Participatory Personal Data

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This is a preprint of an article accepted for publication inJournal of the American Society for Information Science and Technology

copyright 2012 (American Society for Information Science and Technology)

Participatory Personal Data:

An Emerging Research Challenge for the Information Sciences

Katie Shilton

College of Information Studies

University of Maryland

Room 4105 Hornbake Building, South Wing, College Park, MD 20742

Tel: (301) 405-3777

[email protected]


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Abstract

Individuals can increasingly collect data about their habits, routines, and environment using

ubiquitous technologies. Running specialized software, personal devices such as phones and tablets

can capture and transmit users location, images, motion, and text input. The data collected by these

devices are both personal (identifying of an individual), and participatory (accessible by that

individual for aggregation, analysis, and sharing). Such participatory personal data provide a new area

of inquiry for the information sciences. This paper presents a review of literature from diverse fields,

including information science, technology studies, surveillance studies, and participatory research

traditions to explore how participatory personal data relate to existing personal data collections

created by both research and surveillance. It applies three information perspectives information

policy, information access and equity, and data curation and preservation to illustrate social

impacts and concerns engendered by this new form of data collection. These perspectives suggest a

set of research challenges for information science posed by participatory personal data.

Introduction

Self-quantification, mobile health, bio-hacking, self-surveillance, participatory sensing: under a

variety of names, practices, and motivations, these growing movements are encouraging people to

collect data about themselves (Dembosky, 2011; Estrin & Sim, 2010; Hill, 2011). Ubiquitous digital

tools increasingly enable individuals to collect very granular data about their habits, routines, and

environments. Although forms of self-tracking have always existed, ubiquitous technologies such as

the mobile phone enable a new scope and scale for these activities. These always-on, always-present

devices carried by billions can capture and transmit users location, images, motion, and text input.

Mobile health developers are creating applications to track health behaviors, symptoms and side-


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effects, and treatment adherence and effectiveness (Estrin & Sim, 2010). Citizen science enthusiasts

banded together to test the Butter Mind experiment, volunteering to eat half a stick of butter a day

and to record diverse metrics to test impact on mental performance (Gentry, 2010). Office workers,

or their bosses, can use new software to track computer usage and analyze productivity. Athletes use

a range of portable devices to monitor physiological factors that affect performance. And curious

individuals track correlations between variables as diverse as stress, mood, food, sex, and sleep (Hill,

2011). Some hail the era of easy self-tracking and the resulting detailed stores of data for their

potential to unlock patterns and new knowledge. Others raise privacy, accessibility, and other social

concerns. As John Perry Barlow was recently quoted: Everything you do in life leads to a digital

slime trail" (Boudreau, 2011).

This new form of personal data invokes challenges and value judgments at the heart of

information science, including best practices for data organization and management; the rights,

power and agency of data collectors and aggregators; the nature of user participation in data

collection and new knowledge creation; and what should, or will, be documented, shared, and

remembered. This paper draws upon literatures from information science, technology studies, and

surveillance studies to investigate granular personal data, collected by and for individuals, as an

information problem. It uses this literature to explore two questions: how do these new forms of

personal data depart from existing personal data? And how do information perspectives help us

analyze the social values, impacts and concerns engendered by these new data? These questions

suggest new research challenges for privacy and information policy, information access and equity,

and data management, curation and preservation.

The paper begins by definingparticipatory personal data, and suggesting how these data differ

from existing research and surveillance data. It then explores literature from three information


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perspectives key to understanding participatory personal data: information policy, information

access, and data management and preservation. It uses these perspectives to suggest next steps to

investigate participatory personal data as an information science problem.

Defining participatory personal data

This paper focuses on a new class of data about people and their environments, generated by

an emerging network of embedded devices and specialized software. Software for mobile phones

and embedded devices enable individuals to track and upload a diverse range of data, including

images, text, location, and motion. Because using ubiquitous digital tools for data capture is a

relatively new activity, the terminology used to describe this research is varied, going under names

including mobile health or mHealth (Estrin & Sim, 2010), self-quantifying (Wolf, 2010), self-

surveillance (Hill, 2011; Kang, Shilton, Burke, Estrin, & Hansen, 2012), participatory sensing (Burke

et al., 2006; Estrin, 2010), and urban sensing (Cuff, Hansen, & Kang, 2008). For example, Your

