The Data Citizen, the Quantified Self, and Personal Genomics
The Data Citizen, the Quantified Self, and Personal Genomics
Abstract and Keywords
The rise of the quantified self and personal social genomics movements pose fundamental questions about the nature of citizenship that go well beyond the confines of reductive concepts of privacy. As we live more and more algorithmically through self-tracking, our identities are necessarily being caught up in the cloud. We maintain that by looking integrally at the “data citizen” we can move beyond reductive concepts of privacy and begin to construct a viable ethics for our brave new world. First, we explore the concept of the data citizen and its policy relevance. Taking the lens of the quantified self and personal genomics, we next discuss five settings in which we can discern the data citizen in action. Finally, we draw up policy recommendations and design principles derived from our notion of the data citizen.
The rise of the quantified self and personal social genomics movements pose fundamental questions about the nature of citizenship that go well beyond the confines of reductive concepts of privacy. As we live more and more algorithmically through self-tracking, our identities are necessarily being caught up in the cloud.
Introduction: We Are All “Data Citizens”
In 1941, pragmatist philosopher Arthur F. Bentley wrote a classic essay entitled: “The Human Skin: Philosophy’s Last Line of Defense,” where he argued that it was epistemologically absurd to separate off the knower (inside the skin) and the known (outside the skin) (Bentley 1941). Rather, he said, we should look at knower and known from the point of view of the knower-known system as a whole.
We argue that seeing the citizen in there (beneath the naked skin) and data about the citizen out there (circulating within ever denser industrial and governmental networks) is equally misconceived; it is thus an instance of misplaced concretism1 (Whitehead 1925 , Whitehead 1997 2; Wildman and Michelson 1969 ; Hickman and Spadafora 2009; Star 1994) which leads us to a raft of concepts (such as privacy) that inevitably wind up in aporias. We maintain that by looking integrally at the “data citizen” we can move beyond these and begin to construct a viable ethics for our brave new world.
The argument follows in three parts. First, we motivate the concept of the data citizen and its policy relevance. Second, taking the lens of the quantified self and personal genomics we discuss five settings in which we can discern the data citizen in action. Third, and finally, we draw up some policy recommendations and design principles derived from this move.
It is commonplace today to deny the centrality of the skin in human biology. While “the skin” definitely has a role (“keeping your insides in” as Allan Sherman remarked3), it is clear that we are not the sum total of our genetic information. We are at birth bequeathed microflora and micro-fauna, which at the end of the day constitute 90 percent of the cells in our bodies—these microbiomes within which and together with “we” live affect our physical and psychological health, tailored only in some ways to ourselves. Further, we are integrally part of wider communities both within and without: our exposome encompasses not only our personal genome and the flora and fauna that inhabit our personal living spaces but also all “life-course environmental exposures (including lifestyle factors), from the prenatal period onwards” (Wild 2005).
If we see ourselves as basically elucidated by the information in our DNA, then it becomes extremely difficult to account for this community of self within which we live and through which we live together. And it becomes difficult to work out good biodiversity policy—for example, if we reify species (a misplaced concretism excoriated in the philosophy of biology) into independent entities that can be somehow independently stored and saved (in seed banks, or in silico) then we are necessarily missing the point of the nature of life on earth and end up saving the wrong kinds of units, elements, beings. These may be human-nonhuman collectives and/or individual and familial life trajectories that are culturally patterned and locative. Taking the relationship as ontologically prior (Bowker 2014) rather than the entity, we not only understand the world differently, but we also interact with it in different ways.
Toward a Policy Process on What Data to Share and How
There is a close analogy here with the role of data in our lives. We live in the epoch of the database (Manovich 1998, 2013; Bowker 2013). From birth, we are measured, described, and both acted upon and partially constituted by these circulating data. We are in a recurso that goes back to the period of the Enlightenment, when the first great national censuses were brought into being, along with and enabling the first great epidemiological surveys. Together, they gave us data about social circumstances and our health, which in turn affected our lives both directly—personally—and through the development of policy related to such data (childhood nutritional programs, health screening, and more). Large-scale systematic data collection moved our being into complex information networks that constitute our selves and our society. There is great uncertainty about (p.213) what data to share and how, for which we need to conceptualize and iteratively evolve a policy process.
