Notes on Teaching Ethics in NLP - Revision history

Xandaschofield: /* General Takeaways */

2021-01-10T00:45:13Z

General Takeaways

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	These notes attempt to summarize themes and conclusions that arose in discussion repeatedly. While these themes and ideas arose frequently enough in discussion to merit recording, they do not necessarily represent the views of all of the participants above.		These notes attempt to summarize themes and conclusions that arose in discussion repeatedly. While these themes and ideas arose frequently enough in discussion to merit recording, they do not necessarily represent the views of all of the participants above.

	* Participants reflecting on the communities of learners they were interested in all described them as having little or no common background in discussing research ethics, whether around human subjects research or broader research impact. There is strong agreement that relying on students to come in with the vocabulary to discuss these issues is unlikely to lead to a rich conversation, so participants highlighted the importance of providing advance access to resources that help shape a core vocabulary. This can include classic resources like the Nuremberg Code and Belmont report, as well as more recent readings in NLP (see [[Ethics in NLP]] for a thorough list of works and syllabi that may help).		* '''Give students the time and resources to talk about ethical topics.''' Participants reflecting on the communities of learners they were interested in all described them as having little or no common background in discussing research ethics, whether around human subjects research or broader research impact. There is strong agreement that relying on students to come in with the vocabulary to discuss these issues is unlikely to lead to a rich conversation, so participants highlighted the importance of providing advance access to resources that help shape a core vocabulary. This can include classic resources like the Nuremberg Code and Belmont report, as well as more recent readings in NLP (see [[Ethics in NLP]] for a thorough list of works and syllabi that may help).
	* Real-world case studies received much more attention in our discussion than toy problems, mirroring a general interest in talking about actual scenarios where language technology can cause (or has caused) harm. Using these problems as the basis for a short discussion presents a challenge, however, as in a computational setting it may be easy to abstract complex societal problems into classification tasks and metrics. It is therefore crucial to provide resources that reflect meaningfully on the social realities of these problems and that embody the principle of “nothing about us without us” (popularized by disability rights activists Michael Masutha, William Rowland, and later James Charlton) -- for example, case studies related to disability should include readings about disability and the disabled community the technology would support that represents the voices of those affected.		* '''Focus on real problems.''' Real-world case studies received much more attention in our discussion than toy problems, mirroring a general interest in talking about actual scenarios where language technology can cause (or has caused) harm. Using these problems as the basis for a short discussion presents a challenge, however, as in a computational setting it may be easy to abstract complex societal problems into classification tasks and metrics. It is therefore crucial to provide resources that reflect meaningfully on the social realities of these problems and that embody the principle of “nothing about us without us” (popularized by disability rights activists Michael Masutha, William Rowland, and later James Charlton) -- for example, case studies related to disability should include readings about disability and the disabled community the technology would support that represents the voices of those affected.
	* Since many courses or instructional settings related to NLP are embedded within a computer science or data science curriculum, both learners and instructors may be accustomed to rubrics with well-defined “correctness” of a solution. Many of these exercises below present a discussion, investigation, or reflection with inherent subjectivity and unanswerable questions. Learners and instructors will benefit from a clear rubric of what meaningful participation in such an assessment looks like in advance of engaging to ensure they feel confident in how to engage with the exercise.		* '''Give clear assessment criteria.''' Since many courses or instructional settings related to NLP are embedded within a computer science or data science curriculum, both learners and instructors may be accustomed to rubrics with well-defined “correctness” of a solution. Many of these exercises below present a discussion, investigation, or reflection with inherent subjectivity and unanswerable questions. Learners and instructors will benefit from a clear rubric of what meaningful participation in such an assessment looks like in advance of engaging to ensure they feel confident in how to engage with the exercise.
	* Depending on background or personal experience with a topic, learners may vary in their comfort with expressing their views or experiences in a group setting and may desire a way to communicate that is more private than a group discussion, whether to ask questions on a social topic with which they are less familiar or to communicate about their own experiences related to discrimination without scrutiny from peers. One can help these students by providing clear information about how to access those channels and, if possible, by providing resources before they are solicited to help learn more or to get support.		* '''Provide multiple paths to participation.''' Depending on background or personal experience with a topic, learners may vary in their comfort with expressing their views or experiences in a group setting and may desire a way to communicate that is more private than a group discussion, whether to ask questions on a social topic with which they are less familiar or to communicate about their own experiences related to discrimination without scrutiny from peers. One can help these students by providing clear information about how to access those channels and, if possible, by providing resources before they are solicited to help learn more or to get support.
			* '''Emphasize that this is a core skill.''' Due to compressed syllabi, ethics is often relegated to one day or one assignment of a course, and changing from that paradigm takes time. However, for students to generalize from these lessons, it is important to emphasize that skills of critical inquiry into possible risks and harms of technology are not a side topic, but part of the work of doing NLP. This can be done through reference back to these exercises in other assignments or in class discussions, as well as reflections on your own work.