Flowing Data(http://yourflowingdata.com) asks participants to use their mobile phones to send

short messages recording data points (e.g., weight, exercise accomplished, mood, or food eaten)

throughout the day. After these data are aggregated by the service, the software provides users with

visualizations to explore patterns in their daily habits and learn from their data. A different example

is the Personal Environmental Impact Report (PEIR) (http://peir.cens.ucla.edu), an application that uses

participants mobile phones to record their location every thirty seconds. Location is uploaded to the

PEIRservers, which use this time-location series to infer how much a participant drives each day,

giving participants a daily calculation of their carbon footprint and exposure to air pollution.

What unifies these projects is the data they collect:participatory personal data. Participatory

personal data are any representation recorded by an individual, about an individual, using a

mediating technology (which could be as simple as a spreadsheet or web entry form, but which is


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commonly a mobile device). Participatory signals that these new data are accessible to the data

subjects. This contrasts with both traditional research and surveillance data, which are typically

obscured or hidden from data subjects (Shilton, 2010). Personaldata are authored by an individual,

describe an individual, or can be mapped to an individual (Kang, 1998). Participatory personal data

often meet all three of these criteria. The individual uses a device to collect data, which are often

descriptive of a persons life, routine, or environment. And the data can quite literally be mapped to

a person. Geotagged photos or a GPS log of a persons movements throughout a day can be used to

identify an individual, even if no names or identifiers are directly attached to the data (Anthony,

Kotz, & Henderson, 2007; Iachello, Smith, Consolvo, Chen, & Abowd, 2005). Participatory personal

data then, refers to aggregations of representations or measurements collected by people, about

people. These data are part of a coordinated activity; they are not only captured, butprocessed,

analyzed, displayed and shared through a technological infrastructure. This article uses participatory

personal data project as an umbrella term for the activity of data capture, andparticipatory personal datato

refer to the resulting information resources.

Data collection technologies

Participatory personal data are dependent on a technical infrastructure consisting of devices,

software, data storage, and user interfaces. The devices are digital, networked, and embedded in

human environments. In practice these are often mobile telephones, due to the ubiquity and

accessibility of these devices (Estrin, 2010). But devices could also include tablets, networked body

sensors, or instrumented smart homes or buildings (Hayes et al., 2007; Kim, Schmid, Charbiwala,

& Srivastava, 2009). Software coordinates data collection by triggering samples (often using variables

such as time of day, location, or battery life), storing data locally, and deciding when to upload them

to central servers for processing (Froehlich, Chen, Consolvo, Harrison, & Landay, 2007). Storage is


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largely cloud-based, although researchers concerned with privacy have also suggested personal home

storage for added data security (Lam, 2009). User interfaces include both web- and phone-based

charts, maps and graphs, which provide aggregation and analysis services, helping users see and

interpret patterns in their data. The combination of devices, software, storage, and interfaces form

technologicalplatforms (Gillespie, 2010): infrastructures marshaled for new goals and purposes.

Excluded from participatory personal data collection platforms are systems which do not

reveal their data to the data subject. For example, instrumented homes that report energy use to a

utility company, but not the homeowner, do not generate participatory data (though they may

generate personal data). Opportunistic sensing research, which uses mobile phones to sense macro-

scale human activity without the consent or knowledge of individual phone users (Campbell,

Eisenman, Lane, Miluzzo, & Peterson, 2006), also does not collect participatory personal data.

Data collection projects

The technological platform is only one part of participatory personal data projects. The

actors and institutions involved in designing and deploying these platforms are quite diverse.

Established corporations and start-ups, academics, community groups, nonprofits and international

non-governmental organizations are all stakeholders in participatory personal data projects. Research

centers at institutions such as UCLA, Dartmouth, MIT, Intel Research, and Microsoft Research are

all developing participatory personal data platforms for use in the social, environmental, and health

sciences. Corporations such as exercise-tracking company FitBit (http://www.fitbit.com/)and

online-activity tracking service RescueTime (https://www.rescuetime.com/)have developed self-

tracking into a business model. Governments, telecommunications providers, health insurers, and

advertisers also have an interest in these data (Hill, 2011). The variety of stakeholders collecting
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participatory personal data will impact the social and political economy of collection, access, and

preservation of these data.

