基于区块链的毕业设计Ethics of Artificial Intelligence and Machine Learning – 人工智能与机器学习伦理学

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Ethics of Artificial Intelligence and Machine Learning

Ethics of Artificial Intelligence and Machine Learning - 人工智能与机器学习伦理学

(Image produced using Deep Dream Generator, a computer vision program that utilizes a convolutional neural network to recreate a picture in the style of another)

Course Description

This discussion-based course will provide an introduction to the ethical issues related to artificial intelligence and machine learning. The first half of the semester will focus on concerns existing in the present day, such as bias and transparency. During the semester’s second half, we will cover topics that will be increasingly important going forward, from consciousness to the future of labor.

Course Details

  • Course: CMSC389V
  • Prerequisites: CMSC216 and CMSC250
  • Credits: 1
  • Seats: 30
  • Lecture Time: Friday, 1:00-1:50 pm
  • Location: Online
  • Course Materials: A microphone and web camera for Zoom calls
  • Course Facilitator: Anthony Ostuni
  • Faculty Advisor: Dr. John Dickerson

Class Format

This online course will primarily be discussion-based, with the topic of each week’s discussion determined by a set of short readings and/or videos. These materials must be completed before class and used to write a response to a short-answer question. Note that the materials do not necessarily reflect the viewpoints of the course facilitator nor faculty advisor; they are simply design to provoke thought on the subject area. Prior to the class discussion, there may be a brief lecture on a specific idea or concept that could be of value to the conversation. The primary assignments for the class will be two papers.

Grading

Grades will be maintained on ELMS.

You are responsible for all material discussed during lecture, as well as readings and other material posted on ELMS or Piazza outside of class.

Percentage Title
10% Reading Short-Answer
30% Participation
25% First Paper
25% Second Paper
10% ML / AI Guidelines

The following cutoffs may be lowered (but will not be raised) depending on the performance of the class.

Grade Cutoff
A+ 97
A 93
A- 90
B+ 87
B 83
B- 80
C+ 77
C 73
C- 70
D 60
F 0

Discussions

The majority of class time will be spent in group discussion. This will allow for an efficient exchange of diverse ideas and perspectives, as well as forcing students to become more comfortable organizing and articulating technical and philosophical concepts. We will decide discussion rules together as a class during the first week. Discussions will be held over Zoom, so students are required to have a microphone and web camera to communicate as smoothly as possible given the circumstances.

Papers

There will be two essays that will compose the majority of your work outside the classroom. The first will be assigned around the 6th week, and it will be on one of the topics discussed up to that point in class. The second paper will be assigned around the 12th week, and it should represent the culmination of ideas developed throughout the previous six weeks. The official assignment details for both papers will be released on ELMS.

All regrade requests must be made within one week of the assignment grade being released.

Final Assignment

In lieu of a final, you will be expected to develop a list of ML / AI Guidelines for a tech company to follow and justify your rules. The official assignment details will be released on ELMS.

Late Policy

All assignments may be turned in up to 24 hours late with a 25% penalty. After 24 hours, no late assignments will be accepted.

Schedule

The schedule is subject to change; students will be notified in such an occurrence.

Week Topic Readings and Slides Assignments
9/4 Introduction to Ethics Slides
9/11 Transparency and Interpretability Interpretability Importance, Slides Questions
9/18 Algorithmic Racism and Bias Criminal Sentencing, Slides Questions
9/25 Societal Algorithms Citizen Score, Hiring Questions
10/2 Data Privacy
10/9 Automated System Error
10/16 War and Military Systems
10/23 Defining Intelligence and the Turing Test
10/30 The Future (or End) of Labor
11/6 Singularity, Control, and Unintended Consequences
11/13 Inequality, Unequal Access, and Open-Sourcing
11/20 Interaction and Affection
11/27 Thanksgiving Break n/a n/a
12/4 Artificial Creativity
12/11 Robot Rights

Communication

The primary means of communication outside of class will be ELMS. Office hours will be scheduled by appointment. The email addresses below should only be used for important and time-sensitive issues.

