Towards Effective Ethical BehaviorDesign

Rodrigo de Oliveira∗

Telefonica ResearchPl. de Ernest Lluch i Martın 508019 - Barcelonaoliveirar@google.com

Juan Pablo CarrascalUniversitat Pompeu FabraCarrer de Tanger, 122-14008018 - Barcelonajp.carrascal@upf.edu

∗ The author is currently affiliated with Google Inc., USA.

Permission to make digital or hard copies of part or all of this work for personalor classroom use is granted without fee provided that copies are not made ordistributed for profit or commercial advantage and that copies bear this noticeand the full citation on the first page. Copyrights for third-party componentsof this work must be honored. For all other uses, contact the owner/author(s).Copyright is held by the author/owner(s).CHI 2014 , April 26–May 1, 2014, Toronto, Ontario, Canada.ACM 978-1-4503-2474-8/14/04.http://dx.doi.org/10.1145/2559206.2581182

AbstractMany of today’s persuasive systems are designed takinginto account cognitive biases to foster positive changes inpeople’s behavior (e.g. adopt greener attitudes).However, these biases are also exploited to shape theusers’ behavior in a way that not necessarily benefit them(e.g. user retention in a website). Scholars addressed thisproblem by developing design guidelines and methods forethics in persuasive computing, but these approachesalone have proved to be inefficient since they require everydesigner to be aware, understand, and comply with therecommended ethical practices. We propose preventiveapproaches that shall support higher compliance, as wellas a remediation-based approach that does not requirecompliance from every designer. These approaches aim tohelp users understand persuasive elements embedded insystems, as well as to take more rational decisions wheninteracting with them. We expect that using preventiveand remediation-based approaches will more effectivelyimplement ethics in behavior design.

Author KeywordsPersuasive computing; Ethics; Cognitive biases

ACM Classification KeywordsH.5.m [Information Interfaces and Presentation (e.g.HCI)]: Miscellaneous; K.7.4 [The Computing Profession]:Professional Ethics

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MotivationOne of the greatest faculties of the human brain is theability to reduce the enormous amount of informationsurrounding us to a proper size for processing and yetkeeping relevant information as proved by millions of yearsof adaptation. However, this extraordinary gift is subjectto a number of cognitive biases that can lead people totake decisions not in their best interest. Some examplesinclude people’s frequent poor dietary choices andsedentary lifestyle [6], which can cause several illnesses,such as diabetes and heart disease.

Understanding and changing these behaviors have beenthe main research topic of a large body of work inpersuasive computing [12]. Many scholars have leveragedpersuasive techniques to shape people’s behavior forpreferable outcomes. For example, Arroyo et al. [7]developed Waterbot, a system that can be installed onhousehold faucets to motivate people to turn off the tapwhen the water is not being used. Green attitudes havealso been promoted for sustainable uses of energyresources [21]. In preventive health, Oliveira el al. [11]developed a mobile social game to help patients becomemore adherent to their medication prescription.Lee et al. [16] evaluated several persuasion techniques topromote healthier eating habits in the workplace,including the default bias and planning strategies.Consolvo et al. [9] used the addictiveness of game playingto fight obesity, And related to privacy, Wang et al. [20]proposed man-in-the-middle-like technologies to mitigatebiases when sharing information in online social networks.

These are only a few examples highlighting how proficientour scientific community has become in fostering positivechanges in people’s behavior. However, in many cases it isnot clear what a positive behavior change is. Should usersbe persuaded to spend more time checking their onlinesocial network feeds? Should they be influenced to buy

more items in an e-commerce website? Designers of thesetechnologies might think so, but it is not clear that thesetarget behaviors are positive for users. Ethical1 issues arisewhen technologies are designed to shape the users’behavior towards a target not intentionally defined bythem. As pointed out by Smids [17], the voluntarinesscondition is key and must consider external influences andwhether the user acts intentionally.

In persuasive computing, scholars have proposedguidelines and methodologies to support designers inethical behavior shaping. Berdichevsky andNeuenschwander [8] were among the first researchers topropose guidelines, suggesting in their golden rule ofpersuasion that “creators of a persuasive technologyshould never seek to persuade a person or persons ofsomething they themselves would not consent to bepersuaded to do”. In a similar attempt, Fogg [12]suggested 7 steps for designers to evaluate the ethicalnature of a persuasive technology by examining itsintentions, methods and outcomes. Discourse ethics wasproposed to search for further guidelines in the field [18].Several methodologies for ethical design have beenproposed, including value sensitive design [10], persuasionprofiling [14], and deep involvement with stakeholders andusers [15]. And in personal informatics, systems havebeen designed to encourage and support self-reflectionand self-behavior management [4].

Although very enlightening, the aforementioned principledapproaches depend on designers’ awareness,understanding, and commitment to ethical practices.However, there is a number of examples revealing thatdesigners have not embraced such practices [1]. Webelieve there is an urgent need to combine today’s

1In this paper we refer to ethical design as the process bywhich designers create persuasive technologies following guidelinesand methodologies suggested for ethical behavior shaping [8, 12].