Characteristics of participatory personal data

Data collection platforms and projects lend particular characteristics to participatory

personal data. Common data types are currently bounded by the technical limitations of the devices

used for capture, although these may shift over time as mobile devices become more advanced. Off-

the-shelf mobile phones and tablets, by far the most accessible devices for collecting participatory

personal data, are currently limited to a handful of on-board sensors. Phones can sense sound (using

microphones), images (using cameras), location (using GPS or cell tower information), co-location

(of other phones or devices using Bluetooth), and motion (using accelerometers). However, when

accessibility or market penetration are not concerns, these on-board capabilities can be almost

infinitely extended, as phones can interface with off-board sensors using Bluetooth or other

communications protocols.

What unites and also defines these collections of sound, images, locations, and motion is

that that they are participatory: they are accessible to the subjects of the data themselves. This is a

departure from traditional forms of personal data collection. Because granular data collection was

expensive and time-consuming, it has historically been conducted by governments or corporations

with both the resources to collect data and the social and economic motivations to do so (Marx,

2002). Though fair information practices have long mandated that personal data be made available

to individuals upon request (Waldo, Lin, & Millett, 2007), participatory personal data upend this

tradition by making individual access to personal data an integral part of collection. This change has

been enabled by the proliferation of mobile and computing devices which enable individual capture,


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processing, and analysis. The participatory nature of such data collection marks a departure from

traditional data archives, social science research data, and corporate and government surveillance.

How participatory personal data depart from existing data

In a focus on collecting data about people, participatory personal data projects echo two familiar

kinds of data: scientific and social science research data, and surveillance data. But because

participatory personal data collection is increasingly performed by the data subjects themselves, it

also contrasts with previous understandings of both kinds of data. Traditionally, data about

individuals might have been collected by researchers, governments, or corporations. But by enabling

dispersed data capture and sharing, participatory personal data projects collapse the role of data

collectors and data subjects. This raises the issue ofparticipationin data collection, and how

participation alters the data landscape.

Surveillance data

Traditionally, granular personal data collection by corporations or governments has been

labeled surveillance. Surveillance studies research suggests that an important element in this data

collection is control (Lyon, 2001; Monahan, 2006b). Surveillance may protect people from danger,

but it may also be used to prevent undesirable behaviors (Lyon, 2001). As surveillance scholars from

Foucault (1979) to Vaz and Bruno (2003) have explained, data collection is often used to normalize

and discipline individuals. Foucaults influential work suggests that surveilled populations will

supervise and discipline themselves. Indeed, self-discipline is a goal embraced by participatory

personal data platforms focused on health interventions or worker productivity. Similar disciplinary

effects are seen when communities organize to collect data. The project Nation of Neighbors

(http://www.nationofneighbors.com)uses mobile devices to expand a community watch model of
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crime reporting, and civic data project CitySourced (http://www.citysourced.com)defines and

reports predefined categories of community problems such as homeless nuisance.

As this last example suggests, a pernicious effect of surveillance is its potential for uneven

application to marginalized and disenfranchised groups. Broad-scale recording of purchase habits,

location and movements enables data sorting and subsequent social profiling, by governments and

corporations (Curry, Phillips, & Regan, 2004). As Monahan describes it:

what is being secured are social relations, institutional structures, and culturaldispositions thatmore often than notaggravate existing social inequalities andestablish rationales for increased, invasive surveillance of marginalized groups(Monahan, 2006b, p. ix)

The social relations and institutional structures secured by participatory personal data are still

forming. The capture and control of participatory personal data are distributed, and people from

marginalized social positions may use the power to collect and analyze data to confront the

powerful. Mobile phones have been used for cop watching and counter-surveillance (Huey, Walby,

& Doyle, 2006; Institute for Applied Autonomy, 2006; Monahan, 2006a), peacekeeping and

economic development (Donner, Verclas, & Toyama, 2008), and self-exploration and play

(Albrechtslund, 2008). In these uses, participatory personal data collection technologies may fit into

a tradition of counter-surveillance or sousveillance: the subversion of observation technologies by

the less powerful to hold authorities accountable.