	== Dual Use ==		== Dual Use ==

Xandaschofield: Updating header text

2021-01-07T21:19:03Z

Updating header text

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	~~'''This page is currently under construction~~, ~~but should~~ be ~~done soon!'''~~		The following notes were compiled from discussions during the [https://www.aclweb.org/anthology/2020.acl-tutorials.2/ ACL Tutorial on Integrating Ethics in the NLP Curriculum] ([https://www.cs.hmc.edu/~xanda/files/acl2020tutorial_teachingethicsinnlp.pdf slides]) conducted on July 5, 2020 as part of the virtual ACL 2020 workshop. While the notes below do not necessarily give fully designed assignments, they can provide a starting point for ideas of ethical domains that could be useful for assignments, structures that could work for different classrooms to approach these subjects, and reflections from conversation that arose during the tutorial about what might be helpful or challenging in approaching ethical subjects in NLP. It is the hope of the tutorial organizers to further flesh out assignments using these notes at a later date.

	The following notes were compiled from form submissions after the [https://www.aclweb.org/anthology/2020.acl-tutorials.2/ ACL Tutorial on Integrating Ethics in the NLP Curriculum] ([https://www.cs.hmc.edu/~xanda/files/acl2020tutorial_teachingethicsinnlp.pdf slides]) conducted on July 5, 2020 as part of the virtual ACL 2020 workshop. In the first section, we provide some overarching conclusions; subsequent sections provide quoted and paraphrased text from our contributors with ideas of case studies, exercise formats, and reflections about setting up these exercises.		In the first section, we provide some overarching conclusions; subsequent sections provide quoted and paraphrased text from our contributors with ideas of case studies, exercise formats, and reflections about setting up these exercises.

	'''Contributors:''' Noëmi Aepli, Nader Akoury, Patrick Alba, Dan Bareket, Steven Bedrick, Emily M. Bender, Su Lin Blodgett, Jamie Brandon, Chris Brew, Trista Cao, Daniel Dahlmeier, Vidas Daudaravicius, Brent Davis, Chad DeChant, Marcia Derr, Guy Emerson, Hannah Eyre, Anjalie Field, Paige Finkelstein, Erick Fonseca, Vasundhara Gautam, Dimitra Gkatzia, Sharon Goldwater, Vivek Gupta, Samar Haider, Maartje ter Hoeve, Dirk Hovy, Aya Iwamoto, Amani Jamal, Micaela Kaplan, Suma Kasa, Katherine Keith, Haley Lepp, Angela Lin, Diane Litman, Yang Liu, Amnon Lotenberg, Emma Manning, Anna Marbut, Krystal Maughan, Sabrina Mielke, Sneha N, Silvia Necsulescu, Isar Nejadgholi, Vlad Niculae, Franziska Pannach, Ted Pedersen, Ben Peters, Gabriela Ramirez, Agata Savary, Tatjana Scheffler, Alexandra Schofield, Nora AlTwairesh, Nandan Thakur, Jannis Vamvas, Esaú Villatoro, Rich Wicentowski, Bock, Cissi, Manchego, Sonali, Tommaso, YeeMan		'''Contributors:''' Noëmi Aepli, Nader Akoury, Patrick Alba, Dan Bareket, Steven Bedrick, Emily M. Bender, Su Lin Blodgett, Jamie Brandon, Chris Brew, Trista Cao, Daniel Dahlmeier, Vidas Daudaravicius, Brent Davis, Chad DeChant, Marcia Derr, Guy Emerson, Hannah Eyre, Anjalie Field, Paige Finkelstein, Erick Fonseca, Vasundhara Gautam, Dimitra Gkatzia, Sharon Goldwater, Vivek Gupta, Samar Haider, Maartje ter Hoeve, Dirk Hovy, Aya Iwamoto, Amani Jamal, Micaela Kaplan, Suma Kasa, Katherine Keith, Haley Lepp, Angela Lin, Diane Litman, Yang Liu, Amnon Lotenberg, Emma Manning, Anna Marbut, Krystal Maughan, Sabrina Mielke, Sneha N, Silvia Necsulescu, Isar Nejadgholi, Vlad Niculae, Franziska Pannach, Ted Pedersen, Ben Peters, Gabriela Ramirez, Agata Savary, Tatjana Scheffler, Alexandra Schofield, Nora AlTwairesh, Nandan Thakur, Jannis Vamvas, Esaú Villatoro, Rich Wicentowski, Bock, Cissi, Manchego, Sonali, Tommaso, YeeMan

