AU CORE 105 Big Data, Bias, and Justice for All

A Complex Problems Seminar

1 Course Description and Purpose

Instead of eliminating the effects of human bias, naive algorithms empowered by big data can perpetuate and accentuate them. Big data often hides historical biases in its vastness while still missing key contextual information about an individual. Big data algorithms have no innate understanding of fairness, justice, or equality. This course investigates big data’s role in justice, health, education, personal finance, and housing and explores the interplay of ethics and practical considerations in subjects such as bias in data collection, privacy protection, open data, algorithmic bias detection, and bias in outcomes and communicating results (e.g., infographics and “fake news”).

You will exercise critical thinking skills to assess and critique new information, especially data-driven stories and infographs, for authenticity, accuracy, and meaning. You will analyze recent publications and online sources, to include open data to research current issues and the state of the practice. By engaging with guest speakers on emerging challenges in policy and technology you will see diverse perspectives to challenge your thinking. You will choose a focus area of personal interest and produce an analysis of the most pressing concerns related to big data and biased outcomes and then recommend creative, actionable solutions for government, industry, and/or society.

As a Complex Problems course, this primary purpose of this course is to exercise and strengthen your abilities in scholarly methods of inquiry. We will use our investigation into real-world issues of big data and bias as an opportunity to apply skills in research, analysis of diverse perspectives, communication, and collaboration. The goal is for you to integrate across methods while you increase competency in moving from problem identification to understanding to persuasive action. Course activities and assignments create opportunities for you to give feedback and to respond to feedback to help you reflect on how you perceive and analyze the world, and engage with others. In short, this course is designed to help you learn about big data, bias, and justice for all, but more importantly, to learn about You.

2 Course Learning Outcomes

All Complex Problems courses share the same learning outcomes, regardless of the course content under investigation, as shown in Figure 1.

Figure 1: Learning Outcomes (Complex Problems)

At the end of the course, a successful student will be able to ...

  1. Appreciate Diverse Perspectives: Analyze complex issues from multiple perspectives, develop self-awareness, and demonstrate civility towards others

  2. Communicate Effectively: Identify audiences, integrate sources, and organize ideas

  3. Employ Critical Reading Skills: Summarize material, respond to material, and develop a “conversation” across materials

  4. Reflect and Respond: Incorporate feedback and practice meta-cognition

  5. Integrate Learning Across Diverse Experiences: Connect external experiences (“co-curriculars”) with academic learning to shape a broader perspective.

    3 Course Details

This course has no prerequisite courses and does not require any prior software development or quantitative analytics experience

3.1 Overall Structure

  • We will use Canvas as our Learning Management System for the course
  • We will meet twice each week for face-to face discussions. If circumstances change and we have to meet via Zoom it will be at the same time as face to face classes.
  • The schedule will change as guest speakers and other co-curriculars evolve.
  • I will be recording portions of most classes .
  • Class materials will be posted on Canvas before each class.

3.2 Textbook

Weapons of Math Destruction
Cathy O’Neill
2016, Broadway Books
ISBN: 978-0-553-41883-5
E book ISBN: 978-0-553-41882-8
Available from multiple sources

WMD book cover

3.3 Other Course Materials

  • Students will need a computing device with internet access

  • Other required and optional materials will be provided via Canvas, Course Reserves, or references to Internet sources, e.g., YouTube

  • In addition to lecture notes these may include academic papers provided by the guest speakers, podcasts, YouTube videos, Ted Talks or other presentations/interviews or other media.

  • The current requested course reserve materials include select chapters from the following books:

Title Author Year
Invisible Women: Exposing Data Bias in a World Designed for Men Caroline Criado Perez 2019
How Charts Lie: Getting Smarter about Visual Information Alberto Cairo 2019
Understand, Manage, and Prevent Algorithmic Bias: A Guide for Business Users and Data Scientists Tobais Baer 2019

3.4 Class Topics and Schedule (Subject to Change)

The course is organized into three main parts:

  • Part 1 covers classes 1 to 7 where we investigate what we mean by Big Data, Bias and Justice.
  • Part 2 covers classes 8 to 15 where we investigate different frameworks for understanding and analyzing issues associated with Big Data, Bias and Justice.
  • Part 3 covers class 16 to 28 where we investigate specific scenarios involving the use of Big Data.
  • Our last class, 29, is during final exam period where you will share the results of your final projects with each other.

The schedule below shows the general flow of topics and guest speakers.

  • During a typical week the Monday class will focus on discussion about the topic based on the course resources and Thursday will focus on an group activity about the topic.
  • We will also have co-curricular events held outside of normal class time, one of which will be academic in nature.
  • The academically oriented co-curricular event will be a movie night where we watch the Netflix documentary The Social Dilemma together. We will do this before we have our first guest speaker as the content is directly relevant to their work.
Classes Topic Module
1 Intro and Data Science Life Cycle 1
2-3 Big Data 2
4-5 Bias 3
6-7 Justice and Fairness 4
8-9 Legal and Ethical Frameworks 5
10-11 Analyzing Data: Inputs and Outputs 6
12 Assessing Algorithms: Bias 7
13 Assessing Algorithms: Differential Privacy 7
Co-curricular Move Night on Social Media
14-15 Analyzing Graphics 8
16 Scenarios in Micro-targeting and Recommendations 9
17-18 Scenarios in the Justice System 10
19 GS: Fairness and Responsibility in Social Media 9
20 Scenarios in Finance: Insurance 11
21 GS: Analyzing Data for News/Graphics 8
22 Scenarios in Finance: Credit and Risk 11
23 GS: Ethical AI 9
24 Scenarios in Education 13
25 Scenarios in Education - Admissions 13
26 Scenarios in Medicine 12
27 Course Review and Assessment/SET/Peer Reviews 12
28 GS: Medical 13
29 Final Project Presentations 14
Guest Speakers and Dates are tentative so classes will probably shift.

