Linguistics 575: Societal Impacts of Language Technology

Winter Quarter, 2025

Course Info

• Lecture: Thursdays, 3:30-5:50 in MGH 284 and online (Zoom link in Canvas)
• Course Canvas (discussion board, assignment submission, grades)

Instructor Info

• Emily M. Bender
• Office/Lab Hours: (most) Tuesdays 2:30-3:30pm online only and (most) Fridays 10-11am (hyrbid)

Syllabus

Description

The goal of this course is to better understand the ethical considerations that arise in the deployment of NLP technology, including how to identify people likely to be impacted by the use of the technology (direct and indirect stakeholders), what kinds of risks the technology poses, and how to design systems in ways that better support stakeholder values.

Through discussions of readings in the growing research literature on fairness, accountability, transparency and ethics (FATE) in NLP and allied fields, and value sensitive design, we will seek to answer the following questions:

• What can go wrong, when we use NLP systems, in terms of specific harms to people?
• How can fix/prevent/mitigate those harms?
• What are our responsibilities as NLP researchers and developers in this regard?

Course projects are expected to take the form of a term paper analyzing some particular NLP task or data set in terms of the concepts developed through the quarter and looking forward to how ethical best practices could be developed for that task/data set.

Prerequisites: Graduate standing. The primary audience for this course is expected to be CLMS students, but graduate students in other programs are also welcome.

Accessibility policies

If you have already established accommodations with Disability Resources for Students (DRS), please communicate your approved accommodations to me at your earliest convenience so we can discuss your needs in this course.

If you have not yet established services through DRS, but have a temporary health condition or permanent disability that requires accommodations (conditions include but not limited to; mental health, attention-related, learning, vision, hearing, physical or health impacts), you are welcome to contact DRS at 206-543-8924 or uwdrs@uw.edu or disability.uw.edu. DRS offers resources and coordinates reasonable accommodations for students with disabilities and/or temporary health conditions. Reasonable accommodations are established through an interactive process between you, your instructor(s) and DRS. It is the policy and practice of the University of Washington to create inclusive and accessible learning environments consistent with federal and state law.

Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW's policy, including more information about how to request an accommodation, is available at Faculty Syllabus Guidelines and Resources. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form available at https://registrar.washington.edu/students/religious-accommodations-request/.

[Note from Emily: The above language is all language suggested by UW and in the immediately preceding paragraph in fact required by UW. I absolutely support the content of both and am struggling with how to contextualize them so they sound less cold. My goal is for this class to be accessible. I'm glad the university has policies that help facilitate that. If there is something you need that doesn't fall under these policies, I hope you will feel comfortable bringing that up with me as well.]

Requirements

• KWLA paper (approx 7 pages) (15)
• Exercise 1: SciComm (10)
• Exercise 2: Ethical Considerations Section (10)
• Participation in discussions (incl. Canvas) (15)
• Peer feedback on term project (5)
• Term project (45)

Schedule of Topics and Assignments (still subject to change)

Bibliography

NOTE This is still very much a work in progress! I have more papers to add, and some of these need to be recategorized.

Overviews/Calls to Action

• Alkhatib, A. (2024). Defining AI
• Amblard, M. (2016). Pour un TAL responsable. Traitement Automatique des Langues , 57 (2), 21-45.
• Bender, Emily M. 2024. Resisting Dehumanization in the Age of "AI". Current Directions in Psychological Science.
• boyd danah. (Sept 13, 2019). Facing the great reckoning head-on. Medium.
• Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature, 538 (7625), 311.
• Dusseau, Melanie. 2024. Burn It Down: A License for AI Resistance Inside Higher Ed.
• Executive Office of the President National Science and Technology Council Committee on Technology. (2016). Preparing for the future of artificial intelligence. (See especially p. 30)
• Fort, K., Adda, G., & Cohen, K. B. (2016). Ethique et traitement automatique des langues et de la parole: entre truismes et tabous. Traitement Automatique des Langues, 57 (2), 7-19.
• Gebru, T. 2024. Who Is Tech Really For? As Silicon Valley chases military tech and funding, it’s losing sight of what inspires its workers
• Green, Daniel, Anna Lauren Hoffmann, and Luke Stark. 2019. Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning. Critical and Ethical Studies of Digital and Social Media.
• Grissom II, A. (2019). Thinking about how NLP is used to serve power: Current and future trends. /Presentation at Widening NLP 2019. [Slides] [Video]
• Hanna, Alex and Park, Tina M. 2020. Against Scale: Provocations and Resistances to Scale Thinking, CSCW Workshop '20.
• Hovy, D., & Spruit, S. L. (2016, August). The social impact of natural language processing. In Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: Short papers) (pp. 591-598). Berlin, Germany: Association for Computational Linguistics.
  • Another presentation by Hovy on related material
• Jackson, Liz and Williams, Rua. 2024. How Disabled People Get Exploited to Build the Technology of War. The New Republic
• Larsen, Benjamin Cedric. 2021. A Framework for Understanding AI-Induced Field Change: How AI Technologies are Legitimized and Institutionalized AIES 2021.
• Lefeuvre-Halftermeyer, A., Govaere, V., Antoine, J.-Y., Allegre, W., Pouplin, S., Departe, J.-P., et al. (2016). Typologie des risques pour une analyse éthique de l'impact des technologies du TAL. Traitement Automatique des Langues, 57 (2), 47-71.
• Markham, A. (May 18, 2016). OKCupid data release fiasco: It's time to rethink ethics education. Data & Society: Points.
• Nissenbaum, H. 2002. New Research Norms for a New Medium In The Commodification of Information, Eds. N. Elkin-Koren and N. Netanel, The Hague: Kluwer Academic Press, 2002. 433-457
• O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. NY: Crown Publishing Group.
• Preston, L. (2024). An Age of Hyperabundance
• Rogaway, P. (2015). The moral character of cryptographic work.
• Shneiderman, B. (2016). Opinion: The dangers of faulty, biased, or malicious algorithms requires independent oversight. Proceedings of the National Academy of Sciences, 113 (48), 13538-13540.
• Sourour, B. (Nov 13, 2016). The code I'm still ashamed of. Medium.
• Tacheva, Jasmina and Ramasubramanian, Srividya. 2023. AI Empire: Unraveling the interlocking systems of oppression in generative AI's global order. Big Data and Society
• Utochkin, Denise. 2024. Cut the 'AI' bullshit, UCPH
• Whittaker, M. 2021. The Steep Cost of Capture, ACM Interactions.