EmilyBender: /* Case Studies */

2021-01-01T06:07:55Z

Case Studies

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	* Recommender systems are often customized to detect or respond to user preferences by comparing with similar uses. How might bias manifest in these cases?		* Recommender systems are often customized to detect or respond to user preferences by comparing with similar uses. How might bias manifest in these cases?
	* Imagine if an MT or summarization system was used to communicate in a scenario with some kind of importance, e.g. something big and scary (refugee/disaster scenario) or just mundane but important (scheduling an appointment, opening a bank account, etc.). What kinds of errors might arise from common sources of data bias?		* Imagine if an MT or summarization system was used to communicate in a scenario with some kind of importance, e.g. something big and scary (refugee/disaster scenario) or just mundane but important (scheduling an appointment, opening a bank account, etc.). What kinds of errors might arise from common sources of data bias?
	* Grammatical error correction often represents only a minimal number of dialects. How might these systems be made to better handle linguistic variation.		* Grammatical error correction often represents only a minimal number of dialects. How might these systems be made to better handle linguistic variation?
	* Consider a sentiment analysis tool whose text input handles names. How might such a system respond to proper names of individuals? Could systems learn bias in this context? How would one mitigate that bias?		* Consider a sentiment analysis tool whose text input handles names. How might such a system respond to proper names of individuals? Could systems learn bias in this context? How would one mitigate that bias?

EmilyBender: /* Assessment Options */

2021-01-01T06:03:16Z

Assessment Options

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	* Standard group discussion with slides and report for a small toy classifier from "artificial" data (maybe some less fraught social characteristic) and see what can actually be discovered this way. Would need: Some standard dataset, some programming skill with major classification framework e.g. NLTK.		* Standard group discussion with slides and report for a small toy classifier from "artificial" data (maybe some less fraught social characteristic) and see what can actually be discovered this way. Would need: Some standard dataset, some programming skill with major classification framework e.g. NLTK.
	* Short paper on the topic, possibly with guiding questions. Would need: access to an existing system or classifier. Transcript of exploration of system characteristics.		* Short paper on the topic, possibly with guiding questions. Would need: access to an existing system or classifier. Transcript of exploration of system characteristics.
	* Have them write an obligatory section with an ethical discussion in reports for their own project. Ground it in the three ~~kind~~ of context from before: sociological, philosophical and technical perspective. Would need: large project as part of the course, literature review and prior discussion of case studies in dual use.		* Have them write an obligatory section with an ethical discussion in reports for their own project. Ground it in the three kinds of context from before: sociological, philosophical and technical perspective. Would need: large project as part of the course, literature review and prior discussion of case studies in dual use.
	* Ask students to post one new thought on a class online forum on a topic and respond to a fellow student's ideas. That way, participation is “mandated”, and there is an understanding of what participation a student should have. Additionally, the online format allows people a certain amount of distance from their peers, which may make it easier to express their own opinions or ideas. Would need: online forum, introduction module for online discussion/understanding of dual use, case study or article as basis for discussion		* Ask students to post one new thought on a class online forum on a topic and respond to a fellow student's ideas. That way, participation is “mandated”, and there is an understanding of what participation a student should have. Additionally, the online format allows people a certain amount of distance from their peers, which may make it easier to express their own opinions or ideas. Would need: online forum, introduction module for online discussion/understanding of dual use, case study or article as basis for discussion
	* Explore data subjectivity. E.g. show the complexity of labeling depression in a text corpus, e.g. by exploring the data, self-labeling some data, doing inter-annotator agreement study with the class. Would need: dataset, preparation on data labeling, metrics, and agreement		* Explore data subjectivity. E.g. show the complexity of labeling depression in a text corpus, e.g. by exploring the data, self-labeling some data, doing inter-annotator agreement study with the class. Would need: dataset, preparation on data labeling, metrics, and agreement