  • Faculty Adviser: Dr. John Dickerson: john [at] umd.edu
  • Course Facilitator: Anthony Ostuni: aostuni [at] umd.edu

Excused Absence and Academic Accommodations

See the section titled “Attendance, Absences, or Missed Assignments” available at Course Related Policies.

Disability Support Accommodations

See the section titled “Accessibility” available at Course Related Policies.

Academic Integrity

Note that academic dishonesty includes not only cheating, fabrication, and plagiarism, but also includes helping other students commit acts of academic dishonesty by allowing them to obtain copies of your work. In short, all submitted work must be your own. Cases of academic dishonesty will be pursued to the fullest extent possible as stipulated by the Office of Student Conduct.

It is very important for you to be aware of the consequences of cheating, fabrication, facilitation, and plagiarism. For more information on the Code of Academic Integrity or the Student Honor Council, please visit http://www.shc.umd.edu.

Feedback

If you have any suggestions for improving this class, don’t hesitate to tell the instructor or facilitators during the semester. At the end of the semester, please don’t forget to provide your feedback using the campus-wide CourseEvalUM system. Your comments will help make this class better in future iterations.


人工智能与机器学习伦理课程描述学术住宿

残疾支持住宿

学术诚信

反馈

  • 课程:CMSC389V
  • 先决条件:CMSC216和CMSC250
  • 学分:1
  • 座位:30
  • 授课时间:周五下午1:00-1:50
  • 地点:在线
  • 课程材料:用于缩放呼叫的麦克风和网络摄像头大学教育部
  • 课程主持人:Anthony Ostuni:aostuni[at]大学教育部
  • 10% 阅读简短回答 30% 参与度 25% 25% 第一篇论文 25% 第二篇论文 10% ML/AI指南 A+ 97

    A 93 A-

    90 B+B+ 87

    87 87 83

    83 77< 80 C+ 77 C 73 C- 70 D

    60 F 0 9/4 道德导论 幻灯片,幻灯片 问题自动化系统错误奇点、控制和意外后果,开放式采购和开放式采购和开放式采购 11/20 11/20 互动和情感的相互作用和影响 11/27 11/27 感恩节休息日 n/a 12/4

    >人工创意 >>>机器人机器人机器人权利,机器人权利,机器人的权利,机器人的权利, >>>>>>>>>>>>>>>>>>>>>机器人/td>

    Ethics of Artificial Intelligence and Machine Learning - 人工智能与机器学习伦理学

    (使用深度梦想生成器生成的图像,这是一种利用卷积神经网络以另一种风格再现图片的计算机视觉程序)

    残疾支持住宿

    这门基于讨论的课程将介绍与人工智能和机器学习相关的伦理问题。本学期上半学期将集中讨论当今社会存在的问题,如偏见和透明度。在下半学期,我们将讨论未来越来越重要的话题,从意识到劳动的未来。

    学术诚信

    • 课程:CMSC389V
    • 先决条件:CMSC216和CMSC250
    • 学分:1
    • 座位:30
    • 授课时间:周五下午1:00-1:50
    • 地点:在线
    • 课程材料:用于缩放呼叫的麦克风和网络摄像头大学教育部
    • 课程主持人:Anthony Ostuni:aostuni[at]大学教育部
    • Faculty Advisor: Dr. John Dickerson

    反馈

    本在线课程主要以讨论为基础,每周讨论的主题由一系列短文和/或视频确定。这些材料必须在上课前完成,并用来写一个简短回答问题的答案。请注意,这些材料不一定反映课程主持人或教师顾问的观点;它们只是为了激发对主题领域的思考而设计的。在课堂讨论之前,可能会有一个简短的讲座,讲一个对对话有价值的特定想法或概念。这堂课的主要作业是两篇论文。

