https://termsweservewith.org Terms-we-Serve-with Thu, 09 Jun 2022 19:59:51 +0000 https://termsweservewith.org en https://termsweservewith.org/index Thu, 09 Jun 2022 19:59:51 +0000 Terms-we-Serve-with https://termsweservewith.org/index ︎"Power flows through governing bodies, social institutions, and micro-interactions, all of which engage with technologies of the time."- Jenny Davis, Apryl Williams, and Michael W Yang2021. Algorithmic reparation. Big Data &amp; Society, 8(2), 20539517211044808.Recent updates: - 11/15/23 - read our academic paper - Rakova, B., Shelby, R., &amp; Ma, M. (2023).&nbsp;Terms-we-serve-with: Five dimensions for anticipating and repairing algorithmic harm. Big Data &amp; Society, 10(2). https://doi.org/10.1177/20539517231211553 - 10/04/23 - a blog post update published by Stanford Law School&nbsp; - Engaging on Responsible AI terms: Rewriting the small print of everyday AI systems.&nbsp;&nbsp; - 05/24/23 - a blog post update published by Data and Soceity - A New Framework for Coming to Terms with Algorithms.&nbsp;Reflections on terms of service, gender equity, and chatbots&nbsp; “I agree to the terms of service” is perhaps the most falsely given form of consent, often leaving individuals powerless in cases of algorithmic harm i.e.&nbsp;incidents experienced by individuals and communities that lead to social, material, or ecological harms, resulting from algorithmic systems and interactions between human and algorithmic actors.&nbsp; The Terms-we-Serve-with (TwSw) is a&nbsp;social, computational, and legal framework. Along each of its five dimensions, we help technology companies, communities, and policymakers co-design and operationalize critical feminist interventions that help them engage with AI in a way centered on trust, transparency, and human agency.&nbsp; &nbsp;We are looking to engage with interdisciplinary collaborators - express your interest in joining our online focus group here.Socio-technical outputs from engaging with the framework include:Human-centered user agreements i.e. ToS, content policies, data use agreements, etc.User studies and user experience research that enables specific kinds of UX design frictionAn ontology of user-perceived AI failure modes Contestability mechanisms that empower continuous AI monitoring grounded in an ontologyMechanisms that enable the mediation of potential algorithmic harms, risks, and functionality failures when they emerge Reach out to us to learn more: b.rakova@gmail.com Explore the five dimensions of the&nbsp;Terms-we-Serve-with (TwSw) social, computational, and legal framework - co-constitution, addressing friction, informed refusal, disclosure-centered mediation, and contestability.<img width="1109" height="969" width_o="1109" height_o="969" data-src="https://freight.cargo.site/t/original/i/68dafae2b6f0892adc9fc949c560c3e57f3654b9b0cc2680bc791f4509a9cb84/twsw_expanded.png" data-mid="191202770" border="0" src="https://freight.cargo.site/w/1000/i/68dafae2b6f0892adc9fc949c560c3e57f3654b9b0cc2680bc791f4509a9cb84/twsw_expanded.png" /> Case Studies Read about our pilot project with the Kwanele startup, using an AI chatbot in the context of gender-based violence prevention. They wanted to engage with the TwSw framework in determining ways to incorporate AI in a manner that aligns with their mission, values, and the needs of their users. We ran a multi-stakeholder workshop with them and inspired them to leverage the design principles of an early version of the TwSw open source technical tool. As a result, they developed what we frame as, critical feminist interventions, for example (1) mechanisms to better engage with their users, helping them understand and navigate potential risks and harms of their use of an AI chatbot and (2) user interface that empowers users to continuously give the developer team improved feedback about potential risks and harms of the AI. Ultimately, helping them better serve their users. Read a summary blog post on reimagining consent and contestability in AI. Join us in discussing