This study investigates the ethical use of Big Data and Artificial Intelligence (AI) technologies (BD + AI)—using an empirical approach. The paper categorises the current literature and presents a multi-case study of 'on-the-ground' ethical issues that uses qualitative tools to analyse findings from ten targeted case-studies from a range of domains. The analysis coalesces identified singular ethical issues, (from the literature), into clusters to offer a comparison with the proposed classification in the literature. The results show that despite the variety (...) of different social domains, fields, and applications of AI, there is overlap and correlation between the organisations’ ethical concerns. This more detailed understanding of ethics in AI + BD is required to ensure that the multitude of suggested ways of addressing them can be targeted and succeed in mitigating the pertinent ethical issues that are often discussed in the literature. (shrink)

This book addresses key conceptual issues relating to the modern scientific and engineering use of computer simulations. It analyses a broad set of questions, from the nature of computer simulations to their epistemological power, including the many scientific, social and ethics implications of using computer simulations. The book is written in an easily accessible narrative, one that weaves together philosophical questions and scientific technicalities. It will thus appeal equally to all academic scientists, engineers, and researchers in industry interested in questions (...) related to the general practice of computer simulations. (shrink)

In this article, we explore how digital marketers think about marketing in the age of Big Data surveillance, automatic computational analyses, and algorithmic shaping of choice contexts. Our starting point is a contradiction at the heart of digital marketing namely that digital marketing brings about unprecedented levels of consumer empowerment and autonomy and total control over and manipulation of consumer decision-making. We argue that this contradiction of digital marketing is resolved via the notion of relevance, which represents what Fredric Jameson (...) calls a symbolic act. The notion of the symbolic act lets us see the centering of relevance as a creative act of digital marketers who undertake to symbolically resolve a contradiction that cannot otherwise be resolved. Specifically, we suggest that relevance allows marketers to believe that in the age of surveillance capitalism, the manipulation of choice contexts and decision-making is the same as consumer empowerment. Put differently, relevance is the moment when marketing manipulation disappears and all that is left is the empowered consumer. To create relevant manipulations that are experienced as empowering by the consumer requires always-on surveillance, massive analyses of consumer data and hyper-targeted responses, in short, a persistent marketing presence. The vision of digital marketing is therefore a fascinating one: marketing disappears at precisely the moment when it extends throughout the life without limit. (shrink)

This study identifies and explores evolving concepts of trust and privacy in the context of user-generated health data. We define “user-generated health data” as data captured through devices or software and used outside of traditional clinical settings for tracking personal health data. The investigators conducted qualitative research through semistructured interviews with researchers, health technology start-up companies, and members of the general public to inquire why and how they interact with and understand the value of user-generated health data. We found significant (...) results concerning new attitudes toward trust, privacy, and sharing of health data outside of clinical settings that conflict with regulations governing health data within clinical settings. Members of the general public expressed little concern about sharing health data with the companies that sold the devices or apps they used, and indicated that they rarely read the “terms and conditions” detailing how their data may be exploited by the company or third-party affiliates before consenting to them. In contrast, interviews with researchers revealed significant resistance among potential research participants to sharing their user-generated health data for purposes of scientific study. The widespread rhetoric of personalization and social sharing in “user-generated culture” appears to facilitate an understanding of user-generated health data that deemphasizes the risk of exploitation in favor of loosely defined benefits to individual and social well-being. We recommend clarification and greater transparency of regulations governing data sharing related to health. (shrink)

Though rapid collection of big data is ubiquitous across domains, from industry settings to academic contexts, the ethics of big data collection and research are contested. A nexus of data ethics issues is the concept of creep, or repurposing of data for other applications or research beyond the conditions of original collection. Data creep has proven controversial and has prompted concerns about the scope of ethical oversight. Institutional review boards offer little guidance regarding big data, and problematic research can still (...) meet ethical standards. While ethics seem concrete through institutional deployment, I frame ethics as produced. Informed by my ethnographic research at a large public university in the U.S., I explore ethics through two models: ethics as institutional procedures and ethics as acts and intentions. The university where I conducted fieldwork is the development grounds for a predictive model that uses student data to anticipate academic success. While students consent to data collection, the circumstances of consent and the degree to which they are informed are not so apparent, as many data are a product of creep. Drawing from interviews and participant observation with administrators, data scientists, developers, and students, I examine data ethics, from a larger institutional model to everyday enactments related to data creep. After demonstrating the limits of such models, I propose a remodeling of ethics that draws on recent works on data, justice, and refusal to pose generative questions for rethinking ethics in institutional contexts. (shrink)

