61 - 70 of 236 results for: MS

Client: Various government agencies and nonprofit groups. Formerly incarcerated individuals face a range of personal and institutional challenges in their reentry into broader society. Considerable research and many programs have focused on systems reform and support and social programs to increase the likelihood of successful reentry. But technological tools also have the potential to help lower friction and increase the success of reentry. This policy lab will engage with challenging legal, social, government systems, and technological questions, with opportunities to design and/or implement new tools to aid in the reentry process. We will work with a variety of stakeholders including government organizations and programs, non-profit entities, and legal innovators to prototype and evaluate new technological solutions to facilitate the reentry process and reduce recidivism. This practicum will build a collaborative team of diverse backgrounds and skill sets to learn from each other and enhance the overall capacity of the research and tool development. We encourage students who are interested in criminal justice, technology for social impact, access to justice, and entrepreneurship and innovation for social good 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. Elements used in grading: Attendance, Performance, Class Participation, Written Assignments, Final PROJECT. 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.

Client: Lift Our Voices, https://www.liftourvoices.org/. In recent years, a large fraction of U.S. employers--including many leading law firms -- have required their employees to sign contracts containing mandatory arbitration clauses and "non-disclosure agreements" (NDAs). Available research suggests that more than 60 million American workers are bound by these arbitration clauses, which require employees who have any type of legal claim arising out of their work or workplace to waive their right to trial and resolve their claims, on an individual basis, in private arbitration. Traditionally, arbitration takes place behind closed doors, and the details of the employee's claim (and employer's response), any evidence presented to the arbitrators, the proceedings themselves and the ultimate outcome are confidential. Moreover, employees who are offered monetary settlements to resolve their arbitration claims -- or lawsuits, for those who were not compelled to arbitrate under a contractual provision -- are typically required to sign NDAs as a condition of receiving compensation. As a result of arbitration and NDAs, information about wrong-doing in the workplace -- even egregious wrong-doing -- never becomes public, arguably diminishing the ability of the legal system to deter harmful behavior. Moreover, with claims resolved individually, in private, and settlements protected by NDAs, it is impossible to detect a pattern of wrongful behavior and to hold wrongdoers to account in the public square. These consequences seem particularly problematic in claims arising from gender discrimination, particularly sexual harassment. Secrecy also prevents us from discovering whether women of color or low-income women of all colors are particularly disadvantaged by mandatory arbitration and NDAs. The expanding use of mandatory arbitration and NDAs in employment claims has evoked considerable controversy and legislation has been introduced at both the national and state level to prohibit the inclusion of these clauses in employment contracts. However, the legislation has yet to move forward on the national level and whether state statutes will withstand challenge is currently unclear. Moreover, there is little systematic evidence of the consequences of mandatory arbitration and NDAs, leaving both supporters and opponents to rely on anecdotes. There is little hard information on the numbers of employees covered by arbitration contracts or how this varies by industry sector and employee gender, race, ethnicity or socio-economic characteristics. Importantly we do not know how the existence of these contracts affects men and women's willingness to bring their claims to their employers' attention or how claiming varies by race, ethnicity or employee status. Nor do we know how pursuing claims for gender discrimination, including sexual harassment, affects claimants' future career trajectories. The Client for this policy lab, Lift Our Voices, was co-founded by women's rights advocates and broadcast journalists Gretchen Carlson and Julie Roginsky. Ms. Carlson's sexual harassment suit against powerful former Fox News chairman and CEO Roger Ailes helped pave the way for the #metoo movement. Roginsky left Fox after settling a lawsuit for sexual harassment and discrimination against Fox News, its former co-president Bill Shine and Ailes. To learn more about Lift Our Voices, go to https://www.liftourvoices.org/ The goal of this practicum is to produce objective empirical evidence -- both quantitative and qualitative -- that can be used in Life Our Voices and others' advocacy activities regarding mandatory arbitration and NDAs, including advocacy -- if the data support this -- that argues in favor of restricting or precluding mandatory arbitration and NDAs in some or all circumstances. In Spring 2021 students in this practicum met with Gretchen Carlson and Julie Roginsky to identify the questions for which empirical evidence would be most useful for policy reform advocacy. Based on these discussions and their review of relevant commentary, the students decided to break up into two teams, each of which would design a research project. Project 1 will interview plaintiff and defense lawyers to develop a better understanding of the incentives for victims of sexual harassment and sexual assault to sign non-disclosure agreements. Project 2 will interview corporate legal counsel in corporations (and potentially law firms) that have abandoned mandatory pre-dispute arbitration contract clauses to develop a better understanding of why these companies and firms abandoned arbitration and what have been the outcomes for the organizations to date. At the end of the spring quarter, each team prepared a memorandum outlining the issues that their team focused on and reviewing the relevant case law and recent statutory reforms. In addition, each team prepared a data collection protocol including draft questionnaires and lists of potential interviewees. The data collection protocols were informed by informal discussions with SLS faculty who are knowledgeable about these issues as well as a few outside advisers. The goals of the fall quarter are to implement these research designs, collect and analyze data and prepare white papers to share with the clients. Early in the quarter, students will meet with Mss. Carlson and Roginski to discuss policy developments since the spring and may revise the spring quarter students' research designs in response. The Canvas page for the fall practicum includes the memoranda and other materials the students produced in the spring quarter. Students interested in registering for the fall practicum should review these materials, keeping in mind that they are free to elaborate on them if they wish and if new policy developments suggest this would is appropriate. If a sufficient number of students register for the practicum one or more related projects may be added to our agenda. Elements used in grading: Attendance, Performance, Class Participation, Written Assignments. 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.