Research data

Data about people have long been important to health, environmental, and social sciences

research (Borgman, 2007). When systematically collected for discovery or new knowledge creation,

participatory personal data is research data. Such research data about people has traditionally been

regulated by federal guidelines such as the Belmont Report (Office of the Secretary of The National
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Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, 1979)

and Title 45 Code of Federal Regulations, Part 46 (Office for Protection of Research Subjects,

2007). These codes emphasize respect for human subjects, beneficence, and justice. A critical

component of respect, beneficence and justice is informed consent, which helps to differentiate

research data from surveillance data.

Traditions such as participatory research (PR) go farther than informed consent by arguing

that the data capture, sorting and use performed as part of knowledge discovery can be empowering

if it is conducted by the people most affected by the data: research subjects themselves (Cargo &

Mercer, 2008). PR practitioners argue that data subjects should be participants not just in data

capture, but in analysis of, and meaning-making from the data. Involvement with every stage of the

research process empowers users and helps justify tradeoffs between new knowledge production

and research risks for participants (Horowitz, Robinson, & Seifer, 2009). But while participatory

ethics foster a stronger notion of consent, they may also complicate design, data capture,

aggregation, and analysis practices. Participatory research traditions have been criticized for

gathering inaccurate data or incorporating bias. Participants who purposefully withhold sensitive

data from research projects may create problems for reliability and accuracy of results. As a variant

of participatory research, participatory personal data projects will need to grapple with all of these

challenges.

Information perspectives on participatory personal data

Three areas of traditional expertise in the information sciences privacy, information accessibility

and equity, and information management and preservation can provide insight into shaping

participatory personal data systems that enable participation, new knowledge, and discovery, rather


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than control. These information perspectives provide theoretical frameworks that help to unpack

and assess the social impacts and consequences of these emerging data.

Privacy and information policyOne traditional answer to the challenge of surveillance is found in the information science,

computer science, legal, and ethics literatures focused onprivacy. Participatory personal data projects

gather, store and process large amounts of information, creating massive databases of individuals

locations, movements, images, sound clips, text annotations, and even health data. Location

information can reveal habits and routines which are socially sensitive (would you want such

information shared with a boss or friend?) and may be linked with, or have an impact on, legally

protected medical information. There are three major branches of literature that address privacy

problems relevant to participatory personal data. The first are behavioral studies of users

interactions with personal data and data collection systems. The second are conceptual, ethical and

legal investigations into privacy as a human interest. The third are design and technical methods for

ensuring privacy protections.

Much of our understanding of current privacy norms stems from work that analyzes

peoples privacy preferences and behaviors (Altman, 1977; Capurro, 2005; Palen & Dourish, 2003).

Westins (1970) foundational research benchmarked American public opinion on privacy. Over

several decades, Westin used large surveys to confirm a spectrum from privacy fundamentalists

(very concerned) to pragmatic (sometimes concerned) to unconcerned. More recently, Sheehan

(2002) confirmed a similar spectrum among internet users. The Pew Internet & American Life

Project has produced several reports of privacy preferences based upon large U.S. surveys of adults

(Madden, Fox, Smith, & Vitak, 2007) and teenagers (Lenhart & Madden, 2007). These reports

continue to find privacy concerns, even among teens (whom popular wisdom assumes have


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abandoned privacy as a value.) Pew finds, for example, that teens practice privacy-preserving

behaviors such as limiting online information and taking an active part in identity management. A

number of information science studies have attempted to describe such behaviors using more

detailed scales for online privacy preferences. For example, both Yao et al (2007) and Buchanan et al

(2007) suggest factors by which to measure online privacy concern. Yao et al (2007) focus on

psychological variables, while Buchanan et al (2007) incorporate different social aspects of privacy

such as accessibility, physical privacy, and benefits of surrendering privacy.

A persistent problem in such surveys of privacy preferences, however, is that individuals

frequently report preferences that they dont act upon in practice. There is evidence that many

privacy studies prime respondents to think about privacy violations, making them more likely to

report privacy concerns (John, Acquisti, & Loewenstein, 2009). These studies also make problematic

assumptions that people act on a rational privacy interest (Acquisti & Grossklags, 2008). Studies that

observe peoples real-world use of systems attempt to correct for these problems. Raento et al

(2008), for example, present results from field trials of social awareness software that used smart

phones to show contacts locations, length of stay, and activities. The authors found that:

users are not worried not so much about losing their privacy rather aboutpresenting themselves appropriately according to situationally arising demands(Raento, p. 529).

