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31 - 40 of 70 results for: artificial intelligence

INTLPOL 200: The Social & Economic Impact of Artificial Intelligence (CS 22A)

(Formerly IPS 200.) Recent advances in computing may place us at the threshold of a unique turning point in human history. Soon we are likely to entrust management of our environment, economy, security, infrastructure, food production, healthcare, and to a large degree even our personal activities, to artificially intelligent computer systems. The prospect of "turning over the keys" to increasingly autonomous systems raises many complex and troubling questions. How will society respond as versatile robots and machine-learning systems displace an ever-expanding spectrum of blue- and white-collar workers? Will the benefits of this technological revolution be broadly distributed or accrue to a lucky few? How can we ensure that these systems respect our ethical principles when they make decisions at speeds and for rationales that exceed our ability to comprehend? What, if any, legal rights and responsibilities should we grant them? And should we regard them merely as sophisticated tools or as a newly emerging form of life? The goal of CS22 is to equip students with the intellectual tools, ethical foundation, and psychological framework to successfully navigate the coming age of intelligent machines.
Terms: Win | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Kaplan, J. (PI)

LAW 682B: Discussion: Beyond Neoliberalism

Scholars' and policy makers' thinking about political economy evolves as one understanding of the role of government ceases to reflect people's views of social reality and is superseded by another. The laissez faire thinking of the 19th century was replaced by Keynesian management in response to the Great Depression. After WWII, Keynesian thinking was challenged by what has come to be called "neoliberalism"---a challenge that began to achieve success in the 1970s in response to perceived failures of government, high inflation, and other economic and social woes. By the mid-1980s, neoliberalism had become the new conventional wisdom, and liberals as well as conservatives accepted its core premises: that society consists of atomized individuals competing rationally to advance their own interests; that this behavior, in aggregate, produces good social outcomes and the greatest economic growth; that free markets are therefore the best way to allocate societal resources and government shoul more »
Scholars' and policy makers' thinking about political economy evolves as one understanding of the role of government ceases to reflect people's views of social reality and is superseded by another. The laissez faire thinking of the 19th century was replaced by Keynesian management in response to the Great Depression. After WWII, Keynesian thinking was challenged by what has come to be called "neoliberalism"---a challenge that began to achieve success in the 1970s in response to perceived failures of government, high inflation, and other economic and social woes. By the mid-1980s, neoliberalism had become the new conventional wisdom, and liberals as well as conservatives accepted its core premises: that society consists of atomized individuals competing rationally to advance their own interests; that this behavior, in aggregate, produces good social outcomes and the greatest economic growth; that free markets are therefore the best way to allocate societal resources and government should intervene only to remedy market failures. Disagreements about what constitutes such failures and when and how to correct them persisted, but the general premises were widely embraced by policymakers and politicians (reflected, for example, in the so-called Washington Consensus). Today, this consensus is breaking down. Neoliberal policies have contributed to generating profound wealth inequality and have little to offer to address the perceived negative consequences of globalization and emerging technologies like artificial intelligence and robotics. But what should come next? Our readings in the course will explore a variety of themes related to these debates. How did neoliberalism come to dominate political discourse? What are its core tenets? What kinds of challenges are being presented to them, and what might an alternative approach to political economy for the 21st century look like? Winter Quarter. Five Monday Evenings from 6:30 - 8:30 (precise dates TBD). DISCUSSIONS IN ETHICAL & PROFESSIONAL VALUES COURSES RANKING FORM: To apply for this course, 2L, 3L and Advanced Degree students must complete and submit a Ranking Form available on the SLS website (Click Courses at the bottom of the homepage and then click Consent of Instructor Forms). See Consent Application Form for instructions and submission deadline. Elements used in grading: Class attendance at all sessions and class participation.
Terms: Win | Units: 1 | Grading: Law Mandatory P/R/F