3.5 Guest Speakers

The classes with GS in the Topic in the table above are planned Guest Speakers.

4 Graded Elements and Rubrics

There are multiple categories of graded elements in this course as seen in the table below. There are no quizzes or exams planned but that may change if determined to be in the best interest of achieving the learning outcomes. The assignments and due dates may evolve as the schedule changes to accommodate Guest Speakers or the flow of the course.

The Learning Outcome on oral and written communication means this class will require you to speak and write in a style and structure to communicate effectively in the given situation. Many of the graded elements require effective communication of your impressions or analysis. You will have to select the appropriate style and structure for the element and the audience. A style that might be appropriate for a discussion may not be appropriate for a journal or a final project paper. In all cases, generally accepted uses of punctuation, grammar and spelling help you communicate more effectively. The AU Writing Center is a great resource for students.

  • Each graded element has a specific maximum possible score with a specific instructions and a rubric tailored to the category and this course.

  • The rubrics identify the standard of performance required to achieve a numerical score within the categories of Excellent, Proficient, Limited or Poor for the essential aspects of the assignment.

  • The maximum scores range from 4 points for each class attendance and engagement, to 5 points for a weekly Journal entry, to 55 Points for the Final Project Essay paper.

  • No single item is worth more than 12% of the overall score as class engagement and participation (~20%) builds up class by class.

  • The graded elements exercise a primary learning outcome with multiple supporting learning outcomes.

  • As the course schedule, including guest speakers may change, there may be changes to the elements below. Elements may be added or deleted or due dates may be changed

4.1 Class Room Engagement

  • As a seminar course, your contributions to the discussion are important to your learning and that others. Complex problems benefit from considering diverse perspectives so contribute your ideas.
  • The rubric on classroom engagement has two criteria: Attendance and Engagement. One must be present to engage.
  • You will complete a self reflection survey after each class to assess your attendance and engagement.
  • Figure 2 from Lisa Grocott and the THRIVING Research Lab provides some ideas on how to engage in the course to advance your learning and the learning of others.

Figure 2: elements of effective engagement in class: Active Listening, Frequent Participation, Advanced Peers's Learning, Depth of Insights and Preparedness and Perseverance

4.2 Final Project

  • Detailed instructions will be provided in the actual assignment but the following should provide some idea of the expectations for the Final Project.
  • It is an individual effort but could be considered a “quasi-group” project as it requires the solicitation and use of peer feedback throughout the development process.
  • The primary element is the Essay exploring a relevant topic of your choice.
  • Each person will present a five minute summary of their analysis to the class in an oral presentation during the final exam period.
  • The Project Presentation includes the requirement to produce and present a “Call to Action”. This is a creative artifact you develop to inspire others to act on behalf of the recommendations resulting from your analysis.
  • The Call to Action can be in any medium appropriate for your intended audience and for sharing within a two minute span in a teleconference. As examples: it could be a dynamic infographic, a press release, or a point paper. However, it could also be a TikTok video, a poem or a song, a poster or painting - whatever form best suits your talents and passion. Whatever the medium, it does have to be appropriate for the intended audience and contain elements expressing the issue and your recommended action(s).
  • The rubric for the presentation includes points a peer assessment of the presentation so you will be reviewing each other’s presentations in accordance with the criteria in the rubric.

4.3 Typical Assignments during a Semester.

The course has many assignments but many are short. Pay close attention to the instructions and rubrics to ensure you capture all elements.

Category Name Rubric Primary LO
Engagement Class Attendance and Engagement (29) Class Engagement (85 Pts) Communication
Engagement Class Attendance and Engagement Self Reflection (28) Class Engagement (1 Pt) Reflection
Discussion Online Discussions (3) Online Discussions (30 Pts) Critical Reading
Assignment Guest Speaker Analysis and Questions (GS A&Q) Draft (3-4) Analysis and Questions for Guest Speaker (23 Pts) Diverse Perspectives
Assignment GS A&Q Peer Reviews (3) Conduct Peer Reviews (10 Pts) Diverse Perspectives
Assignment GS A&Q Response to Peer Review (3) Analysis and Questions for Guest Speaker (7 Pts) Reflection
Journal Weekly Journal (11) Weekly Journal (5 Pts) Reflection
Journal Reflection of Journals (1) Short Paper (25 Pts) Reflection
Assignment Big Data, Bias, and Justice Short Paper Short Paper (25 Pts) Integrative Learning
Assignment News Statistics/Graphic Short Paper Point Paper (25 Pts) Integrative Learning
Essay Final Project Essay Essay Paper (55 Pts) Communication
Presentation Final Presentation of Call to Action Presentation of Call to Action (30-45 Pts) Communication

4.4 Final Grades

Final grades are based on the weighted average of the graded elements with an emphasis on how students demonstrate the course learning outcomes by the end of the course.

Range % Letter
93 or above A
90-92 A-
87-89 B+
83-86 B
80-82 B-
77-79 C+
70-76 C-
67 - 69 C-
60-66 D
59 or less F