EmilyBender: /* Assessment Options */

2021-01-01T06:00:54Z

Assessment Options

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	=== Assessment Options ===		=== Assessment Options ===

	* Class discussion of a case study. Grade participation based on creativity of thinking of the dual uses, depth of discussion of positive and negative consequences. Reaching a level of '~~analyze´~~ according the Bloom's taxonomy is usually a good outcome. If students are able to explain or distinguish why some technologies ~~behaves~~ as they do, it means they have understood the components of the systems and they can visualize the ~~posible~~ outcomes. Would need: articles on dual use		* Class discussion of a case study. Grade participation based on creativity of thinking of the dual uses, depth of discussion of positive and negative consequences. Reaching a level of 'analyze' according the Bloom's taxonomy is usually a good outcome. If students are able to explain or distinguish why some technologies behave as they do, it means they have understood the components of the systems and they can visualize the possible outcomes. Would need: articles on dual use
	* Standard group discussion with slides and report for a small toy classifier from "artificial" data (maybe some less fraught social characteristic) and see what can actually be discovered this way. Would need: Some standard dataset, some programming skill with major classification framework e.g. NLTK.		* Standard group discussion with slides and report for a small toy classifier from "artificial" data (maybe some less fraught social characteristic) and see what can actually be discovered this way. Would need: Some standard dataset, some programming skill with major classification framework e.g. NLTK.
	* Short paper on the topic, possibly with guiding questions. Would need: access to an existing system or classifier. Transcript of exploration of system characteristics.		* Short paper on the topic, possibly with guiding questions. Would need: access to an existing system or classifier. Transcript of exploration of system characteristics.

EmilyBender: Punctuation tweak

2021-01-01T05:30:30Z

Punctuation tweak

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	'''This page is currently under construction, but should be done soon!'''		'''This page is currently under construction, but should be done soon!'''

	The following notes were compiled from form submissions after the [https://www.aclweb.org/anthology/2020.acl-tutorials.2/ ACL Tutorial on Integrating Ethics in the NLP Curriculum] [https://www.cs.hmc.edu/~xanda/files/acl2020tutorial_teachingethicsinnlp.pdf (slides)] conducted on July 5, 2020 as part of the virtual ACL 2020 workshop. In the first section, we provide some overarching conclusions; subsequent sections provide quoted and paraphrased text from our contributors with ideas of case studies, exercise formats, and reflections about setting up these exercises.		The following notes were compiled from form submissions after the [https://www.aclweb.org/anthology/2020.acl-tutorials.2/ ACL Tutorial on Integrating Ethics in the NLP Curriculum] ([https://www.cs.hmc.edu/~xanda/files/acl2020tutorial_teachingethicsinnlp.pdf slides]) conducted on July 5, 2020 as part of the virtual ACL 2020 workshop. In the first section, we provide some overarching conclusions; subsequent sections provide quoted and paraphrased text from our contributors with ideas of case studies, exercise formats, and reflections about setting up these exercises.