LAW 682L: Discussion: The Ethical Robot

We will consider the developing legal and ethical problems of robots and artificial intelligence (AI), particularly self-directed and learning AIs. How do self-driving cars value human lives? How do we trade off accuracy against other values in predictive algorithms? And how can courts and legislatures set legal rules robots can understand and obey? Spring Quarter. Meeting Dates: TBD. DISCUSSIONS IN ETHICAL & PROFESSIONAL VALUES COURSES RANKING FORM: To apply for this course, 2L, 3L and Advanced Degree students must complete and submit a Ranking Form available on the SLS website (click Courses at the bottom of the homepage and then click Consent of Instructor Forms). See Consent Application Form for instructions and submission deadline. Elements used in grading: Attendance, Class Participation.
Terms: Spr | Units: 1 | Grading: Law Mandatory P/R/F
Instructors: Lemley, M. (PI)

LAW 806N: Policy Practicum: The Future of Algorithms: Navigating Legal, Social and Policy Challenges

Clients: (1) Stanford Machine Learning Group (https://stanfordmlgroup.github.io/); (2) the Center for Automotive Research at Stanford ( https://cars.stanford.edu/); and (3) the Computational Policy Lab ( https://policylab.stanford.edu/). Although much of the media attention surrounding artificial intelligence (AI) tends to focus on the advances being made in industry, major breakthroughs in the field often begin at the university level. Stanford is among the global leaders in this regard. All across campus, teams led by preeminent researchers are deploying projects that apply cutting-edge AI systems to complex and highly challenging social, technical and policy problems. Stanford has pioneered projects ranging from systems aimed at improving palliative care outcomes, to those aimed at improving the ethical decision-making of autonomous vehicles, to those that shape critical decisions in the criminal justice system. Yet the successful deployment of these projects in the real-world is de more »
Clients: (1) Stanford Machine Learning Group (https://stanfordmlgroup.github.io/); (2) the Center for Automotive Research at Stanford ( https://cars.stanford.edu/); and (3) the Computational Policy Lab ( https://policylab.stanford.edu/). Although much of the media attention surrounding artificial intelligence (AI) tends to focus on the advances being made in industry, major breakthroughs in the field often begin at the university level. Stanford is among the global leaders in this regard. All across campus, teams led by preeminent researchers are deploying projects that apply cutting-edge AI systems to complex and highly challenging social, technical and policy problems. Stanford has pioneered projects ranging from systems aimed at improving palliative care outcomes, to those aimed at improving the ethical decision-making of autonomous vehicles, to those that shape critical decisions in the criminal justice system. Yet the successful deployment of these projects in the real-world is deeply intertwined with questions of regulation and legal liability that push existing doctrinal boundaries---from IP to health regulation to due process and civil rights---to their limits. This policy lab seeks to engage with some of the most challenging legal questions and opportunities presented by these emerging technologies. We will work closely with some of Stanford's leading research teams to help them navigate the murky---oftentimes uncharted---legal, regulatory, ethical, and policy waters surrounding the deployment of novel AI applications. In doing so, we will provide extensive legal research support, collaboratively strategize and design deployments, help innovators evaluate and pilot new applications, and ultimately expand access to transformative technologies for populations in serious need. Students will work primarily with clients from Stanford departments at the forefront of studying, developing and deploying AI systems: (1) the Computer Science Department's Stanford Machine Learning Group, led by Andrew Ng (https://stanfordmlgroup.github.io/); (2) the Mechanical Engineering Department's Center for Automotive Research at Stanford, led by Chris Gerdes and Stephen Zoepf ( https://cars.stanford.edu/); and (3) the Management, Science, and Engineering Department's Computational Policy Lab, led by Sharad Goel ( https://policylab.stanford.edu/). We seek to build a collaborative team of diverse backgrounds and skill sets to learn from each other and enhance the overall capacity of the research. We encourage students who are interested in tech policy, entrepreneurship, AI, access to justice, and social impact to join us, including upper-division and graduate students from Law, Computer Science, Electrical Engineering, Mechanical Engineering, MS&E, Public Policy, and the social sciences. Students interested in this policy lab should submit a consent form with a resume and statement of interest to be reviewed by Professor Malone. Law students wishing to undertake R credit will perform additional research for a white paper analyzing the issues and results of the collective research. R credit is possible only by consent of the instructor. After the term begins, and with the consent of the instructor, students accepted into the course may transfer from section (01) into section (02), which meets the R requirement. NOTE: Students may not count more than a combined total of eight units of directed research projects and policy lab practica toward graduation unless the additional counted units are approved in advance by the Petitions Committee. Such approval will be granted only for good cause shown. Even in the case of a successful petition for additional units, a student cannot receive a letter grade for more than eight units of independent research (Policy Lab practicum, Directed Research, Senior Thesis, and/or Research Track). Any units taken in excess of eight will be graded on a mandatory pass basis. For detailed information, see "Directed Research/Policy Labs" in the SLS Student Handbook. CONSENT APPLICATION: To apply for this course, students must complete and submit a Consent Application Form available on the SLS website (Click Courses at the bottom of the homepage and then click Consent of Instructor Forms). See Consent Application Form for instructions and submission deadline. Elements used in grading: Attendance, Performance, Class Participation, Written Assignments, Final Paper.
Terms: Win | Units: 2-3 | Repeatable for credit | Grading: Law Honors/Pass/Restrd Cr/Fail