This demonstrated concern for contextual privacy and identity management has been reiterated in

both theoretical (Nissenbaum, 2009) and descriptive research (boyd & Hargittai, 2010). Nissenbaum

(2004) labels this concern for fluid and variable disclosure contextual privacy and argues that its

absence not only leads to exposure, but also decreasing individual autonomy and freedom, damage

to human relationships, and eventually, degradation of democracy. Nissenbaum (2009) suggests that

individuals sense of appropriate disclosure, as well as understanding of information flow developed


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by experience within a space, contribute to individual discretion. Contextual privacy suggests that

individuals may be willing to disclose highly personal information on social networking sites because

they believe they understand the information flow of those sites (Lange, 2007).

Because of complexities and inconsistencies in individual privacy behaviors, policy and legal

researchers have sought to move away from user choices and individual risks, and towards new

regulations to encourage social protections for privacy (J. E. Cohen, 2008; Rule, 2004; Swarthout,

1967; Waldo et al., 2007). U.S. law does not interpret personal data to be owned by the subject of

those data. Instead, legal regimes give control of, and responsibility for, personal data to the

institution that collected the data (Waldo et al., 2007). Fair information practicesare the ethical

standard for collection and sharing that those institutions are asked to follow. Originally codified in

the 1970s, these practices are still considered the gold standard for privacy protection (Waldo et

al., 2007, p. 48), and they have been voluntarily adopted by other nations as well as private entities.

Fair information practices such as notice and awareness, choice and consent, access and

participation, integrity and security, and enforcement and redress can certainly apply to participatory

personal data. But if privacy concerns expand to include processes of enforcing personal boundaries

(Shapiro, 1998), negotiating social situations (Camp & Connelly, 2008; Palen & Dourish, 2003), and

portraying fluid identities (Phillips, 2002, 2005), fair information practices formulated for protecting

corporate and government data may be insufficient for personal collections.

Other researchers suggest that concerns about data capture extend beyond the protection of

individual privacy. Curry, Phillips and Regan (2004) write that data capture makes places and

populations increasingly visible or legible. Increasing knowledge about the actions of people and their

movements can lead to function creep. For example, collections of demographic data can enable

social discrimination through practices such as price gouging or delivery of unequal services. Could


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participatory personal data gathered to track an individuals health concern or document a

communitys assets be repurposed to deny health insurance or set higher prices for goods and

services?

All of this cross-disciplinary attention points to the fact that building participatory personal

data systems that protect privacy remains a challenge. Human-computer interaction research

considers ways that systems might notify or interact with users to help them understand privacy risks

(Anthony et al., 2007; Bellotti, 1998; Nguyen & Mynatt, 2002). Computer science and engineering

research innovates methods to obscure, hide, or anonymize data in order to give users privacy

options (Ackerman & Cranor, 1999; Agrawal & Srikant, 2000; Fienberg, 2006; Frikken & Atallah,

2004; Ganti, Pham, Tsai, & Abdelzaher, 2008; Iachello & Hong, 2007). Anonymization of data, in

particular, is a hotly debated issue in the privacy literature. Many scholars argue that possibilities for

re-identification of data make anonymization insufficient for privacy protection (Narayanan &

Shmatikov, 2008; Ohm, 2009). Other researchers are pursuing new anonymization techniques to

respond to these concerns (Benitez & Malin, 2010; Malin & Sweeney, 2004).

Privacy approaches for participatory personal data systems draw on a number of these

developments (Christin, Reinhardt, Kanhere, & Hollick, 2011). These include limiting sensing by

granularity, time of day, location, or social surroundings; providing capture and sharing options to

match diverse user preferences; methods to collect and contribute data without revealing identifying

information; data retention and deletion plans; and access control mechanisms. All of these methods

focus on privacy by design: building features into systems to help users manage their personal data

and sharing decisions (Spiekermann & Cranor, 2009). Privacy by design is a promising avenue of

research for participatory personal data, and advocacy organizations such as the Center for


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Democracy & Technology are currently pushing mobile application developers to take responsibility

for privacy in their design practices (Center for Democracy & Technology, 2011).

