What is Responsible CS?

The Responsible CS program aims to integrate ethics and social impact topics across Brown's computer science curriculum. Presenting ethical questions alongside technical information will draw a clear connection between the choices of computer scientists and their societal effects, and give students an opportunity to practice and analyze ethical design over the course of their entire computer science education.

Why are ethics important?

  • As the role of technology increases in nearly every area of life, the work of computer scientists has a broader impact on society.
  • Technical decisions, if widely adopted, can inadvertently function as policy decisions, and computer scientists are often given broad leeway to make those decisions.
  • Technology can be harnessed to improve lives, but engineers and designers are often not asked to consider its adverse effects.

What does it mean to be ethical?

  • Recognize that technology is not neutral, nor does it exist in a vacuum; it contains built-in biases that reflect the preferences, norms, and worldview of its creators.
  • Build with everyone in mind.
  • Understand and fulfill the responsibility to advocate against unethical product or research decisions.

How does the program work?

The program embeds TAs in a set of selected classes with the goal of getting Brown CS students to think about the ethical considerations of their concentration from the beginning of their time in the department through graduation and beyond. This is accomplished by integrating modules on ethics into the fabric of each target class, encouraging students to think about ethics in a structured way, and applying these ideas in projects.

Events

Speakers

Feb 23: Natasha Singer
... More to come!

FAQ

How does the ETA program interact with the courses and professors?
For each course that the program works with, one or more ETAs work as members of the course staff to integrate ethics content into the curriculum. The work of integrating content is a collaborative effort between the ETAs, professors, and rest of the course staff.

How can I get involved as an ETA?
ETA hiring occurs at the same time as regular TA hiring. The MTAs will send out an email the semester before with information about applying for both UTA and ETA positions. Many of the ETAs serve both UTAs and ETAs!

How much are ETAs paid?
The hourly wages for ETAs are calculated with the same formula as regular TAs.

Courses

Course title Ethical issue Integration strategy
Deep Learning (1470) How do we think about deep learning algorithms as part of a system? What are the ethical implications of deep learning technologies? Lab that reduces gender bias in language models, technology-specific ethics lectures (e.g. deepfakes, facial recognition), written questions for projects
Computer Science: An Integrated Introudction (CS0170) What are the ethical considerations when designing and writing programs? In-class turn-and-talk questions presented by Spike (prof. Hughes), homework question about the ACM Code of Ethics
Computing Foundations: Data (CSCI 0111) What are some of the ethical implications and societal impacts of computing and data science? Readings and short writing assignments about ethical issues surrounding technology, in-class discussions about the readings and other ethical questions, written questions connecting projects to related ethical issues, guest lectures by experts of data ethics projects
Intro to Object Oriented Programming (CSCI 0150) What are the ethical questions in the field that should be considered when making career decisions? Beginning of class ethics news summaries, occurring once every two classes, piazza polls allowing students to express their opinions on ethical issues, mini-assignment questions that challenge students to read articles through an ethical lens, section activities/discussions that follow up on mini-assignments
Introduction to Algorithms and Data Structures (CSCI 0160) Introduce students to algorithms and data structures within real-world, socially relevant contexts. Promote critical thinking about ethical considerations and algorithmic fairness for all the code they write. Projects all have a social premise as well as conceptual ethics questions that ground the assignment within realistic, socially relevant problems. Sections involve discussions and activities surrounding ethical issues. One homework assignment centered on algorithmic fairness.
Computing Foundations: Program Organization (CSCI 0112) To develop student understanding of how and why ethics relate to what they are learning. Cultivate a mindset where ethics are at the forefront of consideration when coding. Changes in assignments to have students think about ethics in design from the perspective of both a student and a designer, additions to lectures to further discuss relevant ethical content, in class activities and discussions for students to understand why designs may be ethical or unethical
Creating Modern Web & Mobile Applications (CSCI 1320) Introduce students to several ethical considerations in web and mobile development, paying specific attention to accessibility, privacy, and long-term impact. Give students opportunities to work through technical solutions to these issues, and encourage students to think critically about design choices and their impact. Readings and written questions about several ethical issues relating to each module in the course, technical requirements (e.g. accessibility required for all projects), written questions connecting projects to related ethical issues.
Data Science (CSCI 1951A) Bring attention to the role that data science can play in society and facilitate critical engagement with the potentially adverse effects that data science can have in the real world. Articles and short answers about ethical issues in the assignments, ethical component in final project, an ML debiasing lab
Computer Vision (CSCI 1430) Introduce students to a broad number of ethical issues in Computer Vision, tackling problems such as photo manipulation and dataset limitations. Familiarize students with an awareness of unexpected ethical gray areas, and encourage them to think about them critically. To written part of each project, added ethical questions targeting material relevant to the rest of the project, linking relevant readings. Integrated ethics content into lecture.
Introduction to Software Engineering (CSCI 0320) Give students an understanding of professional ethics in Computer Science. Arm them with the tools to make decisions rooted in professional ethics over the course of the semester and their careers. Impress upon students that CS is an impactful discipline with the potential to be harmful, and provide them with the tools to analyze their actions in the context of potential harm. Introduce students to best practices for inclusive software engineering. Readings and reflections about ethical issues surrounding technology, in-class discussions about the readings and other ethical questions, integrating inclusive design practices into curriculum, providing students with assignments in which they practice professionalism in industry.
Introduction to Computer Systems Security (CSCI 1660) Encourage students to consider the broader implications of computer systems security, from the perspective of users, software developers, and policymakers. By covering case studies about legal backdoors to encrypted systems, responsible disclosure, firewalls, and more, we hope to teach students to be mindful of their own security and privacy, as well as the power they hold over others’ security and privacy as computer scientists. Integrated preliminary discussion topics into the lecture slides, as well as written longer questions for each of the six homeworks. These questions ask students to consider recent case studies, how users are impacted, and what the responsibilities of users, developers, and policymakers are in these cases.
Machine Learning (CSCI 1420) Introduce students to topics and frameworks to approach thinking about ethics in machine learning applications. Emphasize critical thinking of the ethical implications in processing data, writing algorithms, and post model analysis as a fundamental part of the machine learning process. Connect students to resources to learn more about research and applications in machine learning ethics. Readings and responses about the ethical limitations of machine learning models, case studies about ethical issues surrounding machine learning applications, new datasets and projects to illustrate topics like algorithmic fairness in practice.
Computer Science: An Integrated Introduction (CSCI 0180) Introduce students to the political and social values embedded in technology. Model ways of assessing the social impact of technology in the testing and design stages of development. The class begins with a reading assignment introducing students to the social and ethical questions surrounding code. Written and technical questions connect course projects to related ethical issues. In-class examples draw a connection between algorithms and their social contexts.