LAW 806O: Policy Practicum: Administering by Algorithm: Artificial Intelligence in the Regulatory State

Client: Administrative Conference of the United States, https://www.acus.gov/. This policy lab will explore the growing role that artificial intelligence (AI) and related technologies are playing in the federal administrative state. Already, a wide range of federal agencies are utilizing software that uses machine learning and related techniques associated with AI to make and support decisions. Examples include efforts by the Social Security Administration to improve decisional quality in the adjudication of benefits claims, by the EPA to model the toxicity of chemical compounds, and by the IRS to predict tax non-compliance and identify audit targets. Other uses of AI are in the pipeline or already in place but remain outside public view. Such use will almost certainly increase as AI becomes more sophisticated and cheaper and as the private sector's increasing reliance on AI to make decisions presses agencies to keep pace in order to regulate effectively. As agency use of AI proliferat more »
Client: Administrative Conference of the United States, https://www.acus.gov/. This policy lab will explore the growing role that artificial intelligence (AI) and related technologies are playing in the federal administrative state. Already, a wide range of federal agencies are utilizing software that uses machine learning and related techniques associated with AI to make and support decisions. Examples include efforts by the Social Security Administration to improve decisional quality in the adjudication of benefits claims, by the EPA to model the toxicity of chemical compounds, and by the IRS to predict tax non-compliance and identify audit targets. Other uses of AI are in the pipeline or already in place but remain outside public view. Such use will almost certainly increase as AI becomes more sophisticated and cheaper and as the private sector's increasing reliance on AI to make decisions presses agencies to keep pace in order to regulate effectively. As agency use of AI proliferates, administrators, lawyers, and judges will have to ask how well agency deployment of machine learning systems conforms to well-established principles of constitutional and administrative law. Students enrolled in this policy lab will have a unique opportunity to help set the terms of that debate via a first-of-its-kind report to be submitted to the Administrative Conference of the United States (linked here), an independent federal agency charged with recommending improvements to administrative process and procedure. Students will spearhead completion of a report designed to explore the use of AI in the administrative state at multiple levels. The first part of the project will be a mapping exercise, with descriptive and predictive components. The chief descriptive task will be to canvass the hundreds of agencies that make up the federal administrative state and document agency use of AI across a wide range of substantive policy areas. A related, more predictive part of the project will draw on Stanford University's distinctive concentration of technical knowledge in AI and related fields to assess where AI may be most likely to be deployed by agencies in the near- and medium-term. The final part of the project will turn to normative issues, by contributing to a framework for thinking about the many legal, policy-analytic, and philosophical questions raised by agency use of AI to perform regulatory tasks. To the extent possible, we will consider how agency use of AI may affect the administrative state in general terms, and will explore some of the implications of core administrative law doctrines --- such as the nondelegation doctrine, arbitrary and capricious review, due process, and rules governing reliance on subordinates for decisions --- for agency use of AI. Students enrolled in the lab will work in teams, with each allocated a cluster of agencies. As the project unfolds, teams will drill down on practices, both actual and predicted, in specific agencies that exemplify the legal and normative tensions that will arise as agencies increasingly deploy AI technologies. Students will be encouraged to deploy a range of methodologies, including careful secondary and legal research, case studies, survey work, and stakeholder interviews, among others. Some of this work may also require travel to Washington, D.C. or agency regional offices in order to fully understand agency practices or, upon the project's completion, to present findings. The policy lab is open to all graduate students at Stanford University, and will ideally attract both law students and also non-law students from technical fields who can contribute a sophisticated understanding of the current trajectory of AI technology. For law students, past coursework or a strong background or interest in administrative law is highly recommended. Students from all parts of the University who wish to enroll in the policy lab may also consider taking Justice Cuéllar's fall quarter course at the Law School, "Regulating Artificial Intelligence." Law students wishing to undertake R credit will perform additional research or take on additional tasks analyzing the issues and results of the collective research. R credit is possible only by consent of the instructor. After the term begins, and with the consent of the instructor, students accepted into the course may transfer from section (01) into section (02), which meets the R requirement. NOTE: Students may not count more than a combined total of eight units of directed research projects and policy lab practica toward graduation unless the additional counted units are approved in advance by the Petitions Committee. Such approval will be granted only for good cause shown. Even in the case of a successful petition for additional units, a student cannot receive a letter grade for more than eight units of independent research (Policy Lab practicum, Directed Research, Senior Thesis, and/or Research Track). Any units taken in excess of eight will be graded on a mandatory pass basis. For detailed information, see "Directed Research/Policy Labs" in the SLS Student Handbook. CONSENT APPLICATION: To apply for this course, students must complete and submit a Consent Application Form available on the SLS website (Click Courses at the bottom of the homepage and then click Consent of Instructor Forms). See Consent Application Form for instructions and submission deadline. Elements used in grading: Attendance, Performance, Class Participation, Written Assignments, Final Paper.
Terms: Win | Units: 2-3 | Repeatable for credit | Grading: Law Honors/Pass/Restrd Cr/Fail