	'''Contributors:''' Noëmi Aepli, Nader Akoury, Patrick Alba, Dan Bareket, Steven Bedrick, Emily M. Bender, Su Lin Blodgett, Jamie Brandon, Chris Brew, Trista Cao, Daniel Dahlmeier, Vidas Daudaravicius, Brent Davis, Chad DeChant, Marcia Derr, Guy Emerson, Hannah Eyre, Anjalie Field, Paige Finkelstein, Erick Fonseca, Vasundhara Gautam, Dimitra Gkatzia, Sharon Goldwater, Vivek Gupta, Samar Haider, Maartje ter Hoeve, Dirk Hovy, Aya Iwamoto, Amani Jamal, Micaela Kaplan, Suma Kasa, Katherine Keith, Haley Lepp, Angela Lin, Diane Litman, Yang Liu, Amnon Lotenberg, Emma Manning, Anna Marbut, Krystal Maughan, Sabrina Mielke, Sneha N, Silvia Necsulescu, Isar Nejadgholi, Vlad Niculae, Franziska Pannach, Ted Pedersen, Ben Peters, Gabriela Ramirez, Agata Savary, Tatjana Scheffler, Alexandra Schofield, Nora AlTwairesh, Nandan Thakur, Jannis Vamvas, Esaú Villatoro, Rich Wicentowski, Bock, Cissi, Manchego, Sonali, Tommaso, YeeMan		'''Contributors:''' Noëmi Aepli, Nader Akoury, Patrick Alba, Dan Bareket, Steven Bedrick, Emily M. Bender, Su Lin Blodgett, Jamie Brandon, Chris Brew, Trista Cao, Daniel Dahlmeier, Vidas Daudaravicius, Brent Davis, Chad DeChant, Marcia Derr, Guy Emerson, Hannah Eyre, Anjalie Field, Paige Finkelstein, Erick Fonseca, Vasundhara Gautam, Dimitra Gkatzia, Sharon Goldwater, Vivek Gupta, Samar Haider, Maartje ter Hoeve, Dirk Hovy, Aya Iwamoto, Amani Jamal, Micaela Kaplan, Suma Kasa, Katherine Keith, Haley Lepp, Angela Lin, Diane Litman, Yang Liu, Amnon Lotenberg, Emma Manning, Anna Marbut, Krystal Maughan, Sabrina Mielke, Sneha N, Silvia Necsulescu, Isar Nejadgholi, Vlad Niculae, Franziska Pannach, Ted Pedersen, Ben Peters, Gabriela Ramirez, Agata Savary, Tatjana Scheffler, Alexandra Schofield, Nora AlTwairesh, Nandan Thakur, Jannis Vamvas, Esaú Villatoro, Rich Wicentowski, Bock, Cissi, Manchego, Sonali, Tommaso, YeeMan

EmilyBender: Added link to tutorial slides

2021-01-01T05:29:57Z

Added link to tutorial slides

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	'''This page is currently under construction, but should be done soon!'''		'''This page is currently under construction, but should be done soon!'''

	The following notes were compiled from form submissions after the [https://www.aclweb.org/anthology/2020.acl-tutorials.2/ ACL Tutorial on Integrating Ethics in the NLP Curriculum] conducted on July 5, 2020 as part of the virtual ACL 2020 workshop. In the first section, we provide some overarching conclusions; subsequent sections provide quoted and paraphrased text from our contributors with ideas of case studies, exercise formats, and reflections about setting up these exercises.		The following notes were compiled from form submissions after the [https://www.aclweb.org/anthology/2020.acl-tutorials.2/ ACL Tutorial on Integrating Ethics in the NLP Curriculum] [https://www.cs.hmc.edu/~xanda/files/acl2020tutorial_teachingethicsinnlp.pdf (slides)] conducted on July 5, 2020 as part of the virtual ACL 2020 workshop. In the first section, we provide some overarching conclusions; subsequent sections provide quoted and paraphrased text from our contributors with ideas of case studies, exercise formats, and reflections about setting up these exercises.