What happens when you try to keep the “insides” in is demonstrated by the birth and death of the concept of privacy in the home. Privacy law over the past thirty years has dealt uneasily with this concretization of the concept. Is your car an extension of your home such that what is in there is under the same protection? Is your cell phone—password protected or not—geographically located (part of your office) or should it be searchable if you are stopped?4 Indeed, privacy as often conceived in policy circles is largely an empty concept, when data pooling and data analytics together render as moot the whole idea of what is inside and therefore protected. There are not enough locks in the world to keep data from talking with each other. And many are the proofs that locking personal data within a virtual box with metadata inscribed on its envelope is similarly misconceived (see, e.g., Harvard5); “Metadata is not just ‘the envelope’ rather than its contents.”6
Consider the (idealized) phenomenology of the citizen of the modern state. Performative data streams begin to circulate out of the unformed fetus. If the fetus has girl potential, it may in certain cultures be cut off before birth. If the fetus is suffering from Down’s syndrome, it may not be allowed into the world of the working healthy—and if it is, the child will circulate from birth along predetermined social and medical channels. Once born, he or she is given a gender (forcibly in the case of the intersexed, for we live in a world of binary data that are entered into population statistics—along with data about ethnicity and health). Increasingly from birth, her/his genetic data are stored by governmental agencies for medical and/or other monitoring—policing—purposes; in both cases arguably for her/his later protection, though this is always the underlying rationale for attacks on privacy. Barely out of her/his mother’s womb, data are circulating out of her/him into agencies that transform the data and produce actions and policies and practices that come back to act upon and within her/him. Just as she/he was born with an ecology of microflora and microfauna, so is she/he born into an ecology of microdata. Both ecologies are performative. For the microdata, at the large scale, they help shape social policy that affects her/his living conditions; at smaller scales, they trigger a set of responses that affect her/his daily life and the lives of those nearby whether by family, social circles or “common fate”7 following theorists of Gestalt principles. And she/he is barely out of the womb. … To imagine a “citizen” as an ideal type who exists outside of data flows is then conceptually flawed: she/ (p.214) he becomes a citizen because she/he is saturated by data flows, markers, indicators, analyses. Later in life, few will be forks in her/his path that are not modulated by (note we are not saying “determined by”) these data flows—recommender systems will with increasing accuracy channel her/his and her/his consorts into sets of interests which render her/him and them as integrally good consumers and contented—if not satisfied—ones. The question with data is not whether data are essential, will circulate, will be used—but how so.
Taken together, the quantified self (QS) social identity and self-tracking movement and, in parallel, the rise of personal genomics can be seen as recognition and problematic appropriation of this data ecology. The future imaginary of the QS movement is that the QS data citizen will be able to live her/his life to the fullest through being optimally fit physically, cognitively, and emotionally in order to be the best person, worker, leader, and consumer she/he can be. It seems that we are moving into a brave new world, but once we look beyond the resplendence of modern techniques of personal quantification, this can be seen, again, as an essential dimension of the epoch of the database. Fifty years ago, measures were generally more circumscribed in their travels and fewer. We measured our temperature against an ideal number (98.4F) to know if we were running a fever. As children, our parents tracked our height, often by notches on a doorpost. One of our great modern obsessions, our body mass index (BMI), was first calculated by the great social statistician Adolphe Quételet in the early nineteenth century (Quételet 1869). Another obsession, calorie counting, began in the early twentieth century (see, e.g., Schüll 2012). What is fundamentally different about the modern form of QS is that its data have begun to flow into precisely the same kinds of channels—medical, commercial, governmental—that we have just described. The data from our devices—be they glasses, watches, tattoos on the skin, or woven into “smart” clothes—are uploaded onto websites so that we can finetune our bodies and minds to ideal numbers, and so that these data may be used to design ever more applications (hereafter “apps”), gadgets, devices—and to share our data with agencies and entities that seek to influence us.