LAW 1038: The Future of Finance

If you are interested in a career in finance or that touches finance (computational science, economics, public policy, legal, regulatory, corporate, other), this course will give you a useful perspective. We will take on hot topics in the current landscape of global financial markets such as how the world has evolved post-financial crisis, how it is being disrupted by FinTech, RegTech, artificial intelligence, crowd financing, blockchain, machine learning & robotics (to name a few), how it is being challenged by IoT, cyber, financial warfare & crypto currency risks (to name a few) and how it is seizing new opportunities in fast-growing areas such as ETFs, new instruments/payment platforms, robo advising, big data & algorithmic trading (to name a few). The course will include guest-lecturer perspectives on how sweeping changes are transforming business models and where the greatest opportunities exist for students entering or touching the world of finance today including existing, new a more »
If you are interested in a career in finance or that touches finance (computational science, economics, public policy, legal, regulatory, corporate, other), this course will give you a useful perspective. We will take on hot topics in the current landscape of global financial markets such as how the world has evolved post-financial crisis, how it is being disrupted by FinTech, RegTech, artificial intelligence, crowd financing, blockchain, machine learning & robotics (to name a few), how it is being challenged by IoT, cyber, financial warfare & crypto currency risks (to name a few) and how it is seizing new opportunities in fast-growing areas such as ETFs, new instruments/payment platforms, robo advising, big data & algorithmic trading (to name a few). The course will include guest-lecturer perspectives on how sweeping changes are transforming business models and where the greatest opportunities exist for students entering or touching the world of finance today including existing, new and disruptive players. While derivatives and other quantitative concepts will be handled in a non-technical way, some knowledge of finance and the capital markets is presumed. Elements used in grading: Class Participation, Attendance, Final Paper. Consent Application: To apply for this course, students must complete and e-mail the Consent Application Form available on the SLS Registrar's Office website (see Registration) to the instructor(s). Elements used in grading: Class Participation, Attendance, Final Paper. Consent Application: To apply for this course, students must complete and e-mail the Consent Application Form available on the SLS Registrar's Office website (see Registration) to the instructor(s). See Consent Application Form for submission deadline. Cross-listed with Economics ( ECON 152/252), Public Policy ( PUBLPOL 364), Statistics ( STATS 238).
Terms: Win, last offered Winter 2018 | Units: 2 | Grading: Law Honors/Pass/Restrd Cr/Fail