Foundations

• Alkhatib, A. 2021. To Live in Their Utopia: Why Algorithmic Systems Create Absurd Outcomes
• Baria, A. and Cross, K. 2021. The brain is a computer is a brain: neuroscience's internal debate and the social significance of the Computational Metaphor
• Benjamin, R. 2024. The New Artificial Intelligentsia
• Birhane, A. 2021. Algorithmic injustice: a relational ethics approach
• Birhane, A. 2021. The Impossibility of Automating Ambiguity. Artificial Life, 27(1):44-61.
• Birhane, A. et al. 2022. The Values Encoded in Machine Learning Research, FAccT 2022.
• Birhane, A. et al. 2022. The Forgotten Margins of AI Ethics, FAccT 2022.
• Broussard, M. 2023. More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech, MIT Press.
• Calo et al (eds). 2021. Telling Stories: On Culturally Responsive Artificial Intelligence
• Crenshaw, K. (1989). Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. U. Chi. Legal f., 139.
• Crenshaw, K. (1990). Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stan. L. Rev., 43, 1241.
• Cunnigham et al. 2022. On the Grounds of Solutionism: Ontologies of Blackness and HCI, CHI 2022.
• Dai, J. 2020. THE PARADOX OF SOCIALLY RESPONSIBLE COMPUTING The limits and potential of teaching tech ethics
• Ekstrand, M. et al. 2021. Fairness and Discrimination in Information Access Systems
• Gebru, Timnit and Émile Torres. 2024. The TESCREAL bundle: Eugenics and the promise of utopia through artificial general intelligence. First Monday
• Hanna, A. and Park, T.M. 2020. Against Scale: Provocations and Resistances to Scale Thinking
• Hao, K. et al. 2022. AI Colonialism, MIT Technology Review.
• Hoffman, A. L. 2020. Terms of Inclusion: Data, Discourse, Violence
• Hoffman, Anna Lauren and Raina Bloom. 2016. Digitizing Books, Obscuring Women's Work: Google Books, Librarians, and Ideologies of Access. Ada: A Journal of Gender, New Media, and Technology
• Jacobs, A. and Wallach, H. 2021. Measurement and Fairness, FAccT 2021.
• Kukutai T and Taylor J., eds. 2016. Indigenous Data Sovereignty: Toward an agenda, ANU Press.
• Lewis, J.E. et al, 2020. Indigenous Protocol and Artificial Intelligence
• Martin, K. 2022. Algorithmic Bias and Corporate Responsibility: How companies hide behind the false veil of the technological imperative In: Ethics of Data and Analytics.
• Mason, R. 1986. Four Ethical Issues of the Information Age, MIS Quarterly.
• McQuillan, Dan. 2022. Resisting AI: An Anti-fascist Approach to Artificial Intelligence. Bristol University Press.
• Mohamed, S. et al. 2020. Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence
• Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
• Ògúnrẹ̀mí, T. et al. 2023. Decolonizing NLP for "Low-resource Languages": Applying Abebe Birhane's Relational Ethics, Global Review of AI Community Ethics, 1(1).
• O'Neil, C. 2017. Weapons of Math Destruction, Penguin Random House.
• Paullada, A. et al. 2021. Data and its (dis)contents: A survey of dataset development and use in machine learning research Patterns 2.
• Pipkin, E. 2021. A City Is a City -- Against the metaphorization of data
• Prabhakaran V. et al 2022. A Human Rights-Based Approach to Responsible AI Presented as a (non-archival) poster at the 2022 ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization or (EAAMO '22)
• Raji, D. 2020. How our data encodes systematic racism MIT Tech Review.
• Rankin, Y. and Henderson K. 2021. Resisting Racism in Tech Design: Centering the Experiences of Black Youth. CHI 2021.
• Rikap, Cecilia, Cédric Durand, Paolo Gerbaudo, Paris Marx and Edemilson Paraná. 2024. Reclaiming digital sovereignty
• Salvaggio, Eryk. 2024. Challenging The Myths of Generative AI
• Salvaggio, Eryk. 2024. A Critique of Pure LLM Reason. It's Parrots, All The Way Down
• Sambasivan, N. et al. 2021. "Everyone wants to do the model work, not the data work": Data Cascades in High-Stakes AI. CHI 2021.
• Scheiner, B. 2024. The Hacking of Culture and the Creation of Socio-Technical Debt
• Selbst, A.D. et al. 2019. Fairness and Abstraction in Sociotechnical Systems. FAT* 19.
• Vallor et al. 2018. Overview of Ethics in Tech Practice
• Varoquaux, Gael et al. 2024. Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI
• Vinsel, L. 2021. You're Doing It Wrong: Notes on Criticism and Technology Hype
• Whittaker, M. 2021. From Ethics to Organizing: Getting Serious about AI (Video of a talk)
• Talat, Z. et al 2021. A Word on Machine Ethics: A Response to Jiang et al. (2021)
• Zimmermann, A. and Zevenbergen B. 2019. AI Ethics: Seven Traps

Philosophical Underpinnings

• Bartky, S. L. (2002). "Sympathy and solidarity" and other essays (Vol. 32). Rowman & Littlefield.
• Bryson, J. J. (2015). Artificial intelligence and pro-social behaviour. In C. Misselhorn (Ed.), Collective agency and cooperation in natural and artificial systems: Explanation, implementation and simulation (pp. 281-306). Cham: Springer International Publishing.
• Butler, J. (2005). Giving an account of oneself. Oxford University Press. (Available online, through UW libraries)
• Cho, S., Crenshaw, K. W., & McCall, L. (2013). Toward a field of intersectionality studies: Theory, applications, and praxis. Signs: Journal of Women in Culture and Society, 38 (4), 785-810.
• DeLaTorre, M. A. (2013). Ethics: A liberative approach. Fortress Press. (Available online through UW Libraries; read intro + chapter of choice)
• Edgar, S. L. (2003). Morality and machines: Perspectives on computer ethics. Jones & Bartlett Learning. (Available online through UW libraries)
• Fieser, J., & Dowden, B. (Eds.). (2016). Internet encyclopedia of philosophy: Entries on Ethics
• Liamputtong, P. (2006). Researching the vulnerable: A guide to sensitive research methods. Sage. (Available online, through UW libraries)
• Prabhumoye, S., Mayfield, E., & Black, A. W. (2019, August). Principled frameworks for evaluating ethics in NLP systems. In Proceedings of the 2019 workshop on widening nlp (pp. 118-121). Florence, Italy: Association for Computational Linguistics.
• Quinn, M. J. (2014). Ethics for the information age. Pearson.
• Zalta, E. N. (Ed.). (2019). The Stanford encyclopedia of philosophy (Winter 2016 Edition ed.): Entries on Ethics

Value Sensitive Design and Other Design Approaches

• Borning, A. et al. 2005. Informing Public Deliberation: Value Sensitive Design of Indicators for a Large-Scale Urban Simulation, In: Gellersen, H., Schmidt, K., Beaudouin-Lafon, M., Mackay, W. (eds) ECSCW 2005. Springer, Dordrecht.
• Borning, A., & Muller, M. (2012). Next steps for value sensitive design. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1125-1134).
• Friedman, B. (1996). Value-sensitive design. ACM Interactions, 3 (6), 17-23.
• Friedman, B. and Hendry, D. 2012. The envisioning cards: a toolkit for catalyzing humanistic and technical imaginations. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '12). Association for Computing Machinery, New York, NY, USA, 1145-1148.
• Friedman, B., & Hendry, D. G. (2019). Value sensitive design: Shaping technology with moral imagination. MIT Press.
• Friedman, B., Hendry, D. G., Borning, A., et al. (2017). A survey of value sensitive design methods. Foundations and Trends in Human-Computer Interaction , 11 (2), 63-125.
• Friedman, B., & Kahn Jr., P. H. (2008). Human values, ethics, and design. In J. A. Jacko & A. Sears (Eds.), The human-computer interaction handbook (Revised second ed., pp. 1241-1266). Mahwah, NJ.
• Jacobs, N and Huldtgren, A. 2021. Why value sensitive design needs ethical commitments Ethics and Information Technology 23.
• Leidner, J. L., & Plachouras, V. (2017). Ethical by design: Ethics best practices for natural language processing. In Proceedings of the first ACL workshop on ethics in natural language processing (pp. 30-40). Valencia, Spain: Association for Computational Linguistics.
• Miller, J. et al. 2007. Value Tensions in Design: The Value Sensitive Design, Development, and Appropriation of a Corporation's Groupware System, GROUP'07, November 4-7, 2007, Sanibel Island, Florida, USA.
• Nathan, L. P., Klasnja, P. V., & Friedman, B. (2007). Value scenarios: a technique for envisioning systemic effects of new technologies. In CHIགྷ extended abstracts on human factors in computing systems (pp. 2585-2590).
• Schnoebelen, T. (2017). Goal-oriented design for ethical machine learning and NLP. In Proceedings of the first ACL workshop on ethics in natural language processing (pp. 88-93). Valencia, Spain: Association for Computational Linguistics.
• Spiekermann, S. & Winkler, T. 2021. Twenty years of value sensitive design: a review of methodological practices in VSD projects Ethics and Information Technology 23.
• Umbrello, S and van de Poel, I. 2021. Mapping value sensitive design onto AI for social good principles AI and Ethics 1.
• Yoo, D. 2021. Stakeholder Tokens: a constructive method for value sensitive design stakeholder analysis Ethics and Information Technology 23.
• Young, M., Magassa, L., & Friedman, B. (2019). Toward inclusive tech policy design: A method for underrepresented voices to strengthen tech policy documents. Ethics and Information Technology, 21(2), 89-103.