	'''Contributors:''' Noëmi Aepli, Nader Akoury, Patrick Alba, Dan Bareket, Steven Bedrick, Emily M. Bender, Su Lin Blodgett, Jamie Brandon, Chris Brew, Trista Cao, Daniel Dahlmeier, Vidas Daudaravicius, Brent Davis, Chad DeChant, Marcia Derr, Guy Emerson, Hannah Eyre, Anjalie Field, Paige Finkelstein, Erick Fonseca, Vasundhara Gautam, Dimitra Gkatzia, Sharon Goldwater, Vivek Gupta, Samar Haider, Maartje ter Hoeve, Dirk Hovy, Aya Iwamoto, Amani Jamal, Micaela Kaplan, Suma Kasa, Katherine Keith, Haley Lepp, Angela Lin, Diane Litman, Yang Liu, Amnon Lotenberg, Emma Manning, Anna Marbut, Krystal Maughan, Sabrina Mielke, Sneha N, Silvia Necsulescu, Isar Nejadgholi, Vlad Niculae, Franziska Pannach, Ted Pedersen, Ben Peters, Gabriela Ramirez, Agata Savary, Tatjana Scheffler, Alexandra Schofield, Nora AlTwairesh, Nandan Thakur, Jannis Vamvas, Esaú Villatoro, Rich Wicentowski, Bock, Cissi, Manchego, Sonali, Tommaso, YeeMan		'''Contributors:''' Noëmi Aepli, Nader Akoury, Patrick Alba, Dan Bareket, Steven Bedrick, Emily M. Bender, Su Lin Blodgett, Jamie Brandon, Chris Brew, Trista Cao, Daniel Dahlmeier, Vidas Daudaravicius, Brent Davis, Chad DeChant, Marcia Derr, Guy Emerson, Hannah Eyre, Anjalie Field, Paige Finkelstein, Erick Fonseca, Vasundhara Gautam, Dimitra Gkatzia, Sharon Goldwater, Vivek Gupta, Samar Haider, Maartje ter Hoeve, Dirk Hovy, Aya Iwamoto, Amani Jamal, Micaela Kaplan, Suma Kasa, Katherine Keith, Haley Lepp, Angela Lin, Diane Litman, Yang Liu, Amnon Lotenberg, Emma Manning, Anna Marbut, Krystal Maughan, Sabrina Mielke, Sneha N, Silvia Necsulescu, Isar Nejadgholi, Vlad Niculae, Franziska Pannach, Ted Pedersen, Ben Peters, Gabriela Ramirez, Agata Savary, Tatjana Scheffler, Alexandra Schofield, Nora AlTwairesh, Nandan Thakur, Jannis Vamvas, Esaú Villatoro, Rich Wicentowski, Bock, Cissi, Manchego, Sonali, Tommaso, YeeMan

Xandaschofield: /* Bias */

2020-12-31T09:00:59Z

Bias

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	== Bias ==		== Bias ==
	To be ~~added~~.
			=== Case Studies ===
			* Have students consider an existing system and try to determine ways to measure bias in order to check it, following e.g. the template of [http://blog.conceptnet.io/posts/2017/how-to-make-a-racist-ai-without-really-trying/ How to Make a Racist AI Without Really Trying] by Robyn Speer.
			* Consider an automatic speech recognition (ASR) system used to automatically caption conference presentations. What are the impacts of bias in such a system? What kinds of bias may exist?
			* Suppose ASR is being used to assess call center performance. If ASR systems are being trained on mostly white voices and are actively used to assess performance of call center agents, how might the results of this bias affect performance metrics, and how might this effect be mitigated?
			* Recommender systems are often customized to detect or respond to user preferences by comparing with similar uses. How might bias manifest in these cases?
			* Imagine if an MT or summarization system was used to communicate in a scenario with some kind of importance, e.g. something big and scary (refugee/disaster scenario) or just mundane but important (scheduling an appointment, opening a bank account, etc.). What kinds of errors might arise from common sources of data bias?
			* Grammatical error correction often represents only a minimal number of dialects. How might these systems be made to better handle linguistic variation.
			* Consider a sentiment analysis tool whose text input handles names. How might such a system respond to proper names of individuals? Could systems learn bias in this context? How would one mitigate that bias?

