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31 - 40 of 76 results for: artificial intelligence

ETHICSOC 274B: Universal Basic Income: the philosophy behind the proposal (ETHICSOC 174B, PHIL 174B, PHIL 274B, POLISCI 338)

The past three decades have seen the elaboration of a vast body of literature on unconditional basic income a radical policy proposal Philippe Van Parijs referred to as a disarmingly simple idea. It consists of a monthly cash allowance given to all citizens, regardless of personal desert and without means test to provide them with a standard of living above the poverty line. The seminar will seek to engage students in normative debates in political theory (feminism, liberalism, republicanism, communism, libertarianism, etc.) by appealing to the concrete example of basic income. It will allow students to learn a great deal about a policy that is gaining tremendous currency in academic and public debates, while discussing and learning about prominent political theorists - many of whom have written against or for basic income at one point in their career.nnnThe seminar is open to undergraduate and graduate students in all departments. There are no pre-requisites. We will ask questions suc more »
The past three decades have seen the elaboration of a vast body of literature on unconditional basic income a radical policy proposal Philippe Van Parijs referred to as a disarmingly simple idea. It consists of a monthly cash allowance given to all citizens, regardless of personal desert and without means test to provide them with a standard of living above the poverty line. The seminar will seek to engage students in normative debates in political theory (feminism, liberalism, republicanism, communism, libertarianism, etc.) by appealing to the concrete example of basic income. It will allow students to learn a great deal about a policy that is gaining tremendous currency in academic and public debates, while discussing and learning about prominent political theorists - many of whom have written against or for basic income at one point in their career.nnnThe seminar is open to undergraduate and graduate students in all departments. There are no pre-requisites. We will ask questions such as: is giving people cash no strings attached desirable and just? Would basic income promote a more gender equal society through the remuneration of care-work, or would it risks further entrenching the position of women as care-givers? Would alternative policies be more successful (such as the job guarantees, stakeholder grants or a negative income tax)? How can we test out basic income? What makes for a reliable and ethical basic income pilot? Students in Politics, Philosophy, Public Policy, Social Work, and Sociology should find most of those questions relevant to their interests. Some discussions on how to fund basic income, on the macro-economic implications of basic income and on the existing pilots projects (in Finland, Namibia, India, Canada and the US) may be of interest to Economists; while our readings on the impact of new technologies and artificial intelligence on the future of work and whether a basic income could be a solution, are likely to be on interest to computer scientists and engineers. By the end of the class, students will have an in depth knowledge of the policy and will have developed skills in the normative analysis of public policy. They will be able to deploy those critical and analytical skills to assess a broad range of other policies.
Terms: Spr | Units: 4 | Repeatable for credit | Grading: Letter or Credit/No Credit

GSBGEN 503: The Business of Healthcare

Healthcare spending is now nearly 18% of the entire GDP of the U.S. economy. The S&P healthcare sector has been one of the best producing segments of the market for the last five years, and growth of healthcare expenditures continue to escalate at a rapid pace. This has triggered an abundance of opportunities for those interested in a career in healthcare management, investing, or entrepreneurialism. The Business of Healthcare-2017-18 will present the current market framework from the eyes of a clinician and with the perspective of the consumer-patient, but with the experience of a successful business builder and investor. Course will begin with the discussion of the channels of distribution of healthcare delivery, from providers, to practitioners, to consumer-facing ¿healthcare lite¿ sectors of the market. Impact of the regulatory environment, with specific focus on the Affordable Care Act and the impending plans to Repeal/Replace, will be evaluated. High-level exploration of internat more »
Healthcare spending is now nearly 18% of the entire GDP of the U.S. economy. The S&P healthcare sector has been one of the best producing segments of the market for the last five years, and growth of healthcare expenditures continue to escalate at a rapid pace. This has triggered an abundance of opportunities for those interested in a career in healthcare management, investing, or entrepreneurialism. The Business of Healthcare-2017-18 will present the current market framework from the eyes of a clinician and with the perspective of the consumer-patient, but with the experience of a successful business builder and investor. Course will begin with the discussion of the channels of distribution of healthcare delivery, from providers, to practitioners, to consumer-facing ¿healthcare lite¿ sectors of the market. Impact of the regulatory environment, with specific focus on the Affordable Care Act and the impending plans to Repeal/Replace, will be evaluated. High-level exploration of international health care markets and how they compare to the American market will be included. Overview of venture and private equity investing will be deeply probed, with many specific market examples of how investors develop an investment thesis, identify specific targets, diligence companies, and close an investment. Discussion around building financial modeling for target acquisitions will be presented, and the course will delve into the burgeoning area of healthcare analytics and outcomes management, including Artificial Intelligence, and its future impact on positioning, reimbursement and clinical outcomes. Sectors that will be discussed include: Healthcare services, Healthcare IT, Life Sciences, Pharma and Biotechnology, and Managed Care. The topic of the emerging importance of consumerism will be probed and consumer-directed healthcare related products and services will be explored, e.g. nutraceuticals, wellness, fitness, etc. Course will include preparatory readings, presentations from successful and powerful industry leaders, and robust in-class discussion and case studies requiring student engagement. Final grade will consist of class participation, one minor in-class presentation, and a final paper developing either a new healthcare business start-up proposition or presenting an identified investment target in the healthcare industry. Course will be especially valuable for those interested in a career in starting a healthcare company, healthcare investing, healthcare administration, or other healthcare-related management and goal of class will be provide an in-depth overview of how to get started or advance a professional interest in the industry.
Units: 2 | Grading: GSB Student Option LTR/PF
Instructors: Krubert, C. (PI)