Documentation and Transparency

• Akbari, A. 2024 The politics of data justice: exit, voice, or rehumanisation?, Information, Communication, and Society.
• Arnold, Matthew, Rachel K. E. Bellamy, Michael Hind, Stephanie Houde, Sameep Mehta, Aleksandra Mojsilović, Ravi Nair, Karthikeyan Natesan Ramamurthy, Alexandra Olteanu, David Piorkowski, Darrell Reimer, John Richards, Jason Tsay, and Kush R. Varshney. 2019. FactSheets: Increasing trust in AI services through supplier’s declarations of conformity. IBM Journal of Research and Development 63, 4/5 (2019), 6:1-6:13.
• Bender, E. M., & Friedman, B. (2018). Data statements for natural language processing: Toward mitigating system bias and enabling better science. Transactions of the Association for Computational Linguistics, 6, 587-604.
• Bender, E. M., Friedman, B. and McMillan-Major, A. (2021). A Guide for Writing Data Statements for Natural Language Processing.
• Birhane, A., Prabhu, V.U., and Kahembwe, E. (2021). Multimodal datasets: misogyny, pornography, and malignant stereotypes
• Birhane, A., Han, S., Boddeti, V., & Luccioni, S. (2023). Into the LAION’s Den: Investigating Hate in Multimodal Datasets. In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track.
• Costanza-Chock, Sasha, Raji, Inioluwa Deborah and Buolamwini, Joy. (2022). Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem, FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, p.1571–1583
• Couillault, A. et al. 2014. Evaluating Corpora Documentation with regards to the Ethics and Big Data Charter. LREC 2014.
• Crisan, Anamaria, Margaret Drouhard, Jesse Vig, and Nazneen Rajani. 2022. Interactive Model Cards: A Human-Centered Approach to Model Documentation. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22). Association for Computing Machinery, New York, NY, USA, 427–439.
• Dodge et al. 2021. Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus EMNLP 2021.
• Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., and Crawford, K. (2018). Datasheets for datasets. Proceedings of the 5th Workshop on Fairness, Accountability, and Transparency in Machine Learning.
• Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., and Crawford K. (2021). Datasheets for datasets. CACM 64(12):86-92.
• Holland, S., Hosny, A., Newman, S., Joseph, J., & Chmielinski, K. (2018). The dataset nutrition label: A framework to drive higher data quality standards.
• Jo, E. S. and Gebru, T. 2020. Lessons from archives: strategies for collecting sociocultural data in machine learning. FAT* ཐ.
• Koch, B. et al. 2021. Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research, NeurIPS 2021.
• Markl, N. 2022. Mind the data gap(s): Investigating power in speech and language datasets, Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion.
• McMillan-Major, Angelina, Emily M. Bender and Batya Friedman. 2023. Data Statements: From Technical Concept to Community Practice, ACM Journal on Responsible Computing.
• McMillan-Major, Angelina, Salomey Osei, Juan Diego Rodriguez, Pawan Sasanka Ammanamanchi, Sebastian Gehrmann, Yacine Jernite. (2021). Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards. Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), pages 121–135, Online. Association for Computational Linguistics.
• Mieskes, M. (2017, April). A quantitative study of data in the NLP community. In Proceedings of the first ACL workshop on ethics in natural language processing (pp. 23-29). Valencia, Spain: Association for Computational Linguistics.
• Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., et al. (2019). Model cards for model reporting. In Proceedings of the conference on fairness, accountability, and transparency (pp. 220-229). New York, NY, USA: ACM.
• Moss, E. et al. 2021. Assembling Accountability: Algorithmic Impact Assessment for the Public Interest
• Partnership on AI. (2019). ABOUT-ML: Annotation and benchmarking on understanding and transparency of machine learning lifecycles (ABOUT ML).
• Stoyanovich, Julia and Bill Howe. 2019. Nutritional labels for data and models. A Quarterly bulletin of the Computer Society of the IEEE Technical Committee on Data Engineering 42, 3 (2019).
• White House (Biden Administration). (2023). Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Search specifically for requirements about data and model dcoumentation.

Other Best Practices

• Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. CoRR, abs/1606.06565.
• G&ocaute;mez, Marcos J., Juli´n Dabbah, and Luciana Benotti. 2024. A workshop on artificial intelligence biases and its effect on high school students' perceptions, International Journal of Child-Computer Interaction
• Kulynych, B. et al. 2020. POTs: protective optimization technologies. FAT* 2020.
• Markham, A. (2012). Fabrication as ethical practice: Qualitative inquiry in ambiguous Internet contexts. Information, Communication & Society, 15(3), 334-353.
• Ratto, M. (2011). Critical making: Conceptual and material studies in technology and social life. The Information Society, 27 (4), 252-260.
• Russell, S., Dewey, D., & Tegmark, M. (2015). Research priorities for robust and benefcial artifcial intelligence. AI Magainze.
• Shilton, K., & Anderson, S. (2016). Blended, not bossy: Ethics roles, responsibilities and expertise in design. Interacting with Computers.
• Shilton, K., & Sayles, S. (2016). "We aren't all going to be on the same page about ethics": Ethical practices and challenges in research on digital and social media. In 2016 49th Hawaii international conference on system sciences (HICSS) (pp. 1909-1918).
• Tanksley, Tiera Chante. 2024. "We’re changing the system with this one": Black students using critical race algorithmic literacies to subvert and survive AI-mediated racism in school. English Teaching: Practice & Critique

Bias/Discrimination

• Antoniak and Mimno. 2021. Bad Seeds: Evaluating Lexical Methods for Bias Measurement ACL 2021.
• Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜. In Proceedings of FAccT 2021, pp.610-623.
• Blodgett, S. et al. 2020. Language (Technology) is Power: A Critical Survey of "Bias" in NLP ACL 2020.
• Blodgett, S. et al. 2021. Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets ACL 2021.
• Bolukbasi, T., Chang, K.-W., Zou, J. Y., Saligrama, V., & Kalai, A. T. (2016). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, & R. Garnett (Eds.), Advances in neural information processing systems 29 (pp. 4349-4357). Curran Associates, Inc.
• Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora necessarily contain human biases. Science.
• Caliskan, A., Bryson, J., & Narayanan, A. (2016). A story of discrimination and unfairness. (Talk presented at HotPETS 2016)
• Dancy, Christopher L. and Saucier, P. Khalil. 2021. AI and Blackness: Toward Moving Beyond Bias and Representation IEEE Transactions on Technology and Society
• Davani, A.M. et al. 2021. Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations, to appear in TACL.
• Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems (TOIS), 14(3), 330-347.
• Ganesh, M. and Moss, E. 2022. Resistance and refusal to algorithmic harms: Varieties of 'knowledge projects' Media International Australia
• Garg, N., Schiebinger, L., Jurafsky, D., & Zou, J. (2018). Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences , 115 (16), E3635-E3644.
• Goldfarb-Tarrant, S., et al. 2021. Intrinsic Bias Metrics Do Not Correlate with Application Bias. ACL 2021.
• Gonen, H., & Goldberg, Y. (2019). Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them. In Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: Human language technologies, volume 1 (long and short papers) (pp. 609-614). Minneapolis, Minnesota: Association for Computational Linguistics.
• Guynn, J. (Jun 10, 2016). `Three black teenagers' Google search sparks outrage. USA Today.
• Herbelot, A., Redecker, E. von, & Müller, J. (2012). Distributional techniques for philosophical enquiry. In Proceedings of the 6th workshop on language technology for cultural heritage, social sciences, and humanities (pp. 45-54). Avignon, France: Association for Computational Linguistics.
• Hovy, D. and Prabhumoye, S. 2021. Five sources of bias in natural language processing
• Liang, P. et al. 2021. Towards Understanding and Mitigating Social Biases in Language Models Proceedings of the 38 th International Conference on Machine Learning, PMLR 139, 2021.
• Manzini, T., Yao Chong, L., Black, A. W., & Tsvetkov, Y. (2019). Black is to criminal as caucasian is to police: Detecting and removing multiclass bias in word embeddings. In Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: Human language technologies, volume 1 (long and short papers) (pp. 615-621). Minneapolis, Minnesota: Association for Computational Linguistics.
• Milagros, M. et al. 2022. Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?, CHI 2022.
• Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
• Rogers, A. 2021. Changing the World by Changing the Data. ACL 2021.
• Rudinger, R., May, C., & Van Durme, B. (2017). Social bias in elicited natural language inferences. In Proceedings of the first ACL workshop on ethics in natural language processing (pp. 74-79). Valencia, Spain: Association for Computational Linguistics.
• Schluter, N. (2018). The word analogy testing caveat. In Proceedings of the 2018 conference of the north American chapter of the association for computational linguistics: Human language technologies, volume 2 (short papers) (pp. 242-246). New Orleans, Louisiana: Association for Computational Linguistics.
• Schmidt, B. (2015). Rejecting the gender binary: A vector-space operation. (Blog post, accessed 12/29/16)
• Shen, J.H. et al. 2018. Darling or Babygirl? Investigating Stylistic Bias in Sentiment Analysis FATML 2018.
• Sweeney, L. (May 1, 2013). Discrimination in online ad delivery. Communications of the ACM, 56 (5), 44-54.
• Talat, Z. et al. 2021. Disembodied Machine Learning: On the Illusion of Objectivity in NLP.
• Xu, A. et al. 2021. Detoxifying Language Models Risks Marginalizing Minority Voices. NAACL 2021.

