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CHI-Nnabis: Implications of Marijuana Legalization for and from Human-Computer Interaction

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CHI-Nnabis: Implications of Marijuana Legalization for and from Human-Computer Interaction CHI-nnabis: Implications of Marijuana Legalization for and from Human-Computer Interaction Brian C. Keegan Jofish Kaye Abstract University of Colorado, Boulder Mozilla The consumption of cannabis has substantial implications Boulder, CO 80309, USA Mountain View, CA 94041 for medicine, popular culture, and technology use, yet dis- [email protected] acm@jofish.com cussion of it is almost entirely absent in the HCI literature. Patricia Cavazos-Rehg Munmun de Choudhury Taking advantage of CHI 2017’s location in one of the first Washington University Georgia Institute of Technology jurisdictions to legalize recreational use of marijuana in the St. Louis, MO 63110 Atlanta, GA 30308 U.S., this panel will discuss its socio-technical implications, [email protected] [email protected] identify HCI research themes relevant to policy and public health debates, and outline a research agenda. Anh Ngoc Nguyen Michael J. Paul Saolasoft, Inc. University of Colorado, Boulder Author Keywords Centennial, CO 80112 Boulder, CO 80309, USA [email protected] [email protected] cannabis; marijuana; drug policy; legalization Saiph Savage ACM Classification Keywords Universidad Nacional Autonoma K.4.1 [Computers and Society]: Public Policy Issues de Mexico (UNAM) Mexico City, Mexico Background [email protected] While the use of cannabis (popularly: marijuana, pot, weed, ganja, grass, reefer, chronic, dope, herb) remains illegal under U.S. federal law, voters in an increasing number of Permission to make digital or hard copies of part or all of this work for personal or states are proposing and passing ballot initiatives to le- classroom use is granted without fee provided that copies are not made or distributed galize marijuana production, sale, and consumption for for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. recreational purposes. In 2012, Colorado voters passed For all other uses, contact the Owner/Author. a ballot amendment to amend the state constitution to le- Copyright is held by the owner/author(s). CHI’17 Extended Abstracts, May 06–11, 2017, Denver, CO, USA galize recreational sale and consumption. As of 2017, ACM 978-1-4503-4656-6/17/05. seven other states (Nevada, Maine, Washington, Califor- http://dx.doi.org/10.1145/3027063.3051139 nia, Massachusetts, Alaska, and Oregon) now permit the recreational sale and consumption of marijuana and there We have identified four preliminary (but not exhaustive) are pending ballot initiatives across many states to further themes for the panel that intersect a broad set of research decriminalize, permit medical use, or legalize recreational topics within HCI: regulatory compliance, data analytics, uses. As these policy changes accelerate not only within community support, and knowledge management. This the United States, but across the world, policymakers, reg- panel will outline the boundaries of a socio-ecological frame- ulators, and the public will look to experts to provide guid- work to understand the influences on cannabis uses within ance through complex social and technical questions. the context of environments where people live and inter- act. Specifically, this involves understanding multi-level We argue this policy change has substantial and on-going processes such as changes in the policy environment at a implications for and from researchers in human-computer macro-level, social and technological disruptions at a com- interaction. Implications for HCI include understanding how munity level, and individual behaviors at a micro-level [19]. new social and cultural practices co-mingle with information technology design and use. Implications from HCI include Regulatory compliance applying lessons about human-centered computing and Legalization policies require different user roles to collect collaborative work to the design of new systems. Since le- and submit data into centralized database systems. galization went into effect in January 2014, this new local Multi-stakeholder usability. Regulators require produc- industry has seen rapid expansion [8,9, 18]. ers and retailers to regularly submit detailed data about yields, potency, inventory, and prices. What lessons It should come as no surprise that cannabis consumption does HCI provide for implementing ambiguous legisla- intersects with the social uses of information systems [1]. tive language into systems or ensuring usability across Social media platforms like YouTube [11], Instagram [6], diverse (and reluctant) stakeholders? Craigslist [16], and Twitter [3,4,5, 10, 15, 17, 20] are filled Privacy. Databases of consumer or producer information