			=== Assessment Options ===
			* Consider a training data set for a task (e.g., ASR). Quantify in a short report what sorts of biases these data may have and how this might affect a downstream task. Will need: existing dataset for task, (optionally) an existing model for the task to see how it compares.
			* Lead a discussion with seminar students. Prompt them with a case study, and ask them to discuss (in small, then large groups) possible sources of bias, how they might manifest, and how they might be mitigated. Will need: prior background on the domain, prompts and examples to brainstorm from. Include discussion and reflection questions, such as:
			** What should go in documentation so users are aware of bias?
			** How to improve by collecting data?
			** Speculate on important sources of bias based on knowledge about data, insight from observed errors.
			** What sources can be addressed? What cannot?
			** Compare different ASR systems (trained on different dialects, tested on a different dialect)
			** (post-discussion) Do you recognize issues with systems you interact with, which you didn’t recognize before?
			* Consider a larger NLP “pipeline” to address a more complex task. Have students first break this process into “bins”, then split into groups that each focus on one “bin” of the pipeline. Groups can switch between parts after leaving feedback that following groups can see. Would need: background on domain of task and bias, whiteboards/collaborative writing technology, guidance on how to critique as constructive feedback
			* Ask participants to provide their own examples of problematic NLP. Then, in groups, have team members to later put together presentations or write a paper about problematic bias seen in past projects (or, in industry, in a work context). For less technical team members, ensure that they are able to ask the questions necessary to identify bias in data received. Provide a dataset and have each team member come up with relevant questions.
			* Lab exercise for small groups: after implementing a simple recommender system and training it on instructor-provided data, each group is assigned an “identity” (one user of the recommender system, with a particular set of attributes and preferences) and asked to interact with the system as that user, then prepare a short write-up with their findings. Final report includes input from all students and reflects different backgrounds. Final report identifies which groups experience disparate outcomes from the system and how that bias is derived from the data, as well as how this would manifest in the broader context in which these systems are used. This report should include an opportunity for input from all students that reflects different backgrounds. Will need: access to a trained recommender system, background on the model.
			* Have students role play the possible outcomes of a malfunctioning system and reflect on how they would intervene in the way the system runs. After the exercises, students do brief presentations in their groups about the outcomes, focusing on different assigned elements of the exercise. Will need: a planned scenario or script we have the students work through actually exhibits the problems we are "hoping" that it does around bias, an MT/related system relevant for the scenario, plus whatever background materials to support the dialogue we want them to try and do. Students might be able to bring their own system- perhaps one they had built in an earlier phase of the class; that could be part of the exercise- looking at their own system’s training and behavior.
			* As a lab exercise using e.g. a notebook (as in Robyn Speer’s [http://blog.conceptnet.io/posts/2017/how-to-make-a-racist-ai-without-really-trying/ blog post]), using existing references and models, find metrics that can capture different forms of bias, starting with basic examples. Discuss what the pros and cons of different existing metrics are and why there is no one “bias” metric. Ensure students also take the opportunity to look at misclassifications to understand what these metrics do or don’t capture. Will need: some background/reading on bias metrics, prepared code/notebook with data, prior unit on word embeddings, prerequisite understanding of statistics and rigorous evaluation
			* In an industry setting, have a team working on a project with human-centered consequences (e.g. an automatic recognition, scoring, or classification tool for people) read some general background on bias in NLP systems and the ACL/ACM Code of Ethics. Then, have an formal presentation on these topics and an open forum discussion among colleagues to brainstorm and discuss how these relate to the products being worked on. Isolate concrete steps to be taken to modify existing policies to handle data and systems. Meeting notes consolidated from discussions which establish all colleagues are on the same page are also crucial. Assessment will also include examination of additional changes in documentation about testing on different minority populations’ voices or text inputs and gauging associated performances in comparison with majority speakers.
			* Instructions can provide an off-the-shelf model with test data sets of different language varieties. Students will experiment with the model and its performance over the different test data sets to appreciate the variation of outcomes. Will need: dataset with linguistic variation (if appropriate, from dialects spoken by members of the class). After the practical exercises, students write a report about their findings that addresses these questions:
			** Describe the potential impact of linguistic variation on the functioning of NLP/speech technology
			** Describe how to choose what kinds of language variation should be tested for
			** Reason about how differential performance for different social groups can lead to adverse impacts
			** Articulate what kind of documentation should accompany NLP/speech technology to facilitate safe deployment
			* Have teams start by building some “debiased” model, e.g. for sentiment analysis or classification. Give examples of different stakeholders and different kinds of harm, in maybe a tabular form. Then, have those teams trade models and attempt to determine biases still in these systems. Would need: some shared background on that model and domain, starting models to adapt.
			* Have students write a suite of “test cases” for existing models designed to perform a benchmark task to see if these models display certain kinds of biases. Students could design pairs of words/sentences to test out bias in word embeddings / sentiment analysis, e.g., “Emily” and “Shaniqua” as names. Then, have them write an essay about what they learned from designing the exercise and testing it on some public models. Would need: existing background on model, coding for NLP, biases.