Fairness

• Hardt, M. (Sep 26, 2014). How big data is unfair: Understanding sources of unfairness in data driven decision making. Medium.
• Rao, D. (n.d.). Fairness in machine learning. (slides)
• Zliobaite, I. (2015). On the relation between accuracy and fairness in binary classification. CoRR, abs/1505.05723.

Other resources on bias

• Angwin, J., & Larson, J. (Dec 30, 2016). Bias in criminal risk scores is mathematically inevitable, researchers say. ProPublica.
• boyd, d. (2015). What world are we building? (Everett C Parker Lecture. Washington, DC, October 20)
• Brennan, M. (2015). Can computers be racist? big data, inequality, and discrimination. (online; Ford Foundation)
• Clark, J. (Jun 23, 2016). Artificial intelligence has a `sea of dudes' problem. Bloomberg Technology.
• Crawford, K. (Apr 1, 2013). The hidden biases in big data. Harvard Business Review.
• Daumé III, H. (Nov 8, 2016). Bias in ML, and teaching AI. (Blog post, accessed
• Larson, J., Angwin, J., & Parris Jr., T. (Oct 19, 2016). Breaking the black box: How machines learn to be racist. ProPublica. 1/17/17)
• Emspak, J. (Dec 29, 2016). How a machine learns prejudice: Artificial intelligence picks up bias from human creators--not from hard, cold logic. Scientific American.
• Jacob. (May 8, 2016). Deep learning racial bias: The avenue Q theory of ubiquitous racism. Medium.

More papers in the Proceedings of the First Workshop on Gender Bias in Natural Language Processing

Demographic variables

• Ahmen, A. 2018. Trans Competent Interaction Design: A Qualitative Study on Voice, Identity, and Technology. Interacting with Computers
• Field, A. et al. 2021. A Survey of Race, Racism, and Anti-Racism in NLP ACL 2021.
• Larson, B. (2017). Gender as a variable in natural-language processing: Ethical considerations. In Proceedings of the first ACL workshop on ethics in natural language processing (pp. 1-11). Valencia, Spain: Association for Computational Linguistics.
• Morgan Klaus Scheuerman, Katta Spiel, Oliver L. Haimson, Foad Hamidi, Stacy M. Branham. 2020. HCI Guidelines for Gender Equity and Inclusivity

Chatbots

• Abercrombie, G. et al. 2021. Alexa, Google, Siri: What are Your Pronouns? Gender and Anthropomorphism in the Design and Perception of Conversational Assistants Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing.
• Abercrombie, Gavin, Amanda Cercas Curry, Tanvi Dinkar, Verena Rieser, Zeerak Talat. 2023. Mirages. On Anthropomorphism in Dialogue Systems. EMNLP 2023.
• Cercas Curry, A., & Rieser, V. (2018). #MeToo Alexa: How conversational systems respond to sexual harassment. In Proceedings of the second ACL workshop on ethics in natural language processing (pp. 7-14). New Orleans, Louisiana, USA: Association for Computational Linguistics.
• Dinan, E. et al. 2021. Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling
• Elder, A. (2019). Conversation from beyond the grave? A neo-Confucian ethics of chatbots of the dead. Journal of Applied Philosophy.
• Erscoi, L. et al. 2023. Pygmalion Displacement: When Humanising AI Dehumanises Women.
• Fessler, Leah. (Feb 22, 2017). SIRI, DEFINE PATRIARCHY: We tested bots like Siri and Alexa to see who would stand up to sexual harassment. Quartz.
• Fung, P. (Dec 3, 2015). Can robots slay sexism? World Economic Forum.
• Hussain, Hera. 2023. Why Chayn took down its chatbot in 2020 and what we’ve learned about building culturally-aware chatbots since
• Mott, N. (Jun 8, 2016). Why you should think twice before spilling your guts to a chatbot. Passcode.
• Lee, N. et al. 2019. Exploring Social Bias in Chatbots using Stereotype Knowledge. Proceedings of the 2019 Workshop on Widening NLP.
• Paolino, J. (Jan 4, 2017). Google home vs Alexa: Two simple user experience design gestures that delighted a female user. Medium.
• Roach, Rebecca. 2024. My search for the mysterious missing secretary who shaped chatbot history The Conversation
• Seaman Cook, J. (Apr 8, 2016). From Siri to sexbots: Female AI reinforces a toxic desire for passive, agreeable and easily dominated women. Salon.
• Twitter. (Apr 7, 2016). Automation rules and best practices. (Web page, accessed 12/29/16)
• Yao, M. (n.d.). Can bots manipulate public opinion? (Web page, accessed 12/29/16)

Anthropomorphization

• Hayes, Patrick and Kenneth Ford. 1995. Turing Test Considered Harmful IJCAI
• Inie, Nanna, Stefania Durga, Peter Zukerman, and Emily M. Bender. 2024. From "AI" to Probabilistic Automation: How Does Anthropomorphization of Technical Systems Descriptions Influence Trust?, FAccT 24: The 2024 ACM Conference on Fairness, Accountability, and Transparency, pp.2322–2347
• Hunger, F. 2023. Unhype Artificial ‘Intelligence’! A proposal to replace the deceiving terminology of AI.
• Mullaney, T. 2024. Pedagogy And The AI Guest Speaker Or What Teachers Should Know About The Eliza Effect
• Reinecke, M. 2024. Humanizing Chatbots Is Hard To Resist — But Why?
• Reinecke, M. et al. 2024. The double-edged sword of anthropomorphism in LLMs. Adaptive Education: Harnessing AI for Academic Progress '24
• Winograd, Terry. 1980. What Does It Mean to Understand Language? Cognitive Science 4, 209-241.

Privacy

• Resources from Tech Policy Press on privacy and the European GDPR
• Burgess, M. 2023. ChatGPT Has a Big Privacy Problem WIRED.
• Robertson, A. 2023. ChatGPT returns to Italy after ban, VentureBeat.
• UK Information Commissioner's Office (2023) Guidance on Privacy-enhancing technologies (PETs)
• ACLU of Illinois. nd. Episode 16: Protecting Your Biometric Information.

• Abadi, M., Chu, A., Goodfellow, I., Brendan McMahan, H., Mironov, I., Talwar, K., et al. (2016). Deep Learning with Differential Privacy. ArXiv e-prints.
• Amazon.com. 2017. Memorandum of Law in Support of Amazon's Motion to Quash Search Warrant
• Asher-Schapiro, A. and Sherfinski, D. 2021. INSIGHT-AI surveillance takes U.S. prisons by storm Thomson Reuters Foundation News. November 16, 2021.
• Brant, T. (Dec 27, 2016). Amazon Alexa data wanted in murder investigation. PC Mag.
• Carlini, N. et al. 2020. Extracting Training Data from Large Language Models.
• Friedman, B., Kahn Jr, P. H., Hagman, J., Severson, R. L., & Gill, B. (2006). The watcher and the watched: Social judgments about privacy in a public place. Human-Computer Interaction, 21(2), 235-272.
• Golbeck, J., & Mauriello, M. L. (2016). User perception of facebook app data access: A comparison of methods and privacy concerns. Future Internet, 8(2), 9.
• Grissom II, A. (2019). Thinking about how NLP is used to serve power: Current and future trends. /Presentation at Widening NLP 2019. [Slides] [Video]
• Lewis, D., Moorkens, J., & Fatema, K. (2017). Integrating the management of personal data protection and open science with research ethics. In Proceedings of the first ACL workshop on ethics in natural language processing (pp. 60-65). Valencia, Spain: Association for Computational Linguistics.
• Narayanan, A., & Shmatikov, V. (2010). Myths and fallacies of "personally identifiable information". Communications of the ACM, 53 (6), 24-26.
• Nissenbaum, H. (2009). Privacy in context: Technology, policy, and the integrity of social life. Stanford: Stanford University Press.
• Shilton, K. et al. 2021. Excavating awareness and power in data science: A manifesto for trustworthy pervasive data research. Big Data & Society.
• Solove, D. J. (2007). 'I've got nothing to hide' and other misunderstandings of privacy. San Diego Law Review, 44 (4), 745-772.
• Steel, E., & Angwin, J. (Aug 4, 2010). On the Web's cutting edge, anonymity in name only. The Wall Street Journal.
• Tene, O., & Polonetsky, J. (2012). Big data for all: Privacy and user control in the age of analytics. Northwestern Journal of Technology and Intellectual Property, 11(45), 239-273.
• Vitak, J., Shilton, K., & Ashktorab, Z. (2016). Beyond the Belmont principles: Ethical challenges, practices, and beliefs in the online data research community. In Proceedings of the 19th ACM conference on computer-supported cooperative work & social computing (pp. 941-953).

