Ai governance strategy (13 resultados)

Paperback. Condición: new. Paperback. A safety claim about an AI system is only as credible as the measurement that backs it. Everything else in this book follows from that sentence. If a developer says a model is "safe enough to deploy," a regulator says it is "low-risk," or a buyer says it is "fit for purpose," each of those statements is a load-bearing claim about behavior under conditions that have not yet happened. Without a measurement program - without tests that were specified before the system was built, executed by people who can be honest about the results, and reported in a form that outsiders can interrogate - those claims are aspirations dressed up as findings. This book is about how to tell the difference. AI governance discussions in 2026 are crowded with rules, principles, and voluntary commitments. The substance of those commitments, however, almost always reduces to a claim about what an AI system will or will not do. "We will not deploy a model that meaningfully uplifts the creation of biological weapons." "Our system does not exhibit unacceptable bias in hiring contexts." "The model refuses to generate child sexual abuse material." Each of these claims is a measurement claim. Each requires that someone - the developer, an independent lab, a government body - define what the dangerous behavior looks like, design a probe that can elicit it if it is present, run that probe under conditions representative of real use, and report the result. The NIST AI Risk Management Framework treats this measurement function as one of four continuous functions - Govern, Map, Measure, and Manage (National Institute of Standards and Technology, 2023). The framework does not tell organizations what to measure; it tells them that measurement is a precondition for trustworthy AI rather than a downstream activity. The 2024 Generative AI Profile sharpened this for foundation-model systems, calling out evaluation, documentation, and disclosure controls that have to be in place before generative systems are deployed at scale (National Institute of Standards and Technology, 2024). The framework's posture, read carefully, is that without measurement infrastructure there is no risk management - only assertion. The case for treating measurement as infrastructure, not as ornament, has three parts. First, the systems themselves are now too capable and too widely deployed for narrative assurance to substitute for evidence. External evaluators including METR and its ARC Evals predecessor program have published public materials on frontier capability evaluation and dangerous-capability framing (METR, 2023; METR, 2025); the existence of those public evaluation efforts, whatever one thinks of their methodology, implies that the relevant questions cannot be answered by inspection alone. Second, governments have started to build state evaluation capacity - most visibly the UK AI Security Institute, which has positioned itself as a public-facing evaluation body for advanced AI safety and security (UK AI Security Institute, 2025). Third, the academic and open-source community has produced standing benchmark infrastructure, with Stanford's Holistic Evaluation of Language Models (HELM) project running multi-dimensional evaluations across many models and a broad range of scenarios on a continuing basis (Stanford Center for Research on Foundation Models, 2025). Each of these efforts is partial. Each is contested. Together they constitute the first generation of what it would mean to have actual measurement infrastructure for AI - and they make visible how far that infrastructure still has to go. A safety claim about an AI system is only as credible as the measurement that backs it. Everything else in this book follows from that sentence. If a developer says a model is "safe enough to deploy," a regulator says it is "low-risk," or a buyer says it is "fit for purpose," each of those s Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New.