			=== Reflections ===
			* There is some concern about how to come up with an “exam” question for this topic, but an idea offered was an open-ended question that describes a dataset and algorithm, and have the students describe the potential biases, and how they would measure them, and mitigation strategies (where a thoughtful answer that referenced ideas from the course reading would receive full credit)
			* Build specific activities around discussion to avoid people feeling defensive/targeted about products that they’ve built.
			* Express an understanding that everything is going to be biased, no matter how hard we work to avoid it.
			* For in-class discussion or role play exercises,, the exercise would be something that would need to work in an online/distance setting (COVID, etc.), but would also need to ensure lots of interactivity. The exercise would be designed around making that happen.
			* Most examples discussing model bias come from the US/UK - how can they be interesting for all students from various countries and ethnicities?
			* When engaging students about bias in data and models, how do students relate to the underlying data, do they feel victims or perpetrators?
			* Formats like notebooks with pre-written code can be welcoming to students with non-CS background to still engage with the same questions. When students have mixed backgrounds, build on different students’ strengths (technical vs linguistic/psychology backgrounds).
			* Annotators are different from programmers, who are also sometimes different from the people who check the annotations - how do we deal with bias entering from different stages? Can we train annotators using these sorts of exercises?
			* Is the important thing to “solve” debiasing word embeddings or to make them more cognizant of the fact that these biases exist?
			* Speer’s work has an interesting technical aspect, but to some students the findings that some things are not acceptable to say might be novel. Work immediately critical of these models without explaining what the harms are might be hurtful to people who haven’t thought about these issues yet. The phrasing of “of course we don’t want such issues” can alienate people.
			* Potentially have a(n anonymous) survey, to figure out what the consensus is to model teaching based on where we stand.
			* Using story telling to tell how biased systems can impact people's lives without technical knowledge. Sometimes stories work.

Xandaschofield at 23:49, 30 December 2020

2020-12-30T23:49:50Z

← Older revision		Revision as of 23:49, 30 December 2020
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			'''This page is currently under construction, but should be done soon!'''

	The following notes were compiled from form submissions after the [https://www.aclweb.org/anthology/2020.acl-tutorials.2/ ACL Tutorial on Integrating Ethics in the NLP Curriculum] conducted on July 5, 2020 as part of the virtual ACL 2020 workshop. In the first section, we provide some overarching conclusions; subsequent sections provide quoted and paraphrased text from our contributors with ideas of case studies, exercise formats, and reflections about setting up these exercises.		The following notes were compiled from form submissions after the [https://www.aclweb.org/anthology/2020.acl-tutorials.2/ ACL Tutorial on Integrating Ethics in the NLP Curriculum] conducted on July 5, 2020 as part of the virtual ACL 2020 workshop. In the first section, we provide some overarching conclusions; subsequent sections provide quoted and paraphrased text from our contributors with ideas of case studies, exercise formats, and reflections about setting up these exercises.

Xandaschofield: Adding privacy text

2020-12-30T23:47:12Z

Adding privacy text

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 * For group discussions/mock reviews: graduate students are more involved in research institutions and reviewing processes, while undergrads may not be. It’s important for students to reflect on their personal stances and what role they might have in a decision like this, and that role is different for undergraduates and graduates (Keeping in mind that graduate students and undergraduate students may not know where they want to go or what their final role will be). In a classroom with both undergrad and grad students, it might be beneficial to have blended groups. This type of assignment might be applicable to corporate workshops also.
-== Privacy ==
+== Privacy and Anonymity ==
-To be added.
 == Bias ==
 To be added.