The Design/Policy Knot
The policy issues that arise from QS and like movements cannot be addressed by assuming that we may exist outside of our data flows. We maintain, following Jackson, Gillespie, and Payette (2014) that fundamentally we are dealing with a design/policy knot. Thinking of the design/ (p.215) policy knot indicates that it’s not about producing universal principles, but about looking for and nurturing an ongoing process. You cannot design a data system and then look at its policy implications—you’re already too late. Inversely, you cannot state a policy apodictically and then design technology to suit: every policy enshrined in a concept of a citizen “out there” beyond data misses the performative point that what it is to be a citizen and what it is to be a person bring us to the possibility of being a new kind of data citizen. Every such policy, then, is fundamentally affected by the very design of our data flows and the social and engineering technologies that constitute our data ecologies.
We take up the Quantified Self and personal genomics social movements as microcosms for considering contexts and practices of and in relation to biosciences and biosensors, and how they inform our thinking about becoming data citizens and concepts of data citizenship. At the end of the day, we are especially interested in altruism, reciprocity, and immanence in this our brave new world.
Quantified Selves and Personal Genomics: Bioscience, Biosensors, Contexts, and Practices
The pervasive social trend now known as “the Quantified Self movement” orients toward self-tracking health and activity—also known as person-generated data—by means of wearable and other devices, apps, social media-oriented websites, and other sites and sources of digitally available health data via mobile phone or on the web. In the Health Data Exploration Project in which we participated (Health Data Exploration Project 2014;8 see also http://www.rwjf.org/en/library/research/2014/03/personal-data-for-the-public-good.html, accessed August 20, 2015), a surprising result was the marked willingness of individuals to share their personal data with other parties—even in many cases non-anonymously—for the public good.
Willingness and intentions for broader kinds of sharing are expressed particularly in regard to personal genomics.9 As examples among many: the “Million Veteran” campaign by the U.S. Veterans Affairs Office of Research and Development for donations of blood samples for genomics analysis (that will be anonymized); the gift of data from 100,000 member-patients of the U.S. health-care company Kaiser Permanente (a nonprofit trust) in conjunction with the University of California–San Francisco Institute for Human Genetics with support from the National Institutes of Health (https://www.ucsf.edu/news/2011/07/10305/ucsf-and-kaiser-permanente-complete-massive-genotyping-project (p.216) ); and the Personal Genome Project in which each contributor can modulate degrees of anonymity and sharing along a scale that includes the option of sharing in an open data commons (see, e.g., Angrist 2009).10
How may novel technologies and new kinds of personal data contribute to the public good, as a positively “disruptive” turning point that could provoke new modes of agency of individuals in their health, health and biomedical sciences research, and new kinds of “data commons” in which citizens and researchers can contribute and share data? How and why are people willing to share data about themselves? Table 12.1 outlines some possible parameters people might use in thinking through this question. How may new kinds of research collaboration be fostered—for example among “dream teams” that invite citizen participation—that could bring together multidisciplinary biomedical, health, policy, and social sciences researchers with companies and communities that design and make apps, devices, websites, and platforms, and thus hold self-tracked data generated by individuals and collectives? Under what kinds of governance and circumstances might companies make available the data they collect from individuals, might researchers make use of self-tracked data in their research, and might individuals be willing to share their data? Do we need “a new Belmont Report” (NCPHS 1979)—the current gold standard of ethics principles—to address the privacy, confidentiality, and anonymity of personal data in this brave new era? And if so, through what kind of a process will we develop it?