Privacy, of course, is only a relative value, and can frustrate other social goods. As Kang

(1998) points out, commerce can suffer from strong privacy rights, as there is less information for

both producers and consumers in the marketplace. Perhaps worse, truthfulness, openness, and

accountability can suffer at the hands of strict privacy protections (Allen, 2003). Research using

participatory personal data directly confronts this tradeoff between privacy, truthfulness, and

accuracy. For example, researchers are developing algorithms for participatory personal data

collection that allow users to replace sensitive location data with believable but fake data, effectively

lying within the system (Ganti et al., 2008; Mun et al., 2009). What is good for privacy may not

always be good for accuracy or accountability. Investigating privacy and policy for participatory

personal data will include weighing these tradeoffs. It will also include integrating elements from

contextual privacy and useable system design to present a range of appropriate privacy-preserving

choices without unduly burdening participants. And finally, it will mean crafting new policy

institutional as well as national to protect participants from function creep or discrimination based

on their data.

Information access and equityPrivacy is not the only research tradition in information science that can inform a discussion about

participatory personal data. These data also raise challenges for information access and equity. Who

controls data capture, analysis, and presentation? Who instigates projects and sets research goals?

Who owns the data or benefits from project data? Accumulating and manipulating information is a

form of power in a global information economy (Castells, 1999; Lievrouw & Farb, 2003).

Participatory personal data projects invoke this power by enabling previously impossible data


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gathering and interpretation. How do participatory personal data project designers, clients, and users

decide in whose hands this power will reside?

The relationship between information, power and equity has long been a topic of interest in

the information studies literature (Lievrouw & Farb, 2003). A large literature on the digital divide has

focused on access to information, and ways that social demographics limit or enhance information

access (Bertot, 2003). Participatory personal data evoke these basic questions of accessibility.

Anecdotal evidence from popular news reports suggests that hobbyist self-quantifiers are largely

American and European, white, and upper-middle class (Dembosky, 2011). Participants in health or

urban planning data projects, however, may span a much greater socio-economic range. Indeed,

mobile devices are some of the most accessible information technologies on earth (Kinkade &

Verclas, 2008), spanning national, ethnic, and socio-economic groups.

But there are challenges beyond accessibility as well. Lievrouw and Farb (2003) suggest that

researchers concerned with information equity take a different approach, emphasizing the subjective

and context-dependent nature of information needs and access, even among members of one social

group. How do participatory personal data answer context-specific information needs? When

individuals aregenerating the information in question, equity comes to hinge on who benefits from

this information capture. Will it be individuals and informal communities, or more organized

corporations and governments? Sociologists have proposed that loosely organized publics help to

balance power held by formal organizations and governments (Fish, Murillo, Nguyen, Panofsky, &

Kelty, 2011). But the rise of participatory culture has challenged this traditional understanding,

organizing publics and intermeshing them with organizations. For example, participatory personal

data projects exhibit elements of both organizations and publics. Research organizations such as

UCLAs Center for Embedded Networked Sensing (CENS, http://urban.cens.ucla.edu/) partner


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with community groups to actively recruit informal groups of participants into participatory personal

data projects. Examples include health projects which recruited young mothers not only for data

collection, but for focus groups about research design; as well as a community documentation

project which engaged neighbors in Los Angeles Boyle Heights community. Will organizations like

CENS hold the power that design, data, categories and social sorting can bring, or can it be

distributed back to the publics who collect the data? Because data collection methods using mobile

devices can range from participatory to opportunistic, it is unclear how much control individuals will

have over what data are collected, how they are stored, and what inferences are drawn.

It is important to note that increasing measures for participation does not solve problems of

power and equity. As Kreiss et al (2011) point out, there are limits on the emancipatory potential of

peer production. And participatory projects have been criticized for a range of failures, from

struggling to create true participation (Elwood, 2006) to being outright disingenuous in their

approach and goals (Cooke & Kothari, 2001). The intersection of information systems, values, and

culture is also important to consider. Cultural expectations and norms are deeply embedded into the

design of information systems, shaping everything from representation of relationships within

databases (Srinivasan, 2004, 2007) to the explanations drawn from data (Byrne & Alexander, 2006;

Corburn, 2003). The design process is never value-neutral, and questions of what, and whose, values

are embodied by software and system architecture have been controversial for decades (Friedman,