    Grading

    榆树的坡度将保持不变。

    您负责课堂上讨论的所有材料,以及课堂外张贴在榆树或广场上的阅读材料和其他材料。

    Percentage Title
    10% 阅读简短回答
    30% 参与度
    25% 25%
    第一篇论文 25%
    第二篇论文 ML / AI Guidelines

    根据课程的表现,以下截止值可能会降低(但不会提高)。

    Grade Cutoff
    A+ 10%
    ML/AI指南 93
    A+ 97
    93 A-
    B+B+ 87
    87 80
    83 77
    77< 80
    C+ 77
    C 73
    C- 70

    Discussions

    大部分课堂时间将用于小组讨论。这将有助于有效地交换不同的想法和观点,并迫使学生们更加自如地组织和表达技术和哲学概念。我们将在第一周以一节课的形式共同决定讨论规则。讨论将以变焦方式进行,因此要求学生配备麦克风和网络摄像头,以便在特定情况下尽可能顺利地进行交流。

    Papers

    课堂外将有两篇论文构成你大部分的作业。第一节课将在第六周左右完成,它将是课堂上讨论过的话题之一。第二篇论文将在第12周左右完成,它应该代表前六周所形成的思想的高潮。两份报纸的官方任务细节将在ELMS上公布。

    所有重修申请必须在作业成绩发布后一周内提出。

    Final Assignment

    作为期末考试的替代,你将被要求为一家科技公司制定一份ML/AI指南清单,以遵循和证明你的规则。正式的任务细节将在ELMS上公布。

    Late Policy

    所有作业可延迟24小时提交,罚款25%。24小时后,不接受迟交的作业。

    Schedule

    课程表可能会有变动;如有变动,学生会收到通知。

    Week Topic Readings and Slides Assignments
    D F 0 9/4
    道德导论 幻灯片,幻灯片 问题自动化系统错误奇点、控制和意外后果,开放式采购和开放式采购和开放式采购 11/20
    9/18 11/20 互动和情感的相互作用和影响 Questions
    11/27 11/27 感恩节休息日 Questions
    n/a 12/4
    10/9 >>>机器人机器人机器人权利,机器人权利,机器人的权利,机器人的权利, >>>>>>>>>>>>>>>>>>>>>机器人/td>
    10/16 War and Military Systems
    10/23 Defining Intelligence and the Turing Test
    10/30 The Future (or End) of Labor
    11/6 Singularity, Control, and Unintended Consequences
    11/13 Inequality, Unequal Access, and Open-Sourcing
    11/20 Interaction and Affection
    11/27 Thanksgiving Break n/a n/a
    12/4 Artificial Creativity
    12/11 Robot Rights

    Communication

    课外交流的主要方式是ELMS。办公时间按预约时间安排。下面的电子邮件地址只能用于重要和时间敏感的问题。

    • Faculty Adviser: Dr. John Dickerson: john [at] umd.edu
    • Course Facilitator: Anthony Ostuni: aostuni [at] umd.edu

    Excused Absence and Academic Accommodations

    请参阅课程相关政策中标题为“出勤、缺勤或错过作业”的部分。

    Disability Support Accommodations

    请参阅课程相关政策中标题为“可访问性”的部分。

    Academic Integrity

    请注意,学术不诚实不仅包括作弊、捏造和剽窃,还包括通过允许其他学生获取您的作品副本来帮助其他学生实施学术不诚实行为。简而言之,所有提交的工作必须是你自己的。在学生行为办公室的规定下,将尽最大可能追究学术不诚实的案件。

    对你来说,意识到作弊、捏造、便利和剽窃的后果是非常重要的。有关学术诚信守则或学生荣誉委员会的更多信息,请访问http://www.shc.umd.edu。

    Feedback

    如果你对这门课有什么改进的建议,请在学期中告诉导师或辅导员。学期结束时,请不要忘记使用校园课程评估系统提供您的反馈。您的注释将有助于在将来的迭代中使该类更好。

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