In self-identified Quantified Self meet-ups, there’s often a first-person narrative mode formatted as: What did I/we do? How did I/we do it? What did I/we learn? Personal narratives of self-quantification and knowledge gained by quantification and analysis over time and their visualization are proliferating. Yet some recent findings indicated that there might not be as much sharing of QS data as we think, even though in principle there is a willingness to do so (see, e.g., Health Data Exploration Project 2014). Concurrently, there is much sharing that occurs 1:1 in intimate relationships and sharing, much data tracking that is done on behalf of another person, much quantitative “registration” kept for regular routines (e.g., daily) of the body that are not written down but “only kept in the head,” much handwritten recordkeeping related to long-term clinical care, medication regimes, or for physical, cognitive, or medical recovery, whether for an acute condition or life-long (see Fox and Duggan 2013).
Table 12.1 Willingness to Share Quantified Self Data in Scientific Research
ACCESS: Respondent wants to be able to access his/her own data or the entire dataset as well.
NO GOVERNMENT: Data must not be used by the government.
AGGREGATE: Researchers can only have access to aggregated data or only report in the aggregate. This is closely related to privacy.
NON-COMMERCIAL: Non-commercial or nonprofit research only.
BENEFIT: Respondent should benefit in some way from the research (non-monetary).
OPT OUT: Respondent must be able to opt out of the study.
DEPENDS: General “It depends” statements.
OWNERSHIP: Respondent must retain ownership/control of data.
DEPENDS/DATA: It depends on the data. In other words, the respondent would be more willing to share some data than other data.
PAID: Monetary or similar compensation.
FEEDBACK: Researchers must return results of the study to the participants.
PRIVACY: Must protect/assure privacy of participants. This includes any mention of a privacy requirement, even if not absolute. Key terms included here are “privacy,” “private,” anonymous,” “confidential,” “do not want my named attached.”
GREATER GOOD: Research must be for the greater good, e.g.: “If there is definitely a benefit for the greater good, i.e. to help address a disease, etc.”
PROTECTION: Data cannot be used to discriminate against or deny service to Respondent. This includes use by insurance companies to set rates or deny coverage, use by employers in hiring, etc.
INFORMED: Respondent wants to be informed about the particular study or purpose of the research. Implies no general-purpose databases.
REPUTATION: Respondent would only share data with trusted researchers or institutions.
IRB/HIPAA: Specific mention of IRB, HIPAA, or similar regulatory mechanism.
RESEARCH: Data can only be used for research studies, not for marketing, product development, etc.
LOVED ONE: Would only share data if it would directly benefit a loved one.
RESTITUTION: Respondent wants to be able to get restitution for data misuse.
NO ADDS: No advertising or market research purposes.
TIME LIMIT: Data shared only for finite, specified time.
NO CONDITIONS: Would share freely without any conditions.
TRUST COMPANY: Respondent would only allow sharing if he/she trusted the company that held his/her data.
NO EFFORT: Sharing data for research must not require extra effort for the respondent.
WOULD NOT: Respondent would not share his/her data regardless of condition.
Source: Non-published preliminary data depiction (2013). Courtesy of the Health Data Exploration Project, University of California-San Diego and University of California-Irvine. M. J. Bietz, G. C. Bowker, S. Calvert, M. Claffey, J. Gregory, A. Hubenko, K. Patrick, R. Rao, and J. Sheehen. For the published report, see Personal Data for the Public Good: New Opportunities to Enrich Understanding of Individual and Population Health, Health Data Exploration Project, 2014. Supported by the Robert Wood Johnson Foundation.
(p.218) should be used. As in the “privacy paradox” (Norberg, Horne, and Horne 2007), when you ask people if they care about data, they say they do. If you look at their practices, many actually don’t interact with their data very much or very often. That’s why we need to understand practices, not only expressed opinions, to get at what people are actually doing. By the same token, we need better empirical understanding of people’s relationship to their own data as a rich field to study from the design angle (how to turn caring into doing) and from the social angle (understanding what issues are at stake for the citizens of tomorrow).