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principled-based approaches with more effective solutionsto implement ethics in persuasive computing.

Proposed ApproachesIn self-beneficial behavior shaping, designers apply variouspersuasive techniques to shape the users’ behaviortowards targets defined by the users (e.g., eat healthier,quit smoking). In these cases, designers are not requiredto remove biases, but they rather introduce new ones,supposedly “stronger”, to help users change some of theirundesired behaviors.

Figure 1: Mock up of apersuasion summary labeldescribing persuasive elementsused by a website that sells flighttickets. The label could bepresented on the website, inonline application stores, orattached to the physical softwarebox sold in physical stores. Moredetails on the list of inappropriatedesign patterns can be found in[1].

However, this does not apply to cases where users do notknow how they should best behave and would like to takea more rational decision about it (e.g. deciding whetherto use a certain service more often or not). While somedesigners have addressed this user need supportingself-reflection for unbiased decision making [4], othershave designed technologies that shape the user’s behaviorfor their own benefit [1]. From an analysis of previouswork in the field and today’s motivations for buildingpersuasive technologies, we highlight three main issuesthat should be addressed in order to effectively supportethical behavior design:

1. Lack of Awareness: Today’s approaches are based onproviding awareness of guidelines and methodologies forethical behavior design, mostly in scientific journals andconferences (e.g. [8, 12, 10, 14, 18, 15, 4]). However, notevery designer has access to these channels or isproactively engaged in the research community.

2. Lack of Understanding: As noted by Torning et al. [19],ethical considerations have been often mentioned, but notclearly addressed. We further highlight that not only it isunclear how designers should best apply related theoreticalconcepts in commercial products, but also that consumersare not knowledgeable about how persuasive technologies

shape their behavior. Hence why some designers came upwith initiatives to collectively create consumer-targetededucational content [1].

3. Lack of Commitment: By disregarding ethics inbehavior design and leveraging persuasive techniques toincrease user base and user retention, designers were ableto quickly increase their profits [13]. Hence, designershave a conflict-of-interest that prevent them fromcommitting to ethical practices.

Next we propose three approaches to address these issues.

Enforced PreventionPreventive approaches that enforce the use of ethicalguidelines [8, 12] could leverage the influence of regulatoryentities, such as the government or organizations withdecision power to authorize and deny commercialization oftechnologies in a certain territory. Specialized committeescould be created to evaluate ethics of persuasivetechnologies. These evaluations should yieldconsumer-targeted summaries with information about thepersuasive techniques used in each technology studied bythe committee. These summaries could be designed basedon consumer labeling efforts capable of presentingcomplex information in a concise and easy to understandformat, such as the “Nutrition Facts” label [2]. Figure 1shows an example of our proposed design.

The main advantages of this approach include: (1)making consumers aware of ethical issues in persuasivetechnologies, (2) providing them with the tools tounderstand and judge between competing technologies,and (3) ensuring the designers’ commitment to ethicalpractices by means of regularization. However, it is limitedby the complexity and time demand for proposing,discussing, voting, and implementing regulation laws thatprevent bad cases of behavior design.

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Actively Encouraged PreventionThe aforementioned approach can be envisioned in ascenario where designers are not obliged to adhere toethical guidelines, but rather actively encouraged to doso. By active encouragement, we mean to ease andmotivate the adoption of ethical design practices and thegeneration of consumer-targeted summaries.

18:35

Apps

CERTIFIEDFREE PAID

2. Rent-a-Bike

4. Wrecking Ball

3. Politan Metro

Rent-a-Vehicle

Damage Inc.

City Way

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$0.99

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1. My Little CloudLittle Gems Soft.

$5.95

Figure 2: Example of activeencouragement to ethicalbehavior design. The mobilesearch interface groups results bycertified applications, i.e. appsevaluated by an expert committeeand certified to meet minimumrequirements for ethical behaviordesign. This special categorygives higher visibility to designersof these apps, while also enablingconsumers to enter a space wherethey feel safe when searching anddownloading applications.

In that sense, designers should have easy access toguidelines and methodologies for ethical behavior design,as well as to organizations that evaluate compliance tothese ethical practices. In addition, consumers should beable to easily identify and check whether technologiesmeet requirements, e.g. with visual cues, like those fromcertified websites in the internet security domain [5].

In terms of motivation, designers’ compliance to ethicalpractices could be rewarded by displaying certificationstamps next to names of websites or applications—listedin search results—that were awarded the certification.Similarly, online application stores could include a specificcategory for searching “certified apps”, thus supportinghigher visibility of apps that comply with ethical designpractices, as well as allowing consumers to enter a spacewhere they feel comfortable searching and downloadingapplications (see Figure 2). Once these certificationsbecome ubiquitous, we expect consumers to givepreference for using certified persuasive technologies, andhence companies shall be more motivated to followguidelines from persuasive computing. Further research isneeded to identify appropriate incentives for the firstadopters of these certifications, such as tax deductions,privileged governmental partnerships, among others.