GSBGEN 596: Designing AI to Cultivate Human Well-Being

Overview: This is a multi-disciplinary cross-listed course focused on the goal of helping to build AI technology that promotes human flourishing. This course aims to expose (a) GSB students to deep learning and AI techniques focused on human well-being, and (b) CS students to behavioral science and design thinking, as well as frameworks and research to better understand human well-being and human-centered designs. Students will form cross-disciplinary teams and work on a final project that delves into an industry and proposes a detailed 5-year road map on how that industry might evolve with AI algorithms that focused on human well-being. Course Description: The past decade of machine learning has given us self-driving cars, practical speech recognition, video game playing robots, effective web search, and revolutionary drug treatments. While Artificial Intelligence has been impressive in achieving these specific tasks, this does not always correspond to the broader goal of cultivating more »
Overview: This is a multi-disciplinary cross-listed course focused on the goal of helping to build AI technology that promotes human flourishing. This course aims to expose (a) GSB students to deep learning and AI techniques focused on human well-being, and (b) CS students to behavioral science and design thinking, as well as frameworks and research to better understand human well-being and human-centered designs. Students will form cross-disciplinary teams and work on a final project that delves into an industry and proposes a detailed 5-year road map on how that industry might evolve with AI algorithms that focused on human well-being. Course Description: The past decade of machine learning has given us self-driving cars, practical speech recognition, video game playing robots, effective web search, and revolutionary drug treatments. While Artificial Intelligence has been impressive in achieving these specific tasks, this does not always correspond to the broader goal of cultivating human well-being. The goal of this class is to bridge the gap between technology and societal objectives: How do we design AI to promote human flourishing? On Day 1, we draw on behavioral research to discuss what makes humans thrive. Behavioral research shows that for people to flourish, they need meaning, which involves an ability to understand and value others, a sense of belonging, and knowledge that they are making a contribution bigger than themselves. The conditions for this occur when people feel they have the resources and insight to establish a sense of meaning for themselves. Students will draw on this research to focus on building AI technology that effectively understands, communicates with, collaborates with and augment people. On days 2-5, leaders across industries (e.g., healthcare, transportation) that fundamentally affect human wellbeing will participate in lightning round exchanges to delve deeply into the challenge of building technology focused on human well-being, followed by interactive discussion with students. On the last day, the four-person cross-disciplinary teams will present their 2 page white paper proposals to invited guests. Of note: this course is entirely about high-level "programming" and provides no technical insight on machine learning, data-mining or statistical pattern recognition.
Units: 2 | Grading: GSB Pass/Fail

HRMGT 203: People Analytics

How can we use big data, machine learning and artificial intelligence to inform design, hiring, promotion and human resource management processes in organizations? We will discuss the theoretical and practical challenges that these issues present, and the ways by which data can help resolve them. In doing so, we will explore various data analytic methods and different data types, as well as the pitfalls and ethical issues their use introduces.
Units: 2 | Grading: GSB Letter Graded
Instructors: Goldberg, A. (PI)

HUMBIO 96SI: Big problems, big solutions? tackling difficult issues in today's healthcare system.

It is impossible to innovate in healthcare without first understanding the context in which these innovations take place. The course aims to allow students an intimate setting to debate issues that plague healthcare today, and work with guest speakers (from Stanford Medicine, Stanford Biodesign, RockHealth to Apple Health and more!) to gain insight into what's actually being done about it. Some controversial topics highlighted include: Healthcare Legislation (especially in the context of the last tow administrations), Artificial Intelligence in Healthcare, Gene Therapy, and in-depth analysis of Failed Medical Devices and Innovations.
Terms: given next year | Units: 1-2 | Grading: Satisfactory/No Credit

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. The practicum is offered for 2 to 3 units in Winter Quarter. Students enrolled in the Winter Quarter practicum may also enroll in the practicum for one unit in Spring Quarter with instructor consent. 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, Spr | Units: 1-3 | 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 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. The policy practicum is offered for 2-3 units in Winter Quarter and 1-2 units in Spring Quarter. 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, Spr | Units: 1-3 | Repeatable for credit | Grading: Law Honors/Pass/Restrd Cr/Fail
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