Team

Current

Stanley Yip

HTA

Jessica Dai

HTA

Aaron Zhang

General ETA

Amanda Lee

CS112 ETA

Ben Vu

CS1951A ETA

Eli Morimoto

CS112 ETA

Hannah Chow

CS1660 ETA

Heila Precel

CS32 ETA

Isabella Ting

CS1430 ETA

Jamison Wells

CS1320 ETA

Jessy Ma

CS160 ETA

Karen Tu

CS1420 ETA

Katie Friis

CS1430 ETA

Kelvin Yang

CS1420 ETA

Kiran Merchant

CS32 ETA

Lena Cohen

CS180 ETA

Livia Giminez

CS111 ETA

Shawna Huang

CS1660 ETA

Shenandoah Duraideivamani

CS180 ETA

Shira Abramovich

CS1320 ETA

Tzion Jones

CS160 ETA

Tzuhwan Seet

CS111 ETA

Huayu Ouyang

CS1951A ETA

Ugur Cetintemel

Faculty Director

Former

  • Hal Triedman - CS1470 ETA, Fall 2019
  • Andy Rickert - CS1300 ETA, Fall 2019
  • Kendrick Tan - CS0150 ETA, Fall 2019
  • Rebecca Zuo - CS0150 ETA, Fall 2019
  • Signe Golash - CS0170 ETA Fall 2019

Resources

Theme Title Author
Professional Tech What is Computer Ethics James H. Moor
Professional Tech You Are Not A Gadget Jaron Lanier
Professional Tech Future Ethics Cennydd Bowles
Professional Tech Code Lawrence Lessig
Digital Media and Democracy Amusing Ourselves to Death Neil Postman
Digital Media and Democracy #Republic Cass Sunstein
Addictive Tech The Binge Breaker Bianca Bosker
Accesible Tech An Alphabet of Accessibility Issues Anne Gibson
Tech Can't Solve Everything Automating Inequality Virginia Eubanks
Tech Can't Solve Everything Artificial Unintelligence Meredith Broussard
Future of Labor, Job Automation, and The Sharing Economy The Taking Economy: Uber, Information, and Power Ryan Calo, Alex Rosenblatt
Future of Labor, Job Automation, and The Sharing Economy Automation, jobs, and the future of work McKinsey
Algorithmic Bias in AI Algorithmic Justice League Joy Buolamwini
Algorithmic Bias in AI Weapons of Math Destruction Cathy O’Neil
Algorithmic Bias in AI Algorithms of Oppression Safiya Noble
Questions? Contact us at ethicsTAs@lists.cs.brown.edu