this proposal at the&nbsp;Power Asymmetries track of the Connected Life Conference, Oxford Internet Institute. Learn more about the framework, share about your work, and contact us at: b.rakova@gmail.comLet’s engage in co-creating a new trustworthy social imaginary for improved transparency and human agency in AI. &nbsp; https://termsweservewith.org/co-constitution Tue, 07 Jun 2022 16:39:24 +0000 Terms-we-Serve-with https://termsweservewith.org/co-constitution Co-constitution Co-constitution is an opportunity to challenge one-sided and coercive modes of participation in AI development. Instead, we question what does meaningful participation means and how could technology companies co-design with the communities they are serving. Marginalized communities and various stakeholders could take part in drafting user agreements and be rewarded for their participation. We believe they can help technology companies anticipate algorithmic harm and design adequate response mechanisms that empower solidarity.&nbsp; We encourage practitioners to ask -&nbsp;Who are the stakeholders engaged in the lifecycle of design, development, and deployment of AI? How are they contributing? How are they rewarded for their contribution? Are there other stakeholders who are currently not represented, but could be considered unintended users of the algorithmic system and be impacted by it directly or through any downstream decisions made by other human or algorithmic actors? Resources:DiSalvo C, Clement A and Pipek V (2012) Participatory design for, with, and by communities. In: Simonsen J and Robertson T (eds.) The Routledge International Handbook of Participatory Design. Routledge, pp.182-209.Eigen, ZJ (2012) Experimental evidence of the relationship between reading the fine print and performance of form-contract terms. Journal of Institutional and Theoretical Economics 168(1): 124-141. Gordon-Tapiero A,Wood A, and Ligett K (2022) The case for establishing a collective perspective to address the harms of platform personalization. In Proceedings of the 2022 Symposium on Computer Science and Law (CSLAW '22). Association for Computing Machinery.Hagan M (2020) Legal design as a thing: A theory of change and a set of methods to craft a human-centered legal system. Design Issues 36(3): 3-15.Kitkowska, A., Warner, M., Shulman, Y., Wästlund, E., &amp; Martucci, L. A. (2020, August). Enhancing privacy through the visual design of privacy notices: exploring the interplay of curiosity, control and affect. In Proceedings of the Sixteenth USENIX Conference on Usable Privacy and Security (pp. 437-456). <img width="2000" height="3000" width_o="2000" height_o="3000" data-src="https://freight.cargo.site/t/original/i/29e71568ecef7a96e92062f9fc6b3789e9c8277e6d7568b1558bef41cf2e66bc/a.jpg" data-mid="144795662" border="0" src="https://freight.cargo.site/w/1000/i/29e71568ecef7a96e92062f9fc6b3789e9c8277e6d7568b1558bef41cf2e66bc/a.jpg" /> 001a <img width="2000" height="3000" width_o="2000" height_o="3000" data-src="https://freight.cargo.site/t/original/i/319490a738d912d27878f6875bc4088f80ca548ddf7d9641c46228fa3d6f59c8/b.jpg" data-mid="144795664" border="0" src="https://freight.cargo.site/w/1000/i/319490a738d912d27878f6875bc4088f80ca548ddf7d9641c46228fa3d6f59c8/b.jpg" /> 001b <img width="2000" height="3000" width_o="2000" height_o="3000" data-src="https://freight.cargo.site/t/original/i/17fc6c7312e692340c7e67b299a41656814c387a1efd6b6e1cbbdd62121801ac/c.jpg" data-mid="144795663" border="0" src="https://freight.cargo.site/w/1000/i/17fc6c7312e692340c7e67b299a41656814c387a1efd6b6e1cbbdd62121801ac/c.jpg" /> 001c <img width="2000" height="3000" width_o="2000" height_o="3000" data-src="https://freight.cargo.site/t/original/i/66303584544fb48a22200ca159ded6f920008f2a28fa28ade954194c696226db/aaa.jpg" data-mid="144795660" border="0" src="https://freight.cargo.site/w/1000/i/66303584544fb48a22200ca159ded6f920008f2a28fa28ade954194c696226db/aaa.jpg" /> https://termsweservewith.org/productive-friction Tue, 07 Jun 2022 16:39:24 +0000 