Globally, mortality and fertility rates generally fall as resource abundance increases. This pattern represents an evolutionary paradox insofar as resource-rich ecological contexts can support higher numbers of offspring, a component of biological fitness. This paradox has not been resolved, in part because the relationships between fertility, life history strategies, reproductive behavior, and socioeconomic conditions are complex and cultural-historically contingent. We aim to understand how we might make sense of this paradox in the specific context of late-twentieth-century, mid–demographic transition Chile. We (...) use distribution-specific generalized linear models to analyze associations between fertility-related life-history traits—number of offspring, ages at first and last reproduction, average interbirth interval, and average number of live births per reproductive span year—and socioeconomic position (SEP) using data from a cohort of 6,802 Chilean women born between 1961 and 1970. We show that Chilean women of higher SEP have shorter average interbirth intervals, more births per reproductive span year, later age at first reproduction, earlier ages at last reproduction, and, ultimately, fewer children than women of lower SEP. Chilean women of higher SEP consolidate childbearing over a relatively short time span in the middle of their reproductive careers, whereas women of lower SEP tend to reproduce over the entirety of their reproductive lifespans. These patterns may indicate that different SEP groups follow different pathways toward declining fertility during the demographic transition, reflecting different life-history trade-offs in the process. (shrink)

Ethics plays a key role in the normative analysis of the impacts of technology. We know that computers in general and the processing of data, the use of artificial intelligence, and the combination of computers and/or artificial intelligence with robotics are all associated with ethically relevant implications for individuals, groups, and society. In this article, we argue that while all technologies are ethically relevant, there is no need to create a separate ‘ethics of X’ or ‘X ethics’ for each and (...) every subtype of technology or technological property—e.g. computer ethics, AI ethics, data ethics, information ethics, robot ethics, and machine ethics. Specific technologies might have specific impacts, but we argue that they are often sufficiently covered and understood through already established higher-level domains of ethics. Furthermore, the proliferation of tech ethics is problematic because (a) the conceptual boundaries between the subfields are not well-defined, (b) it leads to a duplication of effort and constant reinventing the wheel, and (c) there is danger that participants overlook or ignore more fundamental ethical insights and truths. The key to avoiding such outcomes lies in a taking the discipline of ethics seriously, and we consequently begin with a brief description of what ethics is, before presenting the main forms of technology related ethics. Through this process, we develop a hierarchy of technology ethics, which can be used by developers and engineers, researchers, or regulators who seek an understanding of the ethical implications of technology. We close by deducing two principles for positioning ethical analysis which will, in combination with the hierarchy, promote the leveraging of existing knowledge and help us to avoid an exaggerated proliferation of tech ethics. (shrink)

Data science, and the related field of big data, is an emerging discipline involving the analysis of data to solve problems and develop insights. This rapidly growing domain promises many benefits to both consumers and businesses. However, the use of big data analytics can also introduce many ethical concerns, stemming from, for example, the possible loss of privacy or the harming of a sub-category of the population via a classification algorithm. To help address these potential ethical challenges, this paper maps (...) and describes the main ethical themes that were identified via systematic literature review. It then identifies a possible structure to integrate these themes within a data science project, thus helping to provide some structure in the on-going debate with respect to the possible ethical situations that can arise when using data science analytics. (shrink)

Der Big Data-Technologie wird das Potenzial zugeschrieben, durch Mustererkennung in aggregierten Daten Verhaltensweisen von Personen zu prognostizieren, noch bevor diese intendiert und reflektiert würden. Zumeist widmet sich die Big Data-Debatte daher den Befürchtungen möglicher Einbußen von Privatheit und Freiheit. In unserem Beitrag wählen wir jedoch einen anderen Zugriff und fragen, inwiefern die Big Data-Visionen das Selbstsein betreffen, also das Konzept davon, wer ich selbst eigentlich bin. Wenn Selbstsein, Martin Heidegger zufolge, eigentlich bedeutet, in kritischer Distanz zu vorgegebenen Möglichkeiten zu leben, (...) stellt sich die Frage, welches Kriterium hier eine klare Unterscheidbarkeit gewährleistet: Wie kann ich wissen, echt ich selbst zu sein, ohne nur wiederum andere Üblichkeiten nachzuahmen? Wir fragen daher, ob die Big-Data-Technologie subjektive Verzerrungen und Täuschungen über das Selbst ausschalten könnte. Hierfür wird der Begriff „Selbstsein“ bei Heidegger erarbeitet. Dabei lässt sich allerdings zeigen, dass dieser letztlich ohne Trennschärfe bleibt, da die nur negative Charakterisierung von eigentlichem Selbstsein, Heidegger zufolge, echt oder unecht ausgestaltet sein kann – der aus der Möglichkeit echter oder unechter Selbsterzählung resultierende Relativismus wurde bisher nicht hinreichend beachtet. Big Data kann zwar uneigentliche Erzählmuster vermeiden, nicht aber uneigentliches oder eigentliches Verhalten differenzieren; ob ich z.B. manches – unecht eigentlich – aus Trotz oder Kalkül gegen mein eigenes Selbstverständnis tat, bleibt verborgen. Fazit: Es muss entweder ein Kriterium für echtes Eigentlichsein entwickelt oder der Anspruch, darüber Auskunft zu geben, verworfen werden – bei Heidegger und Big Data. Verzichtet man jedoch auf die Idee einer (echten) Eigentlichkeit verzichtet man auf den Begriff des Selbst. (shrink)