1997). Affordances built into a technology may privilege some uses (and users) while marginalizing

others. Design areas where bias can become particularly embedded include user interfaces (Friedman

& Nissenbaum, 1997), access and input/output devices (Perry, Macken, Scott, & McKinley, 1997),

and sorting and categorization mechanisms (Bowker & Star, 2000; Suchman, 1997). The

intersections between culture, meaning, and information systems have spurred researchers to


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experiment with culturally-specific databases, media archives, and information systems for

indigenous, diasporic, and marginalized communities (Boast, Bravo, & Srinivasan, 2007; Monash

University School of Information Management and Systems, 2006; Srinivasan, 2007; Srinivasan &

Shilton, 2006). Such alternative design projects seek to investigate, expose, redirect, or even

eliminate biases that arise in mainstream design projects (Nieusma, 2004).

Participatory personal data projects, however, often adopt a universal rather than relativist

vision, taking everyone as its intended users. What does it mean to design for everyone? As

Suchman (1997) points out, designing technology is the process of designing not just artifacts, but

also the practices that will be associated with those artifacts. What do designers, implicitly or

explicitly, intend the practices associated with participatory personal data projects to be? And how

will such practices fit into, clash against, or potentially even reshape diverse cultural contexts?

Management, curation and preservationPrivacy, access and equity challenges are all affected by an overarching information concern: how

participatory personal data projects are managed, curated, and preserved. Metadata creation and

ongoing management are necessary to ensure the access control, filtering, and security necessary to

maintain privacy for participatory personal data. Accessibility and interpretability of the data by

individuals as well as governments and corporations will be dependent upon its organization,

retrieval, and visualization. And whether and how data are preserved or forgotten will be

dependent upon curation mechanisms heavily reliant on metadata and data structures (Borgman,

2007).

Participatory personal data echo many of the same management concerns found in large

scientific datasets (D. Cohen, Fraistat, Kirschenbaum, & Scheinfeldt, 2009; Gray et al., 2005; Hey &

Trefethen, 2005). Participatory personal data may consist of large quantities of samples recorded


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every second or minute for days or months. The data are frequently quantitative measurements

dependent upon machine processing and descriptive metadata for human comprehension. These

characteristics require new techniques for organization and management. Developing such methods

for organizing, analyzing, and extracting meaning from large, diverse and largely quantitative datasets

is an emerging challenge for information sciences (Borgman, Wallis, Mayernik, & Pepe, 2007).

Always-on, sensitive data capture brings up a number of theoretical and normative questions

about whether and how these data should persist over time. Where and how will these data be

curated and preserved? What are the benefits of preserving peoples movements, habits, and

routines? And what problems might the ubiquitous nature of this memory raise? Participatory

personal data present an institutional and logistical challenge for preservation. Like all digital

material, methods for long-term preservation are costly and labor-intensive (Galloway, 2004). The

distribution of these data across multiple stakeholders, including individuals, research organizations,

corporations and governments, also challenges traditional preservation models based upon clearly-

defined collecting institutions (Abraham, 1991; Lee & Tibbo, 2007). It is also unclear what

institutions will be responsible for preserving data held by individuals and loosely-organized publics.

Determining who is responsible for authoring, managing, and curating data distributed among

individuals will challenge our existing notions of institutional data repositories and professional data

management.

Perhaps more difficult is the question of whether we should preserve participatory personal

data at all. Historically, a major role of archival institutions was selecting records, keeping only a tiny

portion of records deemed historically valuable (Boles, 1991; Cook, 1991). But the explosion of data

generation paired with cheap storage and cloud computing raises the possibility of saving much

more evidence of daily life. This possibility has become a subject of both celebration (Bell &


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Gemmell, 2007) and debate (Blanchette & Johnson, 2002). Collections of granular personal data

have been invoked to promise everything from improved health care (Hayes et al., 2008, 2007) to

memory banks that allow one to vividly relive an event with sounds and images, enhancing

personal reflection (Bell & Gemmell, 2007, p. 58). And new kinds of personal documentation could

help to counteract the power structures that control current archival and memory practices, in which

the narratives of powerful groups and people are reified while others are marginalized (Ketelaar,

2002; McKemmish, Gilliland-Swetland, & Ketelaar, 2005; Shilton & Srinivasan, 2007).