Social Media

• Association of Internet Researchers. 2019. Internet Research: Ethical Guidelines 3.0
• Dym, B. and Fiesler, C. 2020. Ethical and privacy considerations for research using online fandom data
• Fiesler, C. and Proferes, N. 2018. "Participant" Perceptions of Twitter Research Ethics
• Hallinan, B., Brubaker, J. R., & Fiesler, C. (2019). Unexpected expectations: Public reaction to the facebook emotional contagion study. New Media & Society, 1-19. [Tweet thread]
• Metcalf, J., & Crawford, K. (2016). Where are human subjects in big data research? The emerging ethics divide.Big Data & Society 3(1).
• Roose, K. 2021. Inside Facebook's Data Wars. The New York Times July 14, 2021.
• Shilton, K., & Sayles, S. (2016). ``We aren't all going to be on the same page about ethics'': Ethical practices and challenges in research on digital and social media. In 2016 49th Hawaii international conference on system sciences (HICSS) (pp. 1909-1918).
• Townsend, L., & Wallace, C. (2015). Social media research: A guide to ethics. The University of Aberdeen.
• Tufekci, Z. 2014. Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls Eighth International AAAI Conference on Weblogs and Social Media.
• Williams, M. L., Burnap, P., & Sloan, L. (2017). Towards an ethical framework for publishing twitter data in social research: Taking into account users views, online context and algorithmic estimation. Sociology, 51 (6), 1149-1168.
• Woodfield, K. (Ed.). (2017). The ethics of online research. Emerald Publishing Limited.
  • See especially Chs 2, 5, 7 and 8

Content moderation/Toxicity detection

• Abercrombie, Gavin, Aiqi Jiang, Poppy Gerrard-abbott, Ioannis Konstas, and Verena Rieser. 2023. Resources for Automated Identification of Online Gender-Based Violence: A Systematic Review. In The 7th Workshop on Online Abuse and Harms (WOAH), pages 170–186, Toronto, Canada. Association for Computational Linguistics.
• Ahrin et al. 2021. Ground-Truth, Whose Truth? - Examining the Challenges with Annotating Toxic Text Datasets, NeurIPS 2021.
• Diaz, Mark, Razvan Amironesei, Laura Weidinger, and Iason Gabriel. 2022. Accounting for Offensive Speech as a Practice of Resistance. In Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH), pages 192–202, Seattle, Washington (Hybrid). Association for Computational Linguistics.
• Dwoskin et al, 2019 Content moderators at YouTube, Facebook and Twitter see the worst of the web — and suffer silently, Washington Post July 25, 2019.
• Jiang, J.A., Scheuerman, M.K., Fiesler, C. and Brubaker, J.R. 2021 Understanding international perceptions of the severity of harmful content online
• Jones, L. 2020. Twitter wants you to know that you're still SOL if you get a death threat -- unless you're President Donald Trump
• Gehman, S. et al. 2020. RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models. Findings of EMNLP.
• Hao, K. and Seetharaman, D. 2023. Cleaning Up ChatGPT Takes Heavy Toll on Human Workers Wall Street Journal. (Podcasts embedded.)
• Kirk, Hannah, Wenjie Yin, Bertie Vidgen, and Paul Röttger. 2023. SemEval-2023 Task 10: Explainable Detection of Online Sexism. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 2193–2210, Toronto, Canada. Association for Computational Linguistics.
• Marshall, B. 2021. Algorithmic misogynoir in content moderation practice
• Perrigo, B. 2023. Exclusive: OpenAI Used Kenyan Workers on Less Than $2 Per Hour to Make ChatGPT Less Toxic, TIME.
• Röttger, P. et al. 2021. HateCheck: Functional Tests for Hate Speech Detection Models. ACL 2021.
• Saitov, K. and Derczynski, L. 2021. Abusive Language Recognition in Russian. Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing.
• Sap, M. et al. 2019. The Risk of Racial Bias in Hate Speech Detection ACL 2019.
• Singh, S., Haridasan, A., & Mooney, R. (2023). “Female Astronaut: Because sandwiches won’t make themselves up there”: Towards Multimodal misogyny detection in memes. In The 7th Workshop on Online Abuse and Harms (WOAH) (pp. 150-159).
• Sigurbergsson, G.I. and Derczynski, L. 2020. Offensive Language and Hate Speech Detection for Danish. Proceedings of the 12th Language Resources and Evaluation Conference.
• Simonite, Tom. 2021. Facebook Is Everywhere; Its Moderation Is Nowhere Close. WIRED
• Talat, Z. et al. 2017. Understanding Abuse: A Typology of Abusive Language Detection Subtasks. ACL 2017.
• Yoder, Michael, Chloe Perry, David Brown, Kathleen Carley, and Meredith Pruden. 2023. Identity Construction in a Misogynist Incels Forum. In The 7th Workshop on Online Abuse and Harms (WOAH), pages 1–13, Toronto, Canada. Association for Computational Linguistics.
• Zampieri, Marcos, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, and Ritesh Kumar. 2019. Predicting the Type and Target of Offensive Posts in Social Media. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1415–1420, Minneapolis, Minnesota. Association for Computational Linguistics.
• Zeinert, P., Inie, N. and Derczynski, L. 2021. Annotating Online Misogyny. ACL 2021.

See also the papers in the Proceedings Workshop on Online Abuse and Harms: WOAH 2021, WOAH 2022, WOAH 2023

Crowdsourcing and labor conditions

• Bederson, B. B., & Quinn, A. J. (2011). Web workers unite! Addressing challenges of online laborers. In CHIཇ extended abstracts on human factors in computing systems (pp. 97-106).
• Callison-Burch, C. (2016). Crowd workers. (Slides from Crowdsoucing and Human Computation, accessed online 12/30/16)
• Callison-Burch, C. (2016). Ethics of crowdsourcing. (Slides from Crowdsoucing and Human Computation, accessed online 12/30/16)
• Dinika, Adio-Adet, Milagros Miceli, Krystal Kauffman. 2024. Data workers' inquiry
• Fort, K., Adda, G., & Cohen, K. B. (2011). Amazon mechanical turk: Gold mine or coal mine? Computational Linguistics, 37 (2), 413-420.
• Irani, L. and Silberman, M.S. 2013. Turkopticon: interrupting worker invisibility in Amazon Mechanical Turk CHI 2013.
• Kummerfeld, J.K. 2021. Quantifying and Avoiding Unfair Qualification Labour in Crowdsourcing. ACL 2021.
• Lefeuvre-Halftermeyer, A.; Govaere, V.; Antoine, J.-Y.; Allegre, W.; Pouplin, S.; Departe, J.-P.; Slimani, S. & Spagnulo, A. Typologie des risques pour une analyse éthique de l'impact des technologies du TAL Revue TAL, ATALA (Association pour le Traitement Automatique des Langues), 2016, 57, 47-71
• Shmueli et al. 2021. Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing. NAACL 2021.
• Snyder, J. (2010). Exploitation and sweatshop labor: Perspectives and issues. Business Ethics Quarterly, 20 (2), 187-213.

