure ethical, non-biased AI results in higher education while preserving student privacy and data security. Too frequently, ML/AI has been traditionally associated with risk prediction using sensitive student data, especially demographic and other non-malleable data, which can potentially exacerbate equity gaps. This discussion reveals innovative ways of using integrated analytics to lower equity gaps, going beyond just predictions using black-box models to deliver actionable insights in a safe, ethical manner.
Read moreSep 05, 2024
David Kil, Rupal Shah
How to lower student equity gaps through analytics
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. ML in its native form is only as good as the data it learns from, and can suffer from equity gaps when the underlying data is sampled inadequately to represent population heterogeneity. This article explores innovative approaches to lower equity gaps through proper feature engineering, crowdsourcing analytics, and maximizing human-AI synergy in educational settings.
Read moreSep 05, 2024
David Kil
For too long...
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. CML Insight was founded to help institutions go beyond predictive analytics and to democratize causal machine learning (ML) to discover causal insights in heterogeneous populations.
Read moreSep 04, 2024
David Kil
Featured In
Our research and thought leadership in leading publications
ExperiencED by Dr. Jim Stellar
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ASU: Catalyzing a Culture of Care and Innovation Through Prescriptive and Impact Analytics To Create Full-Cycle Learning
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Using AI to Understand What Causes Diseases
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Gartner 2022 Use Causal AI to Go Beyond Correlation-Based Prediction
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2021 Nobel Award Natural experiments help answer important questions for society
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Educause 2021 Why Data Matters for Student Success in a Post-Pandemic World
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Artificial Intelligence and Human Intelligence in Student Success
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Stanford Social Innovation Review The Case for Causal AI
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The AI Tsunami Is Here: Reinventing Education for the Age of AI
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Dialogue at Scale: AI, Soft Skills, and the Future of Assessment
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From Personalized to Precision Learning: Unlocking the Next Transformation in Higher Education
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