But as more data are collected and indefinitely retained, there may be pernicious social

consequences. Blanchette and Johnson (2002) point out that U.S. law has instituted a number of

social structures to aid in social forgetting or enabling a clean slate. These include bankruptcy law,

credit reports and the clearing of records of juvenile offenders. As information systems increasingly

banish forgetting, we may face the unintended loss of the fresh start. Drawing on this argument,

Bannon (2006) suggests that building systems that forget might encourage new forms of creativity.

He argues that an emphasis on augmenting one human capacity, memory, has obscured an equally

important capacity: that of forgetting. He proposes that designers think about ways that information

systems might serve as forgetting support technologies (2006, p. 5). Mayer-Schoenberger (2007)

presents a similar argument, advocating for a combination of policies and forgetful technologies that

would allow for the decay of digital data. Information professionals interested in questions of data

preservation will find difficult challenges for appraisal and curation in participatory personal data.

Determining the nature and organization of the cyberinfrastructure that will support participatory

personal data will affect many of these questions about privacy, equity, and memory.


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Next Steps

Participatory personal data, as objects of inquiry, present a range of interesting questions for the

information sciences. Participatory personal data coexist with broad data surveillance by

corporations and governments, and may be used towards pernicious ends. But with their emphasis

on participation and targeted capture, participatory personal data may simultaneously give people

their own ways to use data collection tools and platforms. Much of the social impact of participatory

personal data will depend on how data are captured and organized; who has access; whether

individuals consent and participate; and how (or whether) data are curated and preserved.

These issues are ripe for investigation by IS researchers. Scholars focused on privacy might

design and field test privacy-friendly participatory data systems. They could conduct user studies to

evaluate how participatory personal data project participants understand and use their privacy

choices. IS researchers could investigate the sensitivity of various participatory personal data, or

study how combining multiple data collections might complicate privacy concerns. Or they might

use conceptual analysis to answer the challenge of how to incorporate contextual privacy principles

into pervasive systems that span social contexts.

Researchers focused on access and equity can analyze power and participation in existing

and emerging participatory personal data. They might collect demographic information on the

populations participating in, and affected by, participatory personal data projects. IS researchers

could interview stakeholders and understand the mix of organizational and informal publics

involved in data collection projects. They could question and critique the usefulness of participatory

personal data, or they might establish guidelines for making data collection efforts truly

participatory.


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Investigators in the areas of data management, curation and preservation should undertake

the difficult cyberinfrastructure questions raised by participatory personal data. Ethnographies can

reveal the conditions under which participatory personal data project organizers choose open source

or proprietary designs. Social network analysis might help to understand the ways that management

techniques or curation mechanisms spread. And philosophical inquiry into memory and forgetting

can help to answer the normative questions of which data should be actively curated, and which data

are better left to digital obscurity.

Finally, these areas of inquiry may proceed simultaneously, but as is clear from the

intersections of law, policy, social theory, and data management, these questions span diverse areas

within our field and between fields. Continued interdisciplinary conversation can enable findings

from conceptual research to impact system design; empirical findings to affect data management;

and experience from data management and curation to influence social theory. Conversations in

journals such as JASIST are one part of this equation; so too are interdisciplinary conferences and

continued funding collaborations. By querying and shaping how participatory personal data are

organized and managed; how privacy, consent, and participation are handled in pervasive systems;

how participatory personal data affect the balance of power in an information economy; and how

such data impact social and institutional memory and forgetting, information scholars can help to

shape this emerging information landscape through building systems, constructing information

policy, and shaping values in participatory personal data system design.

Acknowledgements

This work is based on material compiled for my doctoral dissertation, Building Values into the

Design of Pervasive Mobile Technologies. Many thanks to my committee: Jeffrey Burke, Deborah

Estrin, Christopher Kelty, Ramesh Srinivasan, and chair Christine Borgman. Their ideas, feedback


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and guidance have shaped this work immensely. Thanks also to Jillian Wallis, Laura Wynholds, and

Jes Koepfler for comments and suggestions on drafts, and to the anonymous reviewers for their

helpful feedback. This work was funded by the National Science Foundation under grant number

0832873.

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Documents

Participatory Personal Data