Human decision making is increasingly being displaced by predictive algorithms. Judges sentence defendants based on statistical risk scores; regulators take enforcement actions based on predicted violations; advertisers target materials based on demographic attributes; and employers evaluate applicants and employees based on machine-learned models. One concern with the rise of such algorithmic decision making is that it may replicate or exacerbate human bias. This course surveys the legal and ethical principles for assessing the equity of algorithms, describes statistical techniques for designing fair systems, and considers how anti-discrimination law and the design of algorithms may need to evolve to account for machine bias. Concepts will be developed in part through guided in-class coding exercises. Admission is by consent of instructor and is limited to 20 students. CONSENT APPLICATION: To enroll in the class, please complete the course application by March 15, 2021 available at: https://5harad.com/mse330/. Elements used in grading: Grading is based on response papers, class participation, and a final project. Cross-listed with Comparative Studies in Race & Ethnicity ( CSRE 230), Management Science & Engineering (MS&E 330), Sociology ( SOC 279).

The course follows the patent application process through the important stages: inventor interviews, patentability analysis, drafting claims, drafting a specification, filing a patent application, and responding to an office action. The subject matter and practical instruction relevant to each stage are addressed in the context of current rules and case law. The course includes four written assignments: an invention capture, a claim set, a full patent application, and an Office Action response. Pre-requisites: Law 326 (IP:Patents), Law 409 (Intro IP), ME 208, or MS&E 278.

Directed research experience for MS students in mechanical engineering who are pursuing the Distinction in Research (DiR). The student is responsible for securing a faculty research advisor and will register under that advisor's section number. Students must provide confirmation of faculty research advisor's agreement to supervise DiR, at which time they will receive a permission code from ME Student Services allowing them to enroll. Course may be repeated for credit.

This course is for MS students in mechanical engineering who are finishing up their Distinction in Research (DiR) and focusing on the writing of their technical report. Faculty supervision is required and students will register under their advisor's section number. Permission codes apply and can be obtained from ME Student Services. Technical report should be read and signed off by advisor no later than two weeks prior to the end of the quarter in which an MS-DiR student plans to confer their degree.

Deliberative democracy is a political theory that holds that democracy should be based on informed, respectful, and inclusive public deliberation. In this SoCo course, we explore the relationship between artificial intelligence (AI) and deliberative democracy, and examine how AI can be used to support and enhance the democratic process through deliberative democracy. This course will focus on the use of AI in the Stanford Online Deliberation Platform (a collaboration between the Crowdsourced Democracy Team and Deliberative Democracy Lab, both at Stanford), the ethics of AI and democracy, and the potential for AI to support deliberation and participation. The course will also explore the challenges and limitations of using AI in a democratic context and the need for effective regulation and governance of AI.

Fundamental concepts and tools for the analysis of problems under uncertainty, focusing on structuring, model building, and analysis. Examples from legal, social, medical, and physical problems. Topics include axioms of probability, probability trees, belief networks, random variables, conditioning, and expectation. The course is fast-paced, but it has no prerequisites.