LAW 4031: Disruptive Technologies: Their Impact on Our Laws, and the Laws' Impact on the Technology

The advent of a highly disruptive technology necessarily butts up against existing laws, regulations and policies designed for the status quo as well as established businesses. This course takes the examples of driverless cars and artificial intelligence and examines the new and challenging legal questions and opportunities presented by these technologies. We will also discuss how business leaders, lawyers and technologists in these areas can navigate and create legal, regulatory and policy environments designed to help their businesses not only survive but thrive. Through a combination of readings, classroom discussions, expert guest speakers from the relevant technology and policy fields and student presentations, this course explores the promise of these technologies, the legal and regulatory challenges presented and the levers in-house counsel and business leaders in these fields can invoke to better navigate the inevitable obstacles facing these highly disruptive technologies. The more »
The advent of a highly disruptive technology necessarily butts up against existing laws, regulations and policies designed for the status quo as well as established businesses. This course takes the examples of driverless cars and artificial intelligence and examines the new and challenging legal questions and opportunities presented by these technologies. We will also discuss how business leaders, lawyers and technologists in these areas can navigate and create legal, regulatory and policy environments designed to help their businesses not only survive but thrive. Through a combination of readings, classroom discussions, expert guest speakers from the relevant technology and policy fields and student presentations, this course explores the promise of these technologies, the legal and regulatory challenges presented and the levers in-house counsel and business leaders in these fields can invoke to better navigate the inevitable obstacles facing these highly disruptive technologies. There are no formal prerequisites in engineering or law required, but students should be committed to pursuing novel questions in an interdisciplinary context. Elements used in grading: class preparation and short reflection papers. This course is open to School of Engineering and graduate students with consent of the instructor.
Terms: not given this year, last offered Winter 2018 | Units: 2 | Grading: Law Honors/Pass/Restrd Cr/Fail