Terms-we-Serve-with https://termsweservewith.org/productive-friction Addressing Friction Collectively anticipating and addressing AI frictions supports development of trustworthy algorithms and redistributes the allocation of benefits and burdens. TwSw interventions acknowledge and reflect marginalized knowledge systems. We challenge existing deceptive design practices and seek to enable meaningful dialogue through the production and resolution of conflict. We believe critical discussion allows individuals to self-organize and discuss algorithmic harms and accountability mechanisms in a way that is safe, respects their privacy, and human dignity. We encourage practitioners to ask:&nbsp;What frictions or tensions exist among stakeholders (i.e. builders, policymakers, vulnerable populations, etc.)? What is your understanding of the failure modes of the AI system? How do different stakeholders experience friction in interacting with the AI when there’s a functionality failure? Could intentional frictions be a force for algorithmic reparation, for example, what are some nudges you've come across in the context of the AI system; what do these nudges enable (e.g., further engagement, caution, learning); what nudges and choice architecture or affordances could empower transparency, slowing down, self-reflection, learning, and care? Resources:Costanza-Chock S (2020) Design Justice: Community-led Practices to Build the Worlds We Need. The MIT Press.Dean J, Dunford K, Gupta K, Marini M (2022)&nbsp;Towards Trusted Design—takeaways from Envisioning Yesterday’s Future. Web Foundation.DeVito MA (2021) Adaptive folk theorization as a path to algorithmic literacy on changing platforms. ACM Conference on Human Computer Interaction 5(CSCW2) (pp.1-38).DiSalvo, C., &amp; Lukens, J. (2009). Towards a critical technological fluency: The confluence of speculative design and community technology programs. Dunne, A., &amp; Raby, F. (2013). Speculative everything: design, fiction, and social dreaming. MIT press.Hamraie A and Fritsch K (2019) Crip technoscience manifesto. Catalyst: Feminism, Theory, Technoscience 5(1): 1-33. Lemley MA (2022) The benefit of the bargain. Stanford Law and Economics Olin Working Paper No. 575. Mathur A, Acar G, Friedman MJ, Lucherini E, Mayer J, Chetty M and Narayanan A (2019) Dark patterns at scale: Findings from a crawl of 11K shopping websites. In Proceedings of the ACM on Human-Computer Interaction 3(CSCW). (pp. 1-32). Nguyen, S and McNealy J (2021) “I, obscura:” Illuminating deceptive design patterns in the wild. UCLA Center for Critical Internet Inquiry. (Accessed 21 February 2023)Raji ID, Kumar IE, Horowitz A and Selbst A (2022) The fallacy of AI functionality. In Proceedings of 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 959-972).Sinders C (2020) We Need a New Approach to Designing for AI, and Human Rights Should Be at the Center.Stanley J (2017) Pitfalls of artificial intelligence decision making highlighted in Idaho. ACLU Case. ACLU Blogs. (Accessed 21 February 2023).Ytre-Arne B and Moe H (2021) Folk theories of algorithms: Understanding digital irritation. Media, Culture &amp; Society 43(5): 807-824. <img width="2000" height="2551" width_o="2000" height_o="2551" data-src="https://freight.cargo.site/t/original/i/af714e59d1c0801c590eb145d00f2cc1d622067790157999ad01fe261410cd16/aa.jpg" data-mid="144795667" border="0" src="https://freight.cargo.site/w/1000/i/af714e59d1c0801c590eb145d00f2cc1d622067790157999ad01fe261410cd16/aa.jpg" /> 001a <img width="2000" height="2551" width_o="2000" height_o="2551" data-src="https://freight.cargo.site/t/original/i/aa45f9524f4926f4e12f565eb471f61ab0bbf6a58b40c28c87d45f87e432f450/cc.jpg" data-mid="144795668" border="0" src="https://freight.cargo.site/w/1000/i/aa45f9524f4926f4e12f565eb471f61ab0bbf6a58b40c28c87d45f87e432f450/cc.jpg" /> 001b <img width="2000" height="2551" width_o="2000" height_o="2551" data-src="https://freight.cargo.site/t/original/i/c242e961677e6c1660576645169e70f8dd816569faf60ea3a1477f20cc5bf3a9/g.jpg" data-mid="144795665" border="0" src="https://freight.cargo.site/w/1000/i/c242e961677e6c1660576645169e70f8dd816569faf60ea3a1477f20cc5bf3a9/g.jpg" /> https://termsweservewith.org/informed-refusal