Based on your book
Practical AI Governance
by Rosenberg, Shoshana
Shoshana Rosenberg moves past the abstract hype surrounding artificial intelligence to focus on the unglamorous, high-stakes reality of implementation. This is a manual for the skeptics and the pragmatists who recognize that technology is only as sound as the oversight guiding it. The writing is clinical and precise, favoring intellectual rigor over fluff. You will feel like you are sitting in on a series of high-level strategy meetings where the focus is on mitigating risk, untangling complex power dynamics, and establishing accountability. It avoids the speculative science fiction trap, instead grounding its arguments in the day-to-day friction of organizational policy. If you are a professional or a student looking to move from broad ethical concerns to actionable, defensible governance frameworks, this book provides the necessary architecture to actually manage these systems instead of just watching them grow.
10 Books similar to 'Practical AI Governance'
Since Practical AI Governance focuses on the mechanics of control and oversight, our selected reading list expands on those foundational anxieties. If you want to see the broader stakes of these policies, books like The Alignment Problem and The Ethical Algorithm bridge the gap between abstract ethics and technical reality. We have curated these titles to provide a complete picture of the AI ecosystem, from the surveillance risks explored in Data and Goliath to the pragmatic integration strategies found in Co-Intelligence. These selections prioritize intellectual depth and real-world impact over trendy, hollow prognostications.
As an Amazon Associate, we earn from qualifying purchases.
by Henry A. Kissinger, Eric Schmidt, and Daniel Huttenlocher
This book provides a high-level, intellectual exploration of how AI is reshaping human society, mirroring the strategic governance concerns found in Rosenberg's work. It offers a broader geopolitical perspective that complements the practical, organizational focus of 'Practical AI Governance'.
Crawford digs into the hidden costs and physical realities of AI, providing a critical counterbalance to purely technical or managerial texts. Readers who appreciate Rosenberg's focus on the 'practical' side of AI will find this essential for understanding the systemic implications of their governance frameworks.
Russell addresses the fundamental 'control problem' in AI, which is the foundational concern driving the need for the governance strategies Rosenberg outlines. It bridges the gap between technical possibility and the necessary regulatory safeguards.
This book offers a deep dive into the technical and ethical challenges of ensuring AI systems act in accordance with human values. It provides the 'why' behind the 'how' of the governance structures detailed in Rosenberg's work.
Love to read on the go?
Explore Kindle e-readers and take your books with you.
As an Amazon Associate, we earn from qualifying purchases.
A concise and accessible primer that contextualizes AI within broader ethical traditions, making it a perfect companion for those implementing governance policies. It helps practitioners move beyond compliance checklists into meaningful ethical decision-making.
by Cathy O'Neil
O'Neil's work is essential reading for anyone interested in the risks of algorithmic bias, which is a core component of AI governance. It highlights the real-world consequences of poor governance, reinforcing the necessity of the frameworks Rosenberg proposes.
This book focuses on data privacy as a fundamental aspect of digital governance, a critical pillar of any robust AI strategy. It provides the ethical and practical arguments needed to justify the privacy-centric policies Rosenberg advocates.
by Michael Kearns and Aaron Roth
Kearns and Roth demonstrate how to mathematically encode ethical principles into algorithms, providing a technical counterpart to the policy-heavy approach of 'Practical AI Governance'. It is perfect for readers who want to understand the 'governance by design' philosophy.
Schneier explores the vast surveillance state and the collection of data, which is the fuel for the AI systems Rosenberg discusses. Understanding these broader surveillance risks is vital for building comprehensive AI governance and security protocols.
Mollick provides a pragmatic, forward-looking view on how organizations can actually integrate AI into their workflows, which is the ultimate goal of effective governance. It offers a more operational, 'boots-on-the-ground' perspective that complements Rosenberg’s structured approach.
Not sure what they've already read?
Let them pick their next favorite with an Amazon Gift Card.
As an Amazon Associate, we earn from qualifying purchases.
As an Amazon Associate, we earn from qualifying purchases.