Language Variation and Emergent Bias

• Bender, E.M. 2019. The #BenderRule: On Naming the Languages We Study and Why It Matters [audio paper] [works cited]
• Broussard, M. (10 May 2018). Agenda: Why the scots are such a struggle for Alexa and Siri. The Herald.
• Garimella, A., Banea, C., Hovy, D., & Mihalcea, R. (2019). Women's syntactic resilience and men's grammatical luck: Gender-bias in part-of-speech tagging and dependency parsing. In Proceedings of the 57th annual meeting of the association for computational linguistics (pp. 3493-3498). Florence, Italy: Association for Computational Linguistics.
• Mengqing Guo, Jiali Li, Jishun Zhao, Shucheng Zhu, Ying Liu, and Pengyuan Liu. 2022. 中文自然语言处理多任务中的职业性别偏见测量(Measurement of Occupational Gender Bias in Chinese Natural Language Processing Tasks). In Proceedings of the 21st Chinese National Conference on Computational Linguistics, pages 510–522, Nanchang, China. Chinese Information Processing Society of China.
• Hovy, D., & Søgaard, A. (2015). Tagging performance correlates with author age. In Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th international joint conference on natural language processing (volume 2: Short papers) (pp. 483-488). Beijing, China: Association for Computational Linguistics.
• Huang, X., & Paul, M. J. (2019). Neural user factor adaptation for text classification: Learning to generalize across author demographics. In Proceedings of the eighth joint conference on lexical and computational semantics (*SEM 2019) (pp. 136-146). Minneapolis, Minnesota: Association for Computational Linguistics.
• Jørgensen, A., Hovy, D., & Søgaard, A. (2015). Challenges of studying and processing dialects in social media. In Proceedings of the workshop on noisy user-generated text (pp. 9-18). Beijing, China: Association for Computational Linguistics.
• Joshi, P. et al. 2020. The State and Fate of Linguistic Diversity and Inclusion in the NLP World ACL 2020.
• Jurgens, D., Tsvetkov, Y., & Jurafsky, D. (2017). Incorporating dialectal variability for socially equitable language identification. In Proceedings of the 55th annual meeting of the association for computational linguistics (volume 2: Short papers) (pp. 51-57). Vancouver, Canada: Association for Computational Linguistics.
• Koenecke, A. et al. 2020. Racial disparities in automated speech recognition
• Markl and Lai. 2021. Context-sensitive evaluation of automatic speech recognition: considering user experience & language variation
• Tatman, R. (2017). Gender and dialect bias in YouTube's automatic captions. In Proceedings of the first ACL workshop on ethics in natural language processing (pp. 53-59). Valencia, Spain: Association for Computational Linguistics.
• Wassink, A., Gansen, C., and Bartholomew, I. 2022. Uneven success: Automatic speech recognition and ethnicity-related dialects. Speech Communication 140:50-70. (Video of related talk; [slides])

Translation Technologies

• Bar-Hillel, Y. (1960). A Demonstration of the Nonfeasibility of Fully Automatic High Quality Translation (Appendix III). In The present status of automatic translation of languages, (Vol. 1, pp. 158-163).
• Bawden, R. (2018). Going beyond the sentence: Contextual machine translation of dialogue (Doctoral dissertation, Université Paris-Saclay (ComUE)).
• Boitet, C. (1988). PROs and CONs of the pivot and transfer approaches in multilingual Machine Translation. Readings in machine translation, 273-279.
• Braun, S. (2019). Technology and interpreting. Routledge Handbook of Translation and Technology, Routledge, London, 271-288.
• Hatim, B., & Munday, J. (2004). Translation an advanced resource book Routledge.
• Hutchins, J. (2003). ALPAC: the (in) famous report. Readings in machine translation, 14, 131-135.
• Kenny, Dorothy, Joss Moorkens, and Félix Do Carmo. Fair MT: Towards ethical, sustainable machine translation. Translation Spaces 9.1 (2020): 1-11.
• Lehman-Wilzig, S. (2017). Babel and babble: Autonomous, algorithmic, simultaneous translation systems in the glocal village -- consequences & paradoxical outcomes. In S. Brunn & R. Kehrein (Eds.), The Changing World Language Map. New York: Springer Publishing.
• Liu, Lydia H., ليديا ﻫ. ليو, James St. André, جيمس سانت أندريه (2018). The Battleground of Translation: Making Equal in a Global Structure of Inequality / الترجمة في المعترك : البحث عن المساواة في سياق اللامساواة العالمي Alif: Journal of Comparative Poetics, 38, 368–387. http://www.jstor.org/stable/26496380.
• Paullada, Amandalynne. 2021. Machine Translation Shifts Power. The Gradient.
• Ramati, I., & Pinchevski, A. (2017). Uniform multilingualism: A media genealogy of Google Translate. New Media & Society, 1461444817726951.
• Savoldi, Beatrice, Marco Gaido, Luisa Bentivogli, Matteo Negri, Marco Turchi. 2021. Gender Bias in Machine Translation. Transactions of the Association for Computational Linguistics 9:845-874.

Biomedical NLP, Mental Health and Social Media

• Andrade, N. N. Gomes de, Pawson, D., Muriello, D., Donahue, L., & Guadagno, J. (2018, Dec 01). Ethics and artificial intelligence: Suicide prevention on Facebook. Philosophy & Technology, 31(4), 669-684.
• Barnett, I., & Torous, J. (2019, 04). Ethics, Transparency, and Public Health at the Intersection of Innovation and Facebook's Suicide Prevention Efforts. Annals of Internal Medicine, 170 (8), 565-566.
• Benton, A., Coppersmith, G., & Dredze, M. (2017). Ethical research protocols for social media health research. In Proceedings of the first ACL workshop on ethics in natural language processing (pp. 94-102). Valencia, Spain: Association for Computational Linguistics.
• Elder, A. (2019). Conversation from beyond the grave? A neo-Confucian ethics of chatbots of the dead. Journal of Applied Philosophy.
• Linthicum, K. P., Schafer, K. M., & Ribeiro, J. D. (2019). Machine learning in suicide science: Applications and ethics. Behavioral sciences & the law , 37 (3), 214-222.
• McKernan, L. C., Clayton, E. W., & Walsh, C. G. (2018). Protecting life while preserving liberty: Ethical recommendations for suicide prevention with artificial intelligence. Frontiers in Psychiatry, 9.
• Suster, S., Tulkens, S., & Daelemans, W. (2017, April). A short review of ethical challenges in clinical natural language processing. In Proceedings of the first ACL workshop on ethics in natural language processing (pp. 80-87). Valencia, Spain: Association for Computational Linguistics.
• Tucker, R. P., Tackett, M. J., Glickman, D., & Reger, M. A. (2019). Ethical and practical considerations in the use of a predictive model to trigger suicide prevention interventions in healthcare settings. Suicide and Life-Threatening Behavior , 49 (2), 382-392.

