Computer science research has changed dramatically over the last ten years, both in terms of the ways in which it is conducted as well as the ends to which it is applied. Research in our field is powered by large quantities of data- Tweets, clicks, records, posts, etc.- which, as a rule, were all created by somebody, somewhere, raising complex issues of informed consent and data protection.
Furthermore, the statistical algorithms and computational techniques that we and others in our field build are finding their way into every corner of our lives. They determine what news we see, affect our financial and professional choices, and are beginning to be widely used as part of our criminal justice system. As they do so, they interact with all aspects of our society, flattening some forms of inequality while amplifying others, often in subtle and surprising ways. Seemingly-minor methodological choices by system designers can have profound consequences on people's lives.
This course will explore these and other issues, with the goal of preparing researchers-in-training to responsibly conduct research in this area, and also to prepare them for their professional careers.
Note: Topics and speakers may change through the term as we adjust our course plan.
|1||4/4/17||Introduction & Overview||Jill's Slides; Steve's Slides||Week 1 Readings|
|2||4/10/17||Regulatory Issues, Informed Consent & Crowdsourcing||Week 2 Readings|
|3||4/17/17||Privacy: Introduction||Week 3 Readings||Neelay|
|4||4/24/17||Privacy & Research||Week 4 Readings||Liu|
|5||5/2/17||Copyright & IP||Week 5 Readings||Sfurti, Ogi|
|6||5/9/17||Algorithmic Bias||Week 6 Readings||Philip, Moises|
|7||5/16/17||Authorship & Data||Week 7 Readings||Archana|
|8||5/23/17||Social implications & Dual-Use Problems||Week 8 Readings||Annie, Anders|
|9||5/30/17||Guest Lecture: Meg Mitchell|
|10||6/6/17||Diversity & Representation||Week 10 Readings||Meghana|
|11||6/13/17||Professional Ethics in CS & ML||Week 11 Readings||Rosemary|
|12||6/20/17||FAT-ML||Week 12 Readings||Neha, Tuan|
Assigned readings for each session may be found below, grouped by week. Students are expected to have read each week's readings before class (excepting the first session), and to be prepared to discuss them in class. In cases where readings are not available through the OHSU Library, we will provide copyright-cleared copies of the articles.
Additionally, students will be expected to take turns leading discussions (to be counted towards the participation component of the grade).
This is a one-credit class, and is intended to be highly interactive. As such, grades will be 60% based on class participation, and 40% based on writing assignments.
The class will contain two writing assignments.
For this assignment, first pick one of the basic ethical principles discussed during the first lecture (autonomy, beneficience, etc.). Second, write a short (1-2 pages) written reflection about how you see that principle interacting with an NLP, AI, machine learning, etc. research question (or real-world application of AI/NLP/etc.). This writing prompt is intentionally open-ended: you are free to go in whatever direction you wish with it. You may write about a way in which the principle conflicts with a research goal, or a way in which a particular research project or application is in line with the principle, or any other sort of interaction you wish. Feel free to include references to articles, papers, etc. if necessary.
The assignment is due on Tuesday, April 18. Please turn it in via email, and include "CS692 Assignment 1" in the subject.
Choose an incident, event, occurrence, situation, etc. that has both computational and ethical considerations. You may pick anything you find interesting or compelling, and you should feel free to either choose something that was discussed in one of our reading sets or to pick something entirely different that you find on your own. It can be an example of an ethical lapse or failure, an example of a company/organization/individual acting properly, or a more nuanced situation altogether. This is intentionally open-ended, and we expect you to have to do some reading and research on your own to identify an interesting topic.
Once you have made your selection, generate an analysis following the analytical framework outlined in section 9.3.1 of Gift of Fire, 4th edition. We expect your analysis to be in the 4-5 page range, and to include appropriately-cited external sources (don't worry, citations do not count towards the page count).
This assignment is due on Friday, June 23rd, by 5:00 pm ("end of business", as they say). Please turn it in via email, and include "CS692 Assignment 2" in the subject.
Note: The Nissenbaum reading is available via the OHSU Library- search for the journal "Daedalus", and go from there.
Quote of the day:
"It terrifies me when those who are passionate about Big Data espouse the right to collect, aggregate, and analyze anything that they can get their hands on. In short, if it's accessible, it's fair game. To get here, we've perverted "public" to mean "accessible by anyone under any conditions at any time and for any purpose." We've stripped content out of context, labeled it data, and justified our actions by the fact that we had access to it in the first place."danah boyd, "Privacy and Publicity in the Context of Big Data", presented at WWW '10 (emphasis added) Required Readings
"This song is Copyrighted in U.S., under Seal of Copyright #154085, for a period of 28 years, and anybody caught singin it without our permission, will be mighty good friends of ourn, cause we don't give a dern. Publish it. Write it. Sing it. Swing to it. Yodel it. We wrote it, that's all we wanted to do."
The original copyright notice on "This Land is Your Land", by Woody Guthrie.
A note about this week's readings: we have selected what we think are a manageable number of meaningful, important, and useful readings, given that we have a finite amount of time. This is not intended to be an exhaustive treatment of this topic; there are many missing subjects in this list (as just one example, we did not include any required readings on accessibility or other issues around ableism in tech). Please consider this week's list of readings to be a starting point, and also do consider checking out some of the recommended readings. Also note that many of these readings describe examples of racial or gender bias and occasionally harassment, so pace yourself accordingly if you need to.Required Readings
Note: Most of these are quite short!
Required Readings (as usual, most of these are on the shorter side)
I would like for our industry to be more like librarians. Librarians have a sense of who they are as a profession. They have a central ethical code around patron privacy. The current Librarian of Congress fought John Ashcroft and was ridiculed by him for the idea that people have a right to read what they want to read without it being reported to the FBI. When you go to library school, you absorb this. If you violate it, you’re shunned by your profession. And no one’s bringing in Ukrainian librarians to circumvent it—it’s not like they’re going to offshore library science to get around patron privacy.
You see this in other fields. Doctors say do no harm. Journalists—in theory, at least—are trying to speak truth to power. But we in the tech industry don’t have anything like that. We need some kind of professional identity about who we are and why we do what we do. It can’t just be about “moving fast and breaking things” or not being evil. We need an organization that takes collective action on behalf of its employees and ensures their voice is heard in major decisions affecting the lives of millions of people. Decisions that determine how data is collected, how it’s stored, and what rights people have to it.
— from an interview with Maciej Cegłowski by Ben Tarnoff (full citation below)
2016 FAT-ML workshop)
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