The Data Citizen Project: New Forms of Data Work of the Self and in Personal Life
Deeper understandings of living algorithmically individually and collectively through sharing self-tracking and personal genomics data in quantifying communities are also about our relationships with data about our selves. New kinds of data require new kinds of data citizens engaging in policy debates about the collection and deployment of these large new datasets, together exploring—with the wider publics—the social, cultural, and ethical issues arising in these vast data collection enterprises.
A variety of discourses concern how novel technologies and new kinds of personal data may contribute to the public good, as a positively “disruptive” turning point that could provoke new modes of agency of individuals in their health, health and biomedical sciences research, and new kinds of data commons in which citizens and researchers can contribute and share data. For these to come about, we need a critical approach to “genetic empowerment” as well as public understandings of as yet invisible algorithms. The mantra for “P4 Medicine” is for genetic empowerment to be “predictive, preventive, personalized, and participatory” (Hood and Galas 2003). Challenging questions abound: What role will personal genetics play in clinical dynamics? What do data citizens want to know? How will “fear of genetic fate” (ibid.) be addressed?
A growth area in QS is the relatively new availability of personal genomic testing. For example, for less than $100 companies like 23andMe will provide a kit for customers to send in a DNA sample that is then analyzed for approximately 250 aspects of health from back pain to breast cancer. Similarly, metagenomic analysis (genomic testing of populations of microorganisms) is being offered as a way to understand the ecosystem of nonhuman organisms to which our bodies play host. There is a growing awareness within the QS community and beyond (see chapter 1, this volume) that personal information can be even more powerful when it is (p.219) made social. Several QS citizen science projects have started to aggregate personal genomic and metagenomic data to, for example, “publish their test results, find others with similar genetic variations, learn more about their results, find the latest primary literature on their variations and help scientists to find new associations.”11 As a commonplace yet profound change, sleep research is exemplary in the transformation of knowledge enabled by digital wearables by the change from being sequestered in special cocoons to being in one’s natural habitat at home.
There is profound potential to lead new kinds of research, sciences, and caring practices, yet there are policy lacunae in regard to consumers’ everyday practices of routinely signing off on “terms and conditions”—as the condition for use of a services, an app, site, device, or gadget. Terms and conditions for big data services (QS, social media) more and more grant corporate ownership, use, reuse, and sale of users’ data corpus in part or in whole to other companies (as an integral part of the companies’ business models), whereas the individual human user-owner-subscriber(s) of the gadget or digital service might not have full rights or co-ownership to his or her own “raw” data stream (having routinely signed off rights to the self-generated data).12 These matters of concern are coming onto the public and policy radar (see especially Health Data Exploration Project 2014).
From research perspectives, we are especially interested in how new kinds of research practices and collaborations may be fostered. We see potential for “dream teams” that could bring together multidisciplinary biomedical, health, and social sciences researchers with companies that design devices, apps, websites, and platforms and thus hold self-tracked data generated by individuals. In regard to quantified selves and personal genomics, there is a much tighter connection from the beginning in policy, design, and practice, with policy not yet relevant enough to the kinds of infrastructures that we already have and that are evolving around us. Because social practices have their own autonomies in sense making, rhythms, commitments, disparate and consonant logics, and temporalities, there is a quickening and tightening up of interweaving in relation to health, biosciences, biosensors, and bioscience technologies—a need for more dynamic knowledge infrastructures (Edwards et al. 2013).
A “design/policy knot” ties together policy, design, and practice. They ravel, unravel, and ravel again and over time. We can tie together a design/policy knot with the development of new kinds of dream teams, ones that must include designers integrally with policy makers as an ongoing principle of governance (i.e., not as a one-off moment). The problem for most ethics frameworks (e.g., the Belmont Report, 1979) are that they (p.220) are seldom or never modified but rather conceived as “standing the test of time.” Yet technology is changing so fast in these interlaced arenas that there is no possible set of universal principles that transcends the technology since we are constituted differently as data citizens at different technological moments. Thus a dream team can become a form that engages an “ideal” yet “materially” grounded iterative process.