Although actively encouraged prevention is not limited bythe lengthy process of establishing regulation laws, it addsextra complexity for implementing different incentive

schemes and does not guarantee commitment from everydesigner.

Remediation-based ApproachPreventive approaches are not perfect, and henceconsumers might be always surrounded by technologiesthat do not comply with ethical design guidelines. Wepropose a remediation-based approach to empower usersto reveal and remove—or at least mitigate—biases inpersuasive technologies. We envision its implementationby groups of designers that implement adaptive solutionsfor technologies that misuse behavior design. Threeactivities shall be conducted by these groups:

1. Identify the most relevant biases in the givenpersuasive technology;

2. Provide interactive mechanisms that enable users toreveal these biases for self-awareness andeducational purposes;

3. Provide intervention methods that empower users toremove or mitigate the effect of existing biases.

user designer

ethical designer

I benefit if user does B

Should I do A or B?

biasing user to do B

design for B

biases mitigated

less biased decision between A and B

layer persuasive technology

identify biases

empower user to reveal and remove (or mitigate) biases

Figure 3: Schematic of our remediation-based approach.

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An schematic representation of our approach is presentedin Figure 3. It does not require commitment from everydesigner, like in [8, 12, 10, 14, 18, 15]. Smaller groups ofethically-conscious designers shall be able to implement itas an extra layer interfacing users and today’s persuasivetechnologies. Next we present a few examples about howthis remediation-based approach can be realized.

Example 1: Mobile. Consider a user that is thinkingabout temporarily disabling his/her phone’s GPS sensordue to privacy concerns. Many of today’s smartphonesintroduce biases that prevent this behavior. In some cases,the functionality is somewhat “hidden” in the settings’menu, requiring more effort on the user’s side to find it(mental/time demand). In other cases, a discouragingmessage is displayed after disabling the sensor, furtherasking for the user’s confirmation (loss aversion2). Ourapproach could be implemented by designers—notnecessarily related to the phone maker—that identify thepresence of the given biases, and develop a mobileapplication to empower users to reveal and remove thesebiases. In that sense, the mobile application could offer aneasy and fast way for users to turn the GPS sensor on andoff, such as through a shortcut button or a gesture basedinteraction.

Example 2: Web. Let us use the famous example ofdefault bias in web forms [6] to exemplify our approach.Consider a user that is buying a flight ticket online using adesktop computer. After s/he makes the flightreservation, the website automatically adds a specialcustomer service for e30, expecting that the default biaswill discourage the user from opting-out the service. In

2Loss aversion bias affects how we think about our possessions,making the loss of giving up something more salient than the gainsof acquiring something else [6]. The message for turning the GPS offfocus on what the user loses when turning the sensor off, whereas nodiscouraging message is usually presented when turning the GPS on.

this case, designers—not necessarily related to the siteowners—could implement our approach by studying thewebsite and identifying the presence of the given defaultbias; and implementing a browser plugin that can beinstalled in the users’ computers, enabling them to revealand remove the default bias. In order to remove it, theplugin could restructure the webpage including an extramodular step that inquiries users on whether they want ornot to include the special service.

Example 3: Social Networks. Removing biases is notalways possible. In these cases, our approach can beimplemented by means of intervention methods that donot specify a target behavior. For example, should usersbrowse social network websites for longer periods of timethan they currently do? In this case, our approach couldbe implemented by revealing the main biases that increaseuser retention time, and enabling users to activateinterventions that mitigate these biases. An example ofintervention is to provide daily summaries about the user’stime spent in these websites. Other interventions that gobeyond self-reflection could prove to be more effective andshould be systematically tested, thus opening differentavenues of research in persuasive computing.

Conclusions and Future WorkIn this paper we proposed preventive andremediation-based approaches to more effectivelyimplement ethics in behavior design. We discussed thescope of each of these approaches, highlighting theiradvantages and disadvantages. We focused our proposalon a remediation-based approach that can be implementedby small groups of ethically-conscious designers, hence notrequiring compliance from every designer, like in most oftoday’s approaches [8, 12, 10, 14, 15].

Many questions remain open for future research anddiscussion. Besides evaluating our remediation-based

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approach, we would like to investigate to what degree ofautomation it would be more successful. Although we givefocus on a manual procedure for inspection of persuasivetechnologies conducted by a committee of designers, onecould envision a hybrid approach that automaticallyidentify certain biases for later manual inspection.

Another topic for discussion is the qualification requiredto be a designer of persuasive technologies. One couldenvision designers being certificated by authorities ororganizations trusted for implementing appropriateguidelines and methods, in a similar way that it is done forother domains, like internet security [5] or qualitymanagement systems [3]. Alternatively, groups ofdesigners could build their reputation by alerting users ofbiases present in the services they use everyday [1], andthus avoid going through a potentially long certificationprocess. We believe both cases have potential whendesigners further provide interactive mechanisms thateducate users in revealing biases in technologies, andempower users to remove or mitigate these biases.

We hope that our proposed approaches generate fruitfuldiscussions in the HCI community to effectively implementpractical solutions for ethical behavior design.

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