Tue, 07 Jun 2022 16:39:25 +0000 Terms-we-Serve-with https://termsweservewith.org/informed-refusal Informed Refusal &nbsp; TwSw interventions see refusal as a generative practice - given the opportunity to opt out, individuals are inspired to provide technology companies their critical feedback about what needs to change for them to opt in. The concept of informed refusal comes from Ruha Benjamin’s work where she compares it to the concept of informed consent in medical law, arguing for a justice-oriented approach to constructing more reciprocal relationships between institutions and communities. &nbsp; We recognize that people hold a multiplicity of dynamic human identities. Inspired by the temporal dynamics in law - writing in the present, reflecting on the past, and encoding the future, we challenge the dominant temporal design of algorithmic systems which have been critically framed as self-fulfilling prophecies and stochastic parrots. We argue for the need to incorporate multifaceted mechanisms to refuse harmful algorithms within the lifecycle of AI including its design, the user interface, as well as contractual agreements such as terms of use and content policies.&nbsp; We encourage practitioners to ask:&nbsp;What are the assumptions and problem formulations that underpin a particular AI model or system? Who gets to define the default conditions? What does consent mean and what forms does it take? What is the temporal dynamics of consent and its refusal? Resources:Barabas, C., Virza, M., Dinakar, K., Ito, J., &amp; Zittrain, J. (2018, January). Interventions over predictions: Reframing the ethical debate for actuarial risk assessment. In Conference on fairness, accountability and transparency (pp. 62-76). PMLRBarabas C (2022) Refusal in data ethics: Re-imagining the code beneath the code of computation in the carceral state. Engaging Science, Technology, and Society 8(2): 35–57.Benjamin R (2016) Informed refusal: Toward a justice-based bioethics. Science, Technology, &amp; Human Values 41(6): 967-990Benjamin R (2020) Race After Technology: Abolitionist Tools for the New Jim Code. Polity.Cifor M, Garcia P, Cowan TL, Rault J, Sutherland T, Chan A, Rode J, Hoffmann AL, Salehi N and Nakamura L (2019) Feminist Data Manifest-No. (Accessed 20 February, 2022)Ganesh MI and Moss E (2022) Resistance and refusal to algorithmic harms: Varieties of ‘knowledge projects’. Media International Australia 183(1): 90-106.Garcia P, Sutherland T, Salehi N, Cifor M and Singh A (2022) No! Re-imagining data practices through the lens of critical refusal. In Proceedings of the 2022 ACM Conference on Computer-Supported Cooperative Work &amp; Social Computing (pp.1-20).Kenway J, François C, Costanza-Chock S, Raji ID and Buolamwini J (2022) Bug bounties for algorithmic harms: Lessons from cybersecurity vulnerability disclosure for algorithmic harms discovery, disclosure, and redress. Algorithmic Justice League. (Accessed 21 February 2023).Matias JN, Johnson A, Boesel WE, Keegan B, Friedman J and DeTar C (2015) Reporting, reviewing, and responding to harassment on Twitter. arXiv preprint arXiv:1505.03359Shen H, DeVos A, Eslami M and Holstein K (2021) Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors. In Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2) (pp.1-29).Wright S (2018) When dialogue means refusal. Dialogues in Human Geography 8(2): 128-132. <img width="2000" height="2786" width_o="2000" height_o="2786" data-src="https://freight.cargo.site/t/original/i/b608340a741d7a9fd2f42fb184867ca5e93f6e0f4f369ad7738587a19a6ca067/a.jpg" data-mid="144795671" border="0" src="https://freight.cargo.site/w/1000/i/b608340a741d7a9fd2f42fb184867ca5e93f6e0f4f369ad7738587a19a6ca067/a.jpg" /> https://termsweservewith.org/complaint Tue, 07 Jun 2022 16:39:25 +0000 Terms-we-Serve-with https://termsweservewith.org/complaint