with cannabis-related content and behavior. Other researchers may be legal at the state level, but they could carry sig- have examined the anonymous online markets like Silk nificant civil or criminal penalties at the federal level. Road where controlled substances are exchanged [7, 21] Relevant results from How can these systems be designed to protect the pri- as well as methods and ethics for researching online popu- ACM DL keyword search vacy of users? What are risks of associating one’s on- lations of drug users [2, 14]. line identity with cannabis content within social network- “cannabis” – 1 ing or e-commerce sites? “marijuana” – 0 However the analysis of cannabis legalization has largely Law enforcement. Criminal organizations have histori- “weed” – 0 been confined to public health [22] and policy research [13]. cally exerted substantial influence over the production “pot” – 0 The broader HCI community is all but absent from these and distribution of cannabis. What kinds of data should “legalization” – 0 scholarly discussions about the design, use, and implica- be used by or insulated from law enforcement to deter “recreational drug” – 0 tions of socio-technical systems for cannabis consumption. criminal activity? How can systems be designed to sup- “illicit drug” – 1 A search of the ACM Digital Library finds very sparse cov- port greater compliance and fairer enforcement? erage of papers discussing cannabis, despite its substantial influence in history, policy, and popular culture [12]. Data analytics Newcomers. Existing cannabis-related communities are Commercial cannabis entrepreneurs are collecting large confronting influxes of new users unfamiliar with exist- and diverse kinds of data to improve their decision-making. ing norms. What strategies are effective for socializing newcomers into existing communities? How are these Genetics and phenotypes. Cannabis breeders invest sig- communities changing their social and technical archi- nificant resources to retain and amplify desired genetic tecture to capture new community members? traits in their strains. Which data sources and analy- Political mobilization. The tenuous legal status of cannabis sis methods are being prioritized for decision making? use in many jurisdictions requires on-going lobbying and What are social and technical barriers to the broader activism. Firms like AirBnB and Uber have attempted adoption of bioinformatics tools and methods? to mobilize their users to take political action around Marketing and sales. State regulations and media poli- policies affecting them. How is the cannabis industry cies bar retailers from traditional strategies for advertis- similarly mobilizing customers to take political action? ing and customer tracking. What marketing strategies do firms use to attract and retain customers? How can online surveys, digital ethnography, and log analysis Knowledge management be used for feedback and forecasting? How does con- Previously marginalized knowledge is being translated into sumption integrate with other modes of interaction? formal systems to support innovation and collaboration. Health and safety. Cannabis can be consumed in many Folk knowledge. Artisinal practices developed as an illicit ways with varying health benefits and risks. How do activity face commercialization pressures following le- users track their consumption as a part of “quantified galization. What breeding strategies, phenotypical traits, self” practices? How can human-centered design ap- and other folk knowledge are translated into quantifiable proaches improve the safety and novelty of cannabis metrics and reproducible processes? How are cannabis consumption with information or design interventions? firms aligning qualitative and quantitative data? Social sensors. “Canna-seurs” document the smell, taste, Supporting communities texture, and effects of different strains and share these Consumers are organizing online and offline communities results with each other. How can these self-reports be to socialize together, market goods, and politically mobilize. aggregated to make recommendations or be aligned with other data? What user experience frameworks can Destigmatization. Recreational cannabis users confront guide customer and breeder decisions? stereotypes and other stigmas around cannabis use. Collaborative work. Many cannabis-specific social com- How are existing platforms like Facebook, Amazon, or puting systems exist to document and organize infor- YouTube managing the presence of cannabis content in mation about different strains, brands, recipes, etc. How their community? What new communities are emerging successful are cannabis-specifc platforms in emulating and what existing affordances are they adopting versus the success of other computer-supported collabora- developing
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