NLP Apps Addressing Ethical Issues/NLP for Social Good

• Demszky, D., Garg, N., Voigt, R., Zou, J., Shapiro, J., Gentzkow, M., et al. (2019). Analyzing polarization in social media: Method and application to tweets on 21 mass shootings. In Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: Human language technologies, volume 1 (long and short papers) (pp. 2970-3005). Minneapolis, Minnesota: Association for Computational Linguistics.
• Fokkens, A. (2016). Reading between the lines. (Slides presented at Language Analysis Portal Launch event, University of Oslo, Sept 2016)
• Jurgens, D., Hemphill, L., & Chandrasekharan, E. (2019). A just and comprehensive strategy for using NLP to address online abuse. In Proceedings of the 57th annual meeting of the association for computational linguistics (pp. 3658-3666). Florence, Italy: Association for Computational Linguistics.
• Lee, N., Bang, Y., Shin, J., & Fung, P. (2019). Understanding the shades of sexism in popular TV series. In Proceedings of the 2019 workshop on widening NLP (pp. 122-125). Florence, Italy: Association for Computational Linguistics.
• Gershgorn, D. (Feb 27, 2017). NOT THERE YET: Alphabet's hate-fighting AI doesn't understand hate yet. Quartz.
• Google.com. (2017). The women missing from the silver screen and the technology used to find them. Blog post, accessed March 1, 2017.
• Greenberg, A. (2016). Inside Google's Internet Justice League and Its AI-Powered War on Trolls. Wired.
• Kellion, L. (Mar 1, 2017) Facebook artificial intelligence spots suicidal users. BBC News.
• Madnani, N., Loukina, A., Davier, A. von, Burstein, J., & Cahill, A. (2017). Building better open-source tools to support fairness in automated scoring. In Proceedings of the first ACL workshop on ethics in natural language processing (pp. 41-52). Valencia, Spain: Association for Computational Linguistics.
• Munger, K. (2016). Tweetment effects on the tweeted: Experimentally reducing racist harassment. Political Behavior, 1-21.
• Munger, K. (Nov 17, 2016). This researcher programmed bots to fight racism on twitter. It worked. Washington Post.
• Murgia, M. (Feb 23, 2017). Google launches robo-tool to flag hate speech online. Financial Times.
• The times is partnering with jigsaw to expand comment capabilities. (Sep 20, 2016). The New York Times.
• Qian, J., ElSherief, M., Belding, E., & Wang, W. Y. (2019). Learning to decipher hate symbols. In Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: Human language technologies, volume 1 (long and short papers) (pp. 3006-3015). Minneapolis, Minnesota: Association for Computational Linguistics.
• Waseem, Z. (2016). Are you a racist or am I seeing things? Annotator influence on hate speech detection on Twitter. In Proceedings of the first workshop on nlp and computational social science (pp. 138-142). Austin, Texas: Association for Computational Linguistics.
• Waseem, Z., & Hovy, D. (2016). Hateful symbols or hateful people? Predictive features for hate speech detection on Twitter. In Proceedings of the naacl student research workshop (pp. 88-93). San Diego, California: Association for Computational Linguistics.
• Wiegand, M., Ruppenhofer, J., & Kleinbauer, T. (2019). Detection of Abusive Language: the Problem of Biased Datasets. In Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: Human language technologies, volume 1 (long and short papers) (pp. 602-608). Minneapolis, Minnesota: Association for Computational Linguistics.

• Fake News Challenge
• Jigsaw Challenges
• Perspective (from Jigsaw)
  • But see: Hosseini, H, S. Kannan, B. Zhang and R. Poovendran. 2017. Deceiving Google's Perspective API Built for Detecting Toxic Comments. ArXiv.
• Textio See also:
  • CEO Kieran Snyder's posts on medium.com
  • Recording of Kieran Snyder's NLP Meetup talk from Aug 15, 2016

Other Issues in NLP: Carbon Emissions, Generation, ...

• Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8).
• Seabrook, J. (2019). The next word: Where will predictive text take us? The New Yorker, 14 Oct 2019.
• Parra Escartín, C., Reijers, W., Lynn, T., Moorkens, J., Way, A., & Liu, C.-H. (2017). Ethical considerations in NLP shared tasks. In Proceedings of the first ACL workshop on ethics in natural language processing (pp. 66-73). Valencia, Spain: Association for Computational Linguistics.
• Smiley, C., Schilder, F., Plachouras, V., & Leidner, J. L. (2017). Say the right thing right: Ethics issues in natural language generation systems. In Proceedings of the first ACL workshop on ethics in natural language processing (pp. 103-108). Valencia, Spain: Association for Computational Linguistics.
• Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th annual meeting of the association for computational linguistics (pp. 3645-3650). Florence, Italy: Association for Computational Linguistics.
• Šuster, S., Tulkens, S., & Daelemans, W. (2017). A short review of ethical challenges in clinical natural language processing. In Proceedings of the first ACL workshop on ethics in natural language processing (pp. 80-87). Valencia, Spain: Association for Computational Linguistics.
• Torbati, Y. (Sept, 2019). Google says Google Translate can't replace human translators. Immigration officials have used it to vet refugees. ProPublica.
• Vincent, J. (Feb, 2019). AI researchers debate the ethics of sharing potentially harmful programs. The Verge.

SciComm and Ethics Education

• Burns, T. W., O'Connor, D. J., & Stocklmayer, S. M. (2003). Science communication: A contemporary definition. Public Understanding of Science, 12(2), 183-202.
• Dai, J. 2020. THE PARADOX OF SOCIALLY RESPONSIBLE COMPUTING The limits and potential of teaching tech ethics
• Di Bari, M., & Gouthier, D. (2002). Tropes, science and communication. Journal of Communication, 2(1).
• Fiesler, C., Garrett, N., & Beard, N. (2020). What do we teach when we teach tech ethics?Links to an external site. A syllabi analysis. In Proceedings of the 51st ACM technical symposium on computer science education (pp. 289-295).
• Fischhoff, B. (2013). The sciences of science communication. Proceedings of the National Academy of Sciences, 110(Supplement 3), 14033-14039.
• Gero, K.I. et al. (2021). What Makes Tweetorials Tick: How Experts Communicate Complex Topics on Twitter, Proc. ACM Hum.-Comput. Interact. 5, CSCW2, Article 422.
• Mooney, C. (2010). Do scientists understand the public? American Academy of Arts & Sciences.
• Ngumbi, E. (2018, January 26). If you want to explain your science to the public, here's some advice. Scientific American.
• Phillips, C. M. L., & Beddoes, K. (2013). Really changing the conversation: The deficit model and public under-standing of engineering. In Proceedings of the 120th ASEE annual conference & exposition.
• Raji, Inioluwa Deborah, Morgan Klaus Scheuerman, and Razvan Amironesei. 2021. You Can't Sit With Us: Exclusionary Pedagogy in AI Ethics Education. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21). Association for Computing Machinery, New York, NY, USA, 515–525.
• Shepherd, M. (2016, November 22). 9 tips for communicating science to people who are not scientists. Forbes, 1-4.
• Simis, M. J., Madden, H., Cacciatore, M. A., & Yeo, S. K. (2016). The lure of rationality: Why does the deficit model persist in science communication? Public Understanding of Science, 25 (4), 400-414.
• Young, E. 2021. What Even Counts as Science Writing Anymore? The Atlantic.

• ACM Ethics Task Force. (2016). Code 2018 | ACM ethics. (Web page, accessed 1/5/17)
• The IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems. (2016). Ethically aligned design: A vision for prioritizing human wellbeing with artificial intelligence and autonomous systems (AI/AS) (Version 1 -- For Public Discussion).
• ACL Ethics FAQ. 2021. EMNLP version
• US FDA. 2021. Good Machine Learning Practice for Medical Device Development: Guiding Principles
• US FTC Office of Technology Blog -- look at entries from 2024 and January 2025.

• AI Bill of Rights (White House proposal)
• Algorithmic Accountability Act (2022; not passed)
• FTC Chair Khan and Officials from DOJ, CFPB and EEOC Release Joint Statement on AI
• White House (Biden Administration). (2023). Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Search specifically for requirements about data and model dcoumentation.
• NIST AI Risk Management Framework
• OMB Draft guidance on AI

• Ashurst, C. et al. 2020. A Guide to Writing the NeurIPS Impact Statement
• Ashurt, C. et al. 2022. AI Ethics Statements: Analysis and Lessons Learnt from NeurIPS Broader Impact Statements FAccT 2022.
• Jacqueline C. Charlesworth. 2024. Generative AI’s Illusory Case for Fair Use
• Daumé III, H. (Dec 12, 2016). Should the NLP and ML Communities have a Code of Ethics? (Blog post, accessed 12/30/16)
• Do et al. 2023. “That’s important, but...”: How Computer Science Researchers Anticipate Unintended Consequences of Their Research Innovations, CHI 2023.
• Etlinger, S., & Groopman, J. (2015). The trust imperative: A framework for ethical data use.
• Liu et al, 2022. Examining Responsibility and Deliberation in AI Impact Statements and Ethics Reviews, AIES 2022.
• Moran, C. 2021. Machine Learning, Ethics, and Open Source Licensing (Part II/II)
• Moura, Ian. 2024. To Regulate Artificial Intelligence Effectively, We Need to Confront Ableism, Tech Policy Press
• Nanayakkara, P. et al. 2021. Unpacking the Expressed Consequences of AI Research in Broader Impact Statements
• Patton, D. U. (16 April 2018). Ethical guidelines for social media research with vulnerable groups. Medium.
• Rakova et al. 2021. Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for Shifting Organizational Practices
• Schwartz et al. 2021. A Proposal for Identifying and Managing Bias in Artificial Intelligence
• Stix, C. 2021. Actionable Principles for Artificial Intelligence Policy: Three Pathways

Ethics Statements

Reading notes

• Schmaltz 2018 is a proposal around this practice. The other references here are examples of paper including an ethics statment. For this week, we'll be writing our own ethics statements, either for our own papers or for others we have selected.