Contestability and ComplaintContestability broadly refers to the ability for&nbsp;people to disagree, challenge, dispute, or otherwise express their critical concerns. Furthermore, in the field of feminist science and technology studies, complaints are expressions of dissatisfaction, pain, or grief.Within this dimension of the framework, we envision mechanisms that empower people to voice concerns as testimonies to structural and institutional problems. These concerns can be related to any and all parts of the AI life cycle including data collection, curation, labeling issues, data use, the training of a specific model, algorithmic audits, as well as the sunsetting of models or entire products. We believe individuals and communities could build and leverage open-source tools in verifying that their concerns are being addressed. For example, we point to the use of computable contracts as a mechanism to verify properties of algorithmic outcomes and enable reporting of algorithmic harm. We see this as a new feedback mechanism between technology companies and civil society stakeholders who have the expertise to take action in helping individuals and communities. We encourage practitioners to ask: What are institutional barriers for AI builders to meaningfully "hear" complaints? Have you ever provided feedback to an app? Or, if you haven't, what prevented you from providing feedback? After deploying the AI, can you anticipate how potential algorithmic bias might lead to harmful user experiences? After deploying the AI, how would you engage with end users and communities? What would it look like to "hear" and act on user complaints?&nbsp; Resources:Ahmed S (2021) Complaint! Duke University Press.Boyarskaya M, Olteanu A and Crawford K (2020) Overcoming failures of imagination in AI infused system development and deployment. arXiv preprint: arXiv:2011.13416Fu B, Lin J, Li L, Faloutsos C, Hong J and Sadeh N (2013) Why people hate your app: Making sense of user feedback in a mobile app store. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1276-1284).Gordon-Tapiero A,Wood A, and Ligett K (2022) The case for establishing a collective perspective to address the harms of platform personalization. In Proceedings of the 2022 Symposium on Computer Science and Law (CSLAW '22). Association for Computing Machinery. https://doi.org/10.1145/3511265.3550450. Griffin D and Lurie E (2022) Search quality complaints and imaginary repair: Control in articulations of Google Search. New Media &amp; Society.Holloway BB and Beatty SE (2003) Service failure in online retailing: A recovery opportunity. Journal of Service Research 6(1): 92-105.&nbsp; Khalid H, Shihab E, Nagappan M and Hassan AE (2014) What do mobile app users complain about? IEEE Xplore 32(3): 70-77.Panichella S, Di Sorbo A, Guzman E, Visaggio CA, Canfora G and Gall HC (2015) How can I improve my app? Classifying user reviews for software maintenance and evolution. In 2015 IEEE International Conference on Software maintenance and evolution (ICSME) (pp. 281-290). <img width="2000" height="2700" width_o="2000" height_o="2700" data-src="https://freight.cargo.site/t/original/i/52edac0a32ddee92e030f8abeb8d0863df298c6e5f7f66f163e55dc4682783a8/22.jpg" data-mid="144795673" border="0" src="https://freight.cargo.site/w/1000/i/52edac0a32ddee92e030f8abeb8d0863df298c6e5f7f66f163e55dc4682783a8/22.jpg" /> <img width="2000" height="2700" width_o="2000" height_o="2700" data-src="https://freight.cargo.site/t/original/i/6ec529f96bf3a266636f9b02c683868d2073b83d92b39b12f2678b837d5e58ec/11.jpg" data-mid="144795674" border="0" src="https://freight.cargo.site/w/1000/i/6ec529f96bf3a266636f9b02c683868d2073b83d92b39b12f2678b837d5e58ec/11.jpg" /> <img width="2000" height="2509" width_o="2000" height_o="2509" data-src="https://freight.cargo.site/t/original/i/9f15a9a346e395667b6b4d0811b722b055f813700d5ed5914eaeaf061558b729/d.jpg" data-mid="144795675" border="0" src="https://freight.cargo.site/w/1000/i/9f15a9a346e395667b6b4d0811b722b055f813700d5ed5914eaeaf061558b729/d.jpg" />