Condición: New.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condición: new. Paperback. This book is the framework volume for an index. The Governance Gap Index is a structured way to compare what 195 nations have written down about artificial intelligence - laws, strategies, policies, ministerial declarations, agency guidance - against what those nations have actually built in the way of enforceable capacity to govern AI systems used inside their borders. It is not a ranking of which countries are "best" at AI. It is not a forecast of which countries will win an AI race. It is not an endorsement of any single regulatory model. It is a measurement instrument, and like any measurement instrument it is only as useful as the honesty with which we describe its limits. The present volume does not publish a complete 195-country scorebook. It defines the dimensions, source classes, coding rules, confidence labels, and responsible-use cautions needed before such a scorebook would be defensible. Chapter 4 includes an illustrative ten-jurisdiction matrix to show how the coding grammar works on real public-source records, but the full country-by-country release is deliberately deferred until every jurisdiction can carry a visible evidence trail. That scope choice is conservative rather than evasive: a governance index is only useful if readers can audit the cells behind the numbers. The premise of the index is uncomfortable: in most jurisdictions, what a government has said about AI and what a government can do about AI are not the same thing, and the distance between the two is itself a policy-relevant variable. A national AI strategy posted to a ministry website is one kind of artifact. A statute with binding effect, an empowered regulator with a budget, a procurement rule that actually constrains how the state buys AI systems, a pathway for citizens to obtain redress - those are different kinds of artifacts. They live in different parts of the governance stack, they require different resources to produce, and they fail in different ways. Conflating them produces the false impression that governance is further along than it is. The Organisation for Economic Co-operation and Development's policy observatory documents hundreds of national AI policy initiatives across its member and partner economies, but the observatory itself is careful to catalogue initiatives rather than to certify their effect (Organisation for Economic Co-operation and Development 2025). UNESCO's Recommendation on the Ethics of Artificial Intelligence, adopted by member states in 2021, lays out normative principles and explicitly contemplates a readiness assessment methodology because the framers anticipated that adopting principles is not the same as implementing them (UNESCO 2021). The European Union's Artificial Intelligence Act, Regulation (EU) 2024/1689, is one of the few instances in which a major jurisdiction has put a binding, risk-tiered statute into force, and even there the question of enforcement capacity - staff, technical expertise, market-surveillance authority - remains an active subject of public debate (European Union 2024). The Governance Gap Index treats these differences as data, not as embarrassments to be smoothed over. There are already serious comparative measures of national AI activity. Oxford Insights publishes the Government AI Readiness Index, which scores governments on dimensions including vision, governance, digital capacity, infrastructure, and skills (Oxford Insights 2024). The International Monetary Fund maintains an AI Preparedness Index that emphasizes the macroeconomic and institutional conditions under which AI adoption proceeds (International Monetary Fund 2024). Stanford's Institute for Human-Centered Artificial Inte This book is the framework volume for an index. The Governance Gap Index is a structured way to compare what 195 nations have written down about artificial intelligence - laws, Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condición: new. Paperback. A safety claim about an AI system is only as credible as the measurement that backs it. Everything else in this book follows from that sentence. If a developer says a model is "safe enough to deploy," a regulator says it is "low-risk," or a buyer says it is "fit for purpose," each of those statements is a load-bearing claim about behavior under conditions that have not yet happened. Without a measurement program - without tests that were specified before the system was built, executed by people who can be honest about the results, and reported in a form that outsiders can interrogate - those claims are aspirations dressed up as findings. This book is about how to tell the difference. AI governance discussions in 2026 are crowded with rules, principles, and voluntary commitments. The substance of those commitments, however, almost always reduces to a claim about what an AI system will or will not do. "We will not deploy a model that meaningfully uplifts the creation of biological weapons." "Our system does not exhibit unacceptable bias in hiring contexts." "The model refuses to generate child sexual abuse material." Each of these claims is a measurement claim. Each requires that someone - the developer, an independent lab, a government body - define what the dangerous behavior looks like, design a probe that can elicit it if it is present, run that probe under conditions representative of real use, and report the result. The NIST AI Risk Management Framework treats this measurement function as one of four continuous functions - Govern, Map, Measure, and Manage (National Institute of Standards and Technology, 2023). The framework does not tell organizations what to measure; it tells them that measurement is a precondition for trustworthy AI rather than a downstream activity. The 2024 Generative AI Profile sharpened this for foundation-model systems, calling out evaluation, documentation, and disclosure controls that have to be in place before generative systems are deployed at scale (National Institute of Standards and Technology, 2024). The framework's posture, read carefully, is that without measurement infrastructure there is no risk management - only assertion. The case for treating measurement as infrastructure, not as ornament, has three parts. First, the systems themselves are now too capable and too widely deployed for narrative assurance to substitute for evidence. External evaluators including METR and its ARC Evals predecessor program have published public materials on frontier capability evaluation and dangerous-capability framing (METR, 2023; METR, 2025); the existence of those public evaluation efforts, whatever one thinks of their methodology, implies that the relevant questions cannot be answered by inspection alone. Second, governments have started to build state evaluation capacity - most visibly the UK AI Security Institute, which has positioned itself as a public-facing evaluation body for advanced AI safety and security (UK AI Security Institute, 2025). Third, the academic and open-source community has produced standing benchmark infrastructure, with Stanford's Holistic Evaluation of Language Models (HELM) project running multi-dimensional evaluations across many models and a broad range of scenarios on a continuing basis (Stanford Center for Research on Foundation Models, 2025). Each of these efforts is partial. Each is contested. Together they constitute the first generation of what it would mean to have actual measurement infrastructure for AI - and they make visible how far that infrastructure still has to go. A safety claim about an AI system is only as credible as the measurement that backs it. Everything else in this book follows from that sentence. If a developer says a model is "safe enough to deploy," a regulator says it is "low-risk," or a buyer says it is "fit for purpose," ea Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - A safety claim about an AI system is only as credible as the measurement that backs it. Everything else in this book follows from that sentence. If a developer says a model is 'safe enough to deploy,' a regulator says it is 'low-risk,' or a buyer says it is 'fit for purpose,' each of those statements is a load-bearing claim about behavior under .…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is the framework volume for an index. The Governance Gap Index is a structured way to compare what 195 nations have written down about artificial intelligence - laws, strategies, policies, ministerial declarations, agency guidance - against what those nations have actually built in the way of enforceable capacity to govern AI systems u. …

Taschenbuch. Condición: Neu. The AI Safety Measurement Problem | What We Can't Test, We Can't Trust | Nimble Books LLC | Taschenbuch | Sprint E | Englisch | 2026 | AI Governance & Strategy | EAN 9798259503489 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …

Taschenbuch. Condición: Neu. The Governance Gap Index | Measuring What 195 Nations Have and Haven't Done About AI | Nimble Books LLC | Taschenbuch | Sprint E | Englisch | 2026 | AI Governance & Strategy | EAN 9798259502925 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…