Thinking about Design: Knowledge through the Senses, Poetics, Reflection
Personal genomics communities and communities such as QS can develop and extend sharing practices in regard to health, wellness, and sharing and caring practices, augmenting established sites such as the Patients-LikeMe, rare illnesses groups, and Pharmville, creating more intimate or more open spaces.
We consider personal genomics at five scales:
1. one’s past, present, future
2. one’s self, family, ancestors, and the future
3. one’s genes, one’s markers, one’s health
4. data big and small, innately intimate data
5. the social, the cultural, new communities
For each we offer a haiku to express these condensed essences of phenomenal layers.
Haiku for Scale 1: one’s past, present, future
The Normal and the Pathological13
Alleles jump and switch and change places
Passenger and driver
Context is everything
Haiku for Scale 2: one’s self, family, ancestors, and the future
Unexpected Utopias Alongside Dystopias
It never occurred to me
That this could become
About undoing stigma
Haiku for Scale 3: one’s genes, one’s markers, one’s health
Transformations of Knowledge
Preventive—but not always
Corporeal and Corporate Landscapes
Are all at once ancestral
Temporally back and forth
Haiku for Scale 5: the social, the cultural, new communities
Altruism and Reciprocity
Walking our cities
One million veterans
And a sixth Haiku: on knowledge transformations, things falling apart and coming together anew
Science spins 360º
Of scientific knowledge14
In “Self Help,” poet Katie Peterson refracts her verse on the metamorphosis of the mind:
The eye is the lamp of the body so I tried to make a world where all I ate was light. Butterflies complete a similar labor in the summer garden, beating their wings slowly like a healthy person, the kind of person who runs for fun, could run from an attacker, eats greens in the same quantity as the salty meats the storytelling part of us appears to favor. I couldn’t decide whether I wanted to stay alive or wanted to go faster, they appeared to contradict each other, I tried in all I did to eat light. I left the argument about the difference between a slave and a servant on the table though I think what I think is that consent to servitude is as much fiction as a butterfly having a nervous breakdown because of the beauty of the lavender. The longer your hunger takes to find a shape the longer you can hold it. Consider the butterfly, only at rest in the middle of consumption, but even then practicing for departure, for disappearance, closing in the middle of the landscape. Trying to manage a world in which all you can eat is light is difficult. Labor, and the lungs should be like wings of the butterfly beating, closing, slowly, the moonlight tensing the edge of each, almost lifting the edge of each towards the middle distance. So all that I consume can make me healthy, illuminate my throat and the interstate of my digestive tract with what a butterfly’s been swimming in” (Deacon and Peterson 2014).
On the entrepreneurial side, companies both large and small, established and startups, need to build in consumers’ privacy protections at every (p.222) stage in developing their products. A more expansive notion of data citizenship might suggest to companies the possibility of considering forms of co-ownership with the people who use and elaborate their digital offerings. These could be reasonable security for consumer data, limited collection and retention of such data, and reasonable procedures to promote data accuracy (see FTC 2010). Here, too, practices run ahead of policy and visions for shared horizons.
If the notion of the “data citizen” suggests that data are always, in a sense, already “shared,” then there is also a vast opportunity to create design spaces for individuals, social circles, health and medical fields, biosciences, scientific discovery. This design for novel design spaces could consider, but not be limited to, cross-sensory surrounds and affordances such as synesthesia (Deacon and Peterson 2014, 122), artful abstraction (Dickerman 2013 ), visual elegance and visible language, aural sensation and subtleties of sound, the tactile, the poetic, interactive vignettes that can be personalized, tales from the future, epistolary and otherwise, mindfulness, restfulness, peacefulness, wisdom-enhancing. In brief, the conditions of possibility exist to design for new digital literacies in shared data.
