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Machine Learning for Policymakers: What It Is and Why It Matters

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Description:Machine learning can spot cancer. It can translate complex texts. Drive cars. Beat the best human in the world at one of the most complex games ever invented. Devise alien-like designs to create more efficient physical structures. Save energy.The science fiction writer and futurist Arthur C. Clarke wrote, “Any sufficiently advanced technology is indistinguishable from magic.” The accomplishments above can indeed at times seem magical, but they are not. These successes are the result of a combination of innovative algorithms, powerful computers, and rich data. This mixture of algorithms, computers, and data can also, when misapplied or when misconfigured, make significant mistakes with catastrophic consequences. To see machine learning as sorcery rather than as a powerful tool that must be wielded carefully and thoughtfully is to invite enormous risk.Machine learning can also seem magical in another way: it can appear impossible to grasp. Our foundational premise in this paper is that this idea is false and dangerous. For each of the aforementioned achievements, and for several others, we will outline the concepts at play in a way that is accessible to generalists. Not only do we believe it is possible for non-specialists to gain intuition about how machine learning works, we think it is urgent. Several important principles are fundamental to understanding the power of machine learning, the opportunities it offers, and the new policy issues it raises.We proceed as follows. The first section outlines the basics of how machine learning works. It provides some important background on artificial intelligence, and discusses three main types of machine learning algorithms. The second section considers the current state of affairs, identifying areas of great progress in machine learning and in the process distilling important foundational concepts. In so doing, we show the ways in which machine learning has already had an impact on a variety of challenges. Next, we turn to the future. The third section examines how machine learning will affect areas of great importance to policymakers. In particular, we focus on warfighting, healthcare, and policing. The discussion of these three areas shows the breadth of the change still to come, and the need for policymaker engagement.While there are sector-specific reasons for policymaker engagement on machine learning, there are also overarching ones. The fourth section examines the challenges that come with the technology. In particular, it articulates concerns about data availability, privacy, fairness, security, and economic impact that must be carefully managed. Each of these areas, if not addressed by technologists and policymakers, represents a way in which poorly designed or applied machine learning tools could cause real harm. We believe they all deserve significant attention. As such, our conclusion provides recommendations on how policymakers can begin to approach machine learning to best maximize its potential and overcome its dangers.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Machine Learning for Policymakers: What It Is and Why It Matters. To get started finding Machine Learning for Policymakers: What It Is and Why It Matters, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

Pages: 58

Format: PDF, EPUB & Kindle Edition

Publisher: Harvard University Belfer Center for Science and International Affairs

Release: 2017

ISBN

Description: Machine learning can spot cancer. It can translate complex texts. Drive cars. Beat the best human in the world at one of the most complex games ever invented. Devise alien-like designs to create more efficient physical structures. Save energy.The science fiction writer and futurist Arthur C. Clarke wrote, “Any sufficiently advanced technology is indistinguishable from magic.” The accomplishments above can indeed at times seem magical, but they are not. These successes are the result of a combination of innovative algorithms, powerful computers, and rich data. This mixture of algorithms, computers, and data can also, when misapplied or when misconfigured, make significant mistakes with catastrophic consequences. To see machine learning as sorcery rather than as a powerful tool that must be wielded carefully and thoughtfully is to invite enormous risk.Machine learning can also seem magical in another way: it can appear impossible to grasp. Our foundational premise in this paper is that this idea is false and dangerous. For each of the aforementioned achievements, and for several others, we will outline the concepts at play in a way that is accessible to generalists. Not only do we believe it is possible for non-specialists to gain intuition about how machine learning works, we think it is urgent. Several important principles are fundamental to understanding the power of machine learning, the opportunities it offers, and the new policy issues it raises.We proceed as follows. The first section outlines the basics of how machine learning works. It provides some important background on artificial intelligence, and discusses three main types of machine learning algorithms. The second section considers the current state of affairs, identifying areas of great progress in machine learning and in the process distilling important foundational concepts. In so doing, we show the ways in which machine learning has already had an impact on a variety of challenges. Next, we turn to the future. The third section examines how machine learning will affect areas of great importance to policymakers. In particular, we focus on warfighting, healthcare, and policing. The discussion of these three areas shows the breadth of the change still to come, and the need for policymaker engagement.While there are sector-specific reasons for policymaker engagement on machine learning, there are also overarching ones. The fourth section examines the challenges that come with the technology. In particular, it articulates concerns about data availability, privacy, fairness, security, and economic impact that must be carefully managed. Each of these areas, if not addressed by technologists and policymakers, represents a way in which poorly designed or applied machine learning tools could cause real harm. We believe they all deserve significant attention. As such, our conclusion provides recommendations on how policymakers can begin to approach machine learning to best maximize its potential and overcome its dangers.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Machine Learning for Policymakers: What It Is and Why It Matters. To get started finding Machine Learning for Policymakers: What It Is and Why It Matters, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Machine Learning for Policymakers: What It Is and Why It Matters

Machine Learning for Policymakers: What It Is and Why It Matters

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