LAW 4039: Regulating Artificial Intelligence

Less than a generation ago,"artificial intelligence" (AI) was largely an esoteric topic in academic computer science and philosophy --- and perhaps a more familiar one in science fiction. Today the term is widely understood to describe fast-evolving technologies (such as elaborate convolutional neural networks leveraging vast amounts of data) increasingly used in finance, transportation, health care, national security, and a variety of other fields. This highly interactive new course surveys current and emerging legal and policy problems related to how law structures humanity's relationship to artificially-constructed intelligence. To deepen future lawyers' understanding of current and medium-term problems in this area, the course explores definitions and foundational concepts associated with "artificial intelligence," likely directions in which AI will evolve, and different types of legally-relevant concerns raised by those developments and by the use of existing versions of AI. We wi more »
Less than a generation ago,"artificial intelligence" (AI) was largely an esoteric topic in academic computer science and philosophy --- and perhaps a more familiar one in science fiction. Today the term is widely understood to describe fast-evolving technologies (such as elaborate convolutional neural networks leveraging vast amounts of data) increasingly used in finance, transportation, health care, national security, and a variety of other fields. This highly interactive new course surveys current and emerging legal and policy problems related to how law structures humanity's relationship to artificially-constructed intelligence. To deepen future lawyers' understanding of current and medium-term problems in this area, the course explores definitions and foundational concepts associated with "artificial intelligence," likely directions in which AI will evolve, and different types of legally-relevant concerns raised by those developments and by the use of existing versions of AI. We will consider distinct settings where regulation of AI is emerging as a challenge or topic of interest, including autonomous vehicles, autonomous weapons, AI in social media/communications platforms, and systemic AI safety problems; doctrines and legal provisions relevant to the development, control, and deployment of AI such as the European Union's General Data Protection Regulation; the connection between the legal treatment of manufactured intelligence and related bodies of existing law, such as administrative law, torts, constitutional principles, criminal justice, and international law; and new legal arrangements that could affect the development and use of AI. Cross-cutting themes will include: how law affects the way important societal decisions are justified, the balance of power and responsibility between humans and machines in different settings, the incorporation of multiple values into AI decisionmaking frameworks, the interplay of norms and formal law, and similarities and differences to other domains of human activity raising regulatory trade-offs and affected by technological change (such as environmental protection, aviation, and the food economy). Note: The course is designed both for students who want a survey of the field and lack any technical knowledge, as well as for students who want to gain tools and ideas to deepen their existing interest or background in the topic. Students with longer-term interest in or experience with the subject are welcome to do a more technically-oriented paper or project in connection with this class. But technical knowledge or familiarity with AI is not a prerequisite, as various optional readings and some in-class material will help provide necessary background. Requirements: The course will involves a mix of lectures, in-class activities, and student-led discussion and presentations. Requirements include attendance, participation in planning and conducting at least one student-led group presentation or discussion, two short 3-5 pp. response papers for other class sessions, and either an exam or a 25-30 pp. research paper. CONSENT APPLICATION: We will try to accommodate as many people as possible with interest in the course. But to facilitate planning and confirm your level of interest, if you are interested in the course please submit a short email with the subject line "application" to Pat Adan (padan@stanford.edu) by September 7, 2018. Please describe in a few sentences (as soon as possible) why you want to take this class, your level of interest in the subject, any topic or topics in which you are especially interested, and whether you prefer the paper or exam option. Emails received after September 7 will be reviewed on a rolling basis.
Terms: Aut | Units: 3 | Grading: Law Honors/Pass/Restrd Cr/Fail
Instructors: Cuellar, M. (PI)