We end as we began with our belief and hope in altruism, reciprocity, and immanence.
We wish especially to acknowledge Matthew J. Bietz, Tom Boellstorff, Paul Dourish, John E. Mattison, Dawn Nafus, Helen Nissenbaum, and Katherine Pine among many more inspired and inspiring colleagues than we can count. We wish to thank the Intel Science & Technology Center on Social Computing for supporting our preliminary study “An Exploratory Study of Personal Genomics and Metagenomics in the Quantified Self Movement” with a seed grant in relation to the research themes Algorithmic Living and Information Ecosystems.
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(2.) Alfred North Whitehead posited the fallacy of misplaced concreteness as when one mistakes an abstract belief, opinion, or concept about the way things are for a physical or “concrete” reality: “There is an error; but it is merely the accidental error of mistaking the abstract for the concrete. It is an example of what I (p.223) will call the ‘Fallacy of Misplaced Concreteness’” (Whitehead 1997 , 51). Whitehead proposed the fallacy in a discussion of the relation of spatial and temporal location of objects. He argued: “among the primary elements of nature as apprehended in our immediate experience, there is no element whatever which possesses this character of simple location. … Accordingly, the real error is an example of what I have termed: The Fallacy of Misplaced Concreteness” (Whitehead 1925 ).
(4.) See, e.g., the U.S. Supreme Court ruling at time of writing: http://www.nytimes.com/2014/06/26/us/supreme-court-cellphones-search-privacy.html, accessed August 18, 2015.
(5.) E-Mail Management: Guide for Harvard Administrators, Records Management Services, Harvard University Archives, http://library.harvard.edu/sites/default/files/E-MailManagementAGuideForHarvardAdministrators_0.pdf, accessed August 20, 2015.
(6.) http://www.cnet.com/news/what-you-need-to-know-about-data-retention/, accessed August 20, 2015; http://www.voidynullness.net/blog/2015/03/03/definition-of-metadata-mass-surveillance/, accessed July 30, 2015.
(7.) See, e.g., http://en.wikipedia.org/wiki/Principles_of_grouping#Common_Fate, accessed August 20, 2015: “When visual elements are seen moving in the same direction at the same rate (optical flow), perception associates the movement as part of the same stimulus. For example, birds may be distinguished from their background as a single flock because they are moving in the same direction and at the same velocity, even when each bird is seen—from a distance—as little more than a dot. The moving ‘dots’ appear to be part of a unified whole. Similarly, two flocks of birds can cross each other in a viewer’s visual field, but they will nonetheless continue to be experienced as separate flocks because each bird has a direction common to its flock. This allows people to make out moving objects even when other details (such as the object’s color or outline) are obscured. This ability likely arose from the evolutionary need to distinguish a camouflaged predator from its background. The law of common fate is used extensively in user-interface design, for example … [t]he movement of a physical mouse is synchronized with the movement of an on-screen arrow cursor, and so on.”
(8.) We wish to acknowledge the Robert Wood Johnson Foundation for support to the Health Data Exploration Project.
(9.) Both by avant-garde groups such as the Personal Genome Project and evidenced by public enrollments in donating.
(10.) The Personal Genome Project (PGP) has the most interesting informed consent process that one of the authors has experienced in that (a) the PGP Genome-Wide Association (GWA) should not be a donor’s first encounter with one’s personal genomics, that is to say the donor must have some self-knowledge about his or her genetic information via 23andMe, clinically or otherwise; (b) to contribute data from one’s GWA, you must read some pages on the basic science of genomics and pass a test as assurance that you understand basic principles and (p.224) terms of genomics; (c) the consent explanations are explicit about the uncertain and changing state of personal genomics knowledge; and (d) the consent process is iterative in consideration of new knowledge discovery that may change understandings.
(12.) Policy lacunae on terms and conditions were evident in a recent national science policy discussion yet blind to this.