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Books & Publications
From no-code data science to human-governed AI decision intelligence.
David J. Patrishkoff writes for professionals, students, educators, and leaders who must turn data, AI capability, and operational knowledge into decisions that can survive contact with reality.
From no-code data science to human-governed AI decision intelligence.
David J. Patrishkoff writes for professionals, students, educators, and leaders who must turn data, AI capability, and operational knowledge into decisions that can survive contact with reality.
No-Code Data Science
Think Data, Not Code.
No-Code Data Science helps students, professionals, and educators perform rigorous data and predictive analysis without traditional programming. Using accessible open-source tools such as Orange and JASP, readers learn to prepare and visualize data, discover patterns, test relationships, build and compare predictive models, evaluate results, and communicate evidence clearly.
The book emphasizes analytical thinking—not merely clicking through software. It gives learners a practical way to move from raw data to defensible insight and develop the confidence to apply data science in healthcare, business, education, quality improvement, and other real-world settings.
Think Data, Not Code.
No-Code Data Science helps students, professionals, and educators perform rigorous data and predictive analysis without traditional programming. Using accessible open-source tools such as Orange and JASP, readers learn to prepare and visualize data, discover patterns, test relationships, build and compare predictive models, evaluate results, and communicate evidence clearly.
The book emphasizes analytical thinking—not merely clicking through software. It gives learners a practical way to move from raw data to defensible insight and develop the confidence to apply data science in healthcare, business, education, quality improvement, and other real-world settings.
Unintended Consequences
Forthcoming - How the AI Arms Race Rewards Risky Shortcuts—and Why the Next AI Challenge is Wiser Human-AI Decision-Making
AI can make a weak decision faster, more persuasive, and easier to scale. The danger is not only inaccurate output. It is human self-deception: accepting the first framing, mistaking a proxy measure for the mission, treating confident agreement as genuine challenge, and allowing an unearned recommendation to become action. When those shortcuts enter real systems, unintended consequences can accumulate before anyone notices.
Unintended Consequences shows how to build Human–AI decision-intelligence systems that move toward wisdom without pretending that wisdom can be installed. It offers practical guidance for qualifying the real problem, preserving evidence and context, challenging assumptions, testing causes and alternatives, bounding authority, monitoring action in reality, intervening when conditions change, and carrying only validated lessons forward.
The book introduces Human–AI Decision Filters and Wisdom Loops—a distrust-first decision architecture for helping people and AI earn the right to influence consequential action.
More intelligence can increase capability. It does not guarantee wiser decisions.
Forthcoming - How the AI Arms Race Rewards Risky Shortcuts—and Why the Next AI Challenge is Wiser Human-AI Decision-Making
AI can make a weak decision faster, more persuasive, and easier to scale. The danger is not only inaccurate output. It is human self-deception: accepting the first framing, mistaking a proxy measure for the mission, treating confident agreement as genuine challenge, and allowing an unearned recommendation to become action. When those shortcuts enter real systems, unintended consequences can accumulate before anyone notices.
Unintended Consequences shows how to build Human–AI decision-intelligence systems that move toward wisdom without pretending that wisdom can be installed. It offers practical guidance for qualifying the real problem, preserving evidence and context, challenging assumptions, testing causes and alternatives, bounding authority, monitoring action in reality, intervening when conditions change, and carrying only validated lessons forward.
The book introduces Human–AI Decision Filters and Wisdom Loops—a distrust-first decision architecture for helping people and AI earn the right to influence consequential action.
More intelligence can increase capability. It does not guarantee wiser decisions.