LAW 4041: Lawyering for Innovation: Artificial Intelligence

In recent years, artificial intelligence (AI) has made the jump from science fiction to technical viability to product reality. Industries as far flung as finance, transportation, defense, and healthcare invest billions in the field. Patent filings for robotics and machine learning applications have surged. And policymakers are beginning to grapple with technologies once confined to the realm of computer science, such as predictive analytics and neural networks. AI's rise to prominence came thanks to a confluence of factors. Increased computing power, large-scale data collection, and advancements in machine learning---all accompanied by dramatic decreases in costs---have resulted in machines that now have the ability to exhibit complex "intelligent" behaviors. They can navigate in real-world environments, process natural language, diagnose illnesses, predict future events, and even conquer strategy games. These abilities, in turn, have allowed companies and governments to entrust machi more »
In recent years, artificial intelligence (AI) has made the jump from science fiction to technical viability to product reality. Industries as far flung as finance, transportation, defense, and healthcare invest billions in the field. Patent filings for robotics and machine learning applications have surged. And policymakers are beginning to grapple with technologies once confined to the realm of computer science, such as predictive analytics and neural networks. AI's rise to prominence came thanks to a confluence of factors. Increased computing power, large-scale data collection, and advancements in machine learning---all accompanied by dramatic decreases in costs---have resulted in machines that now have the ability to exhibit complex "intelligent" behaviors. They can navigate in real-world environments, process natural language, diagnose illnesses, predict future events, and even conquer strategy games. These abilities, in turn, have allowed companies and governments to entrust machines with responsibilities once exclusively reserved for humans---including influencing hiring decisions, bail release conditions, loan considerations, medical treatment and police deployment. But with these great new powers, of course, come great new responsibilities. The first public deployments of AI have seen ample evidence of the technology's disruptive---and destructive---capabilities. AI-powered systems have killed and maimed, filled social networks with hate, and been accused of shaping the course of elections. And as the technology proliferates, its governance will increasingly fall upon lawyers involved in the design and development of new products, oversight bodies and government agencies. AI is the biggest addition to technology law and policy since the rise of the internet, and its influence spreads far beyond the tech sector. As such, those entering practice in a wide variety of fields need to understand AI from the ground up in order to competently assess and influence its policy, legal and product implications as deployments scale across industries in the coming years. This course is designed to teach precisely that. It seeks to equip students with an understanding of the basics of AI and machine learning systems by studying the implications of the technology along the design/deployment continuum, moving from (1) system inputs (data collection) to (2) system design (engineering) and finally to (3) system outputs (product features). This input/design/output framework will be used throughout the course to survey substantive engineering, policy and legal issues arising at each of those key stages. In doing so, the course will span topics including privacy, bias, discrimination, intellectual property, torts, transparency and accountability. The course will also feature leading experts from a variety of AI disciplines and professional backgrounds. An important aspect of the course is gaining an understanding of the technical underpinnings of AI, which will be packaged in an easy-to-understand, introductory manner with no prior technical background required. In addition to reflection paper assignments, students will have the opportunity to write blogposts providing legal, regulatory and policy analysis of current issues involving AI. Student contributions will have the option of being posted on the newly created Stanford AI & Law Blog, which will be designed to compile and synthesize AI-related developments for policymakers, startups and product counsels trying to understand and stay up to date with this fast moving technology. The course will be offered for two units of credit (H/P/R/F). Grading will be determined by attendance, class participation and written assignments. Given the course's multi-disciplinary focus, students outside of the law school, particularly those studying computer science, engineering or business, are welcome. CONSENT APPLICATION: To apply for this course, students must complete and submit a Consent Application Form available on the SLS website (Click Courses at the bottom of the homepage and then click Consent of Instructor Forms). See Consent Application Form for instructions and submission deadline.
Terms: Spr | Units: 2 | Grading: Law Honors/Pass/Restrd Cr/Fail
Instructors: Rubin, T. (PI)

LAW 4043: The Social & Economic Impact of Artificial Intelligence

Recent advances in computing may place us at the threshold of a unique turning point in human history. Soon we are likely to entrust management of our environment, economy, security, infrastructure, food production, healthcare, and to a large degree even our personal activities, to artificially intelligent computer systems. The prospect of "turning over the keys" to increasingly autonomous systems raises many complex and troubling questions. How will society respond as versatile robots and machine-learning systems displace an ever-expanding spectrum of blue- and white-collar workers? Will the benefits of this technological revolution be broadly distributed or accrue to a lucky few? How can we ensure that these systems respect our ethical principles when they make decisions at speeds and for rationales that exceed our ability to comprehend? What, if any, legal rights and responsibilities should we grant them? And should we regard them merely as sophisticated tools or as a newly emerging more »
Recent advances in computing may place us at the threshold of a unique turning point in human history. Soon we are likely to entrust management of our environment, economy, security, infrastructure, food production, healthcare, and to a large degree even our personal activities, to artificially intelligent computer systems. The prospect of "turning over the keys" to increasingly autonomous systems raises many complex and troubling questions. How will society respond as versatile robots and machine-learning systems displace an ever-expanding spectrum of blue- and white-collar workers? Will the benefits of this technological revolution be broadly distributed or accrue to a lucky few? How can we ensure that these systems respect our ethical principles when they make decisions at speeds and for rationales that exceed our ability to comprehend? What, if any, legal rights and responsibilities should we grant them? And should we regard them merely as sophisticated tools or as a newly emerging form of life? The goal of CS22 is to equip students with the intellectual tools, ethical foundation, and psychological framework to successfully navigate the coming age of intelligent machines. Elements used in grading: TBA. Cross-listed with Computer Science ( CS 22A) and International Policy ( INTLPOL 200).
Terms: Win | Units: 1 | Grading: Law Mandatory P/R/F
Instructors: Kaplan, J. (PI)
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