Papers:

• Al-khazra ji, S., Berke, L., Kafle, S., Yeung, P., & Huenerfauth, M. (2018). Modeling the speed and timing of american sign language to generate realistic animations. In Proceedings of the 20th international ACM SIGACCESS conference on computers and accessibility (pp. 259-270). New York, NY, USA: ACM.
• Chen, H., Cai, D., Dai, W., Dai, Z., & Ding, Y. (2019). Charge-based prison term prediction with deep gating network. (To appear at EMNLP 2019)
• Schmaltz, A. (2018). On the utility of lay summaries and AI safety disclosures: Toward robust, open research oversight. In Proceedings of the second ACL workshop on ethics in natural language processing (pp. 1-6). New Orleans, Louisiana, USA: Association for Computational Linguistics.

Synthetic Media Machines

• Atkinson, David. 2025. Unfair Learning: GenAI Exceptionalism and Copyright Law
• Workers are worried about AI taking their jobs. Artists say it's already happening. Business Insider, October 1, 2023
• Bashir, D. (2022). AI literacy : Understanding shifts in our digital ecosystem New Degree Press.
• Beckett, C and M. Yaseen. n.d. Generating Change A global survey of what news organisations are doing with AI.
• Cuthbertson, Anthony. June 2023. ChatGPT ‘grandma exploit’ gives users free keys for Windows 11
• Friedman, Jane. 2023. I Would Rather See My Books Get Pirated Than This (Or: Why Goodreads and Amazon Are Becoming Dumpster Fires).
• Jason Koebler, Nov. 2023. Google Researchers’ Attack Prompts ChatGPT to Reveal Its Training Data
• Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions.
• Johann Rehberger, Nov. 2023. Hacking Google Bard - From Prompt Injection to Data Exfiltration
• Kirchenbauer, J., Geiping, J., Wen, Y., Katz, J., Miers, I., & Goldstein, T. (2023). A Watermark for Large Language Models. ICML.
• Lilian Weng, Oct. 2023. Adversarial Attacks on LLMs
• Lindemann, Nora. 2023. Sealed Knowledges: A Critical Approach to the Usage of LLMs as Search Engines AIES 2023.
• Naughton, John, Can AI-generated art be copyrighted? A US judge says not, but it’s just a matter of time, The Guardian, August 26, 2023.
• Read, Max. 2024. Drowning in Slop A thriving underground economy is clogging the internet with AI garbage — and it’s only going to get worse. New York Magazine
• Robertson, Adi, Who owns AI art?, The Verge, November 15, 2023.
• Schüller, K. (2022). Data and AI literacy for everyone. Statistical Journal of the IAOS, 38(2), 477–490.
• Shah, Chirag and Emily M. Bender. 2022. Situating Search CHIIR 2022.
• Shah, Chirag and Emily M. Bender. 2022. Envisioning Information Access Systems: What Makes for Good Tools and a Healthy Web? ACM Transactions on the Web.
• Shan, Shawn, Jenna Cryan, Emily Wenger, Haitao Zheng, Rana Hanocka, Ben Y. Zhao. 2023. Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models. Proceedings of USENIX Security Symposium.
• US Copyright Office. International Copyright Issues and Artificial Intelligence (Webinar)
• Young, Chloe and Stroud, Scott R. 2020. Can Artificial Intelligence Reprogram the Newsroom? Trust, Transparency, and Ethics in Automated Journalism
• Zachary Smalls, As Fight Over A.I. Artwork Unfolds, Judge Rejects Copyright Claim New York Times, Aug 21, 2023.

Other Readings

• Basile, V. nd. The Perspectivist Data Manifesto
• Cohen, K. B., Pestian, J., & Fort, K. (2015). Annotateurs volontaires investis et éthique de l'annotation de lettres de suicidés. In ETeRNAL (ethique et traitement automatique des langues).
• Dalzell, S. 2020. Federal Government used Google Translate for COVID-19 messaging aimed at multicultural communities
• Fort, K., & Couillault, A. (2016). Yes, we care! results of the ethics and natural language processing surveys. In Proceedings of the tenth international conference on language resources and evaluation (LREC 2016). Paris, France: European Language Resources Association (ELRA).
• Gillespie, T. (2014). The relevance of algorithms. In T. Gillespie, P. J. Boczkowski, & K. A. Foot (Eds.), Media technologies: Essays on communication, materiality, and society (pp. 167-194). MIT Press.
• Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. (Accessed online, 12/30/16)
  • Related blog post
• Kleinberg, J. M., Mullainathan, S., & Raghavan, M. (2016). Inherent trade-offs in the fair determination of risk scores. CoRR, abs/1609.05807.
• Metcalf, J., Keller, E. F., & boyd, d. (2016). Perspectives on big data, ethics, and society. (Accessed 12/30/16)
• Meyer, M. N. (2015). Two cheers for corporate experimentation: The A/B illusion and the virtues of data-driven innovation. Colo. Tech. L.J., 13, 273.
• Sloane et al. 2021. A Silicon Valley Love Triangle: Hiring Algorithms, Pseudo- Science, and the Quest for Auditability
• Wallach, H. (Dec 19, 2014). Big data, machine learning, and the social sciences: Fairness, accountability, and transparency. Medium.
• Wattenberg, M., Viégas, F., & Hardt, M. (Oct 7, 2016). Attacking discrimination with smarter machine learning.

Links

Conferences/Workshops

These links were last updated in 2020.

• ACM FAT* Conference 2020 Barcelona, Spain, January 2020
• ALW3: 3rd Workshop on Abusive Language Online at ACL 2019, Florence, Italy, August 2019
• 1st ACL Workshop on Gender Bias for Natural Language Processing at ACL 2019, Florence, Italy, August 2019
• ACM FAT* Conference 2019 Atlanta, GA, January 2019
• ALW2: 2nd Workshop on Abusive Language Online at EMNLP 2018, Brussels, Belgium, October 2018
• FAT/ML 2018 at ICML 2018, Stockholm, Sweden, July 2018
• Ethics in Natural Language Processing at NAACL 2018, New Orleans LA, June 2018
• ACM FAT* Conference 2018 NY, NY, February 2018
• FAT/ML 2017 at KDD 2017, Halifax, Canada, August 2017
• ALW1: 1st Workshop on Abusive Language Online at ACL 2017, Vancouver, Canada, August 2017
• Ethics in Natural Language Processing at EACL 2017, Valencia, Spain, April 2017
• 3rd International Workshop on AI, Ethics and Society 4th or 5th February 2017 San Francisco, USA
• PDDM16 The 1st IEEE ICDM International Workshop on Privacy and Discrimination in Data Mining December 12, 2016 - Barcelona
• Machine Learning and the Law NIPS Symposium 8 December, 2016 Barcelona, Spain
• FAT/ML 2016 NY, NY, November 2016
• AAAI Fall Symposium on Privacy and Language Technologies, November 2016
• Workshop on Data and Algorithmic Transparency (DATཌ) November 19, 2016, New York University Law School
• WSDM 2016 Workshop on the Ethics of Online Experimentation, February 22, 2016 San Francisco, California
• ETHI-CA2 2016: ETHics In Corpus Collection, Annotation and Application LREC 2016, Protoroz, Slovenia.
• FAT/ML 2015, at ICML 2015, Lille, France, July 2015
• ETeRNAL - Ethique et TRaitemeNt Automatique des Langues June 22, 2015, Caen
• Éthique et Traitement Automatique des Langues, Journée d'étude de l'ATALA Paris, France, November 2014
• Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) 2014, at NeurIPS 2014, Montréal, Canada, December 2014

Other lists of resources

• Critical Algorithm Stuides
• FATML resources page
• The Responsible Conduct of Computational Modeling and Research NSF funded project

Other courses

• ACL wiki page on teaching Ethics in NLP (list of courses)
• A Course on Fairness, Accountability and Transparency in Machine Learning (Suresh Venkatasubramanian)
• Ethics for the Information Age (Michael Quinn)
• Another list of courses from Thomast Morgan Jr. at ETSU
• The Dark Side of NLP: Gefahren automatischer Sprachverarbeitung (Michael Strube)
• Histories of AI from Jonnie Penn, Rebecca Charbonneau, Jenny Carla Moran at the University of Cambridge.