Biometric technology was once the purview of security, with face recognition and fingerprint scans used for identification and law enforcement. This is no longer the case; biometrics is increasingly used for commercial and civil applications. Due to the widespread diffusion of biometrics, it is important to address the ethical issues inherent to the development and deployment of the technology. This article explores the burgeoning research on biometrics for non-security purposes and the ethical implications for organizations. This will be achieved by (...) reviewing the literature on biometrics and business ethics and drawing from disciplines such as computer ethics to inform a more robust discussion of key themes. Although there are many ethical concerns, privacy is the key issue, with associated themes. These include definitions of privacy, the privacy paradox, informed consent, regulatory frameworks and guidelines, and discrimination. Despite the proliferation of biometric technology, there is little empirical research on applied biometrics and business ethics. As such, there are several avenues for research to improve understanding of the ethical implications of using this technology. (shrink)

Discussions about ethics of Big Data often focus on the ethics of data processing: collecting, storing, handling, analysing and sharing data. Data-based systems, however, do not come from nowhere. They are designed and brought into being within social spaces – or social milieu. This paper connects philosophical considerations of individual and collective capacity to enact practical reason to the influence of social spaces. Building a deeper engagement with the social imaginaries of technology development through analysis of two years of fieldwork (...) with start-ups working on Internet of Things, this paper suggests that different action positions can emerge, with consequences for how data is understood and valued. The Disengaged, Pragmatist and Idealist ethical action positions identified in the paper reveal the ways individuals and groups negotiate possibilities for ethical action, through justifications, explanations and structuring of system features. (shrink)

This paper sets out the notion of a current “biopolitical turn of digital capitalism” resulting from the increasing deployment of AI and data analytics technologies in the public sector. With applications of AI-based automated decisions currently shifting from the domain of business to customer (B2C) relations to government to citizen (G2C) relations, a new form of governance arises that operates through “algorithmic social selection”. Moreover, the paper describes how the ethics of AI is at an impasse concerning these larger societal (...) and socioeconomic trends and calls for an ethics of AI that includes, and acts in close alliance with, social and political philosophy. As an example, the problem of Predictive Analytics is debated to make the point that data-driven AI (Machine Learning) is currently one of the main ethical challenges in the ethics of AI. (shrink)

Data platforms represent a new paradigm for carrying out health research. In the platform model, datasets are pooled for remote access and analysis, so novel insights for developing better stratified and/or personalised medicine approaches can be derived from their integration. If the integration of diverse datasets enables development of more accurate risk indicators, prognostic factors, or better treatments and interventions, this obviates the need for the sharing and reuse of data; and a platform-based approach is an appropriate model for facilitating (...) this. Platform-based approaches thus require new thinking about consent. Here we defend an approach to meeting this challenge within the data platform model, grounded in: the notion of ‘reasonable expectations’ for the reuse of data; Waldron’s account of ‘integrity’ as a heuristic for managing disagreement about the ethical permissibility of the approach; and the element of the social contract that emphasises the importance of public engagement in embedding new norms of research consistent with changing technological realities. While a social contract approach may sound appealing, however, it is incoherent in the context at hand. We defend a way forward guided by that part of the social contract which requires public approval for the proposal and argue that we have moral reasons to endorse a wider presumption of data reuse. However, we show that the relationship in question is not recognisably contractual and that the social contract approach is therefore misleading in this context. We conclude stating four requirements on which the legitimacy of our proposal rests. (shrink)

The importance of digital technologies for social and economic developments and a growing focus on data collection and privacy concerns have made the Internet a salient and visible issue in global politics. Recent developments have increased the awareness that the current approach of governments and business to the governance of the Internet and the adjacent technological spaces raises a host of ethical issues. The significance and challenges of the digital age have been further accentuated by a string of highly exposed (...) cases of surveillance and a growing concern about issues of privacy and the power of this new industry. This special issue explores what some have referred to as the “Internet-industrial complex”—the intersections between business, states, and other actors in the shaping, development, and governance of the Internet. (shrink)

The algorithmic accountability literature to date has primarily focused on procedural tools to govern automated decision-making systems. That prescriptive literature elides a fundamentally empirical question: whether and under what circumstances, if any, is the use of algorithmic systems to make public policy decisions perceived as legitimate? The present study begins to answer this question. Using factorial vignette survey methodology, we explore the relative importance of the type of decision, the procedural governance, the input data used, and outcome errors on perceptions (...) of the legitimacy of algorithmic public policy decisions as compared to similar human decisions. Among other findings, we find that the type of decision—low importance versus high importance—impacts the perceived legitimacy of automated decisions. We find that human governance of algorithmic systems increases perceptions of the legitimacy of algorithmic decision-making systems, even when those decisions are likely to result in significant errors. Notably, we also find the penalty to perceived legitimacy is greater when human decision-makers make mistakes than when algorithmic systems make the same errors. The positive impact on perceived legitimacy from governance—such as human-in-the-loop—is greatest for highly pivotal decisions such as parole, policing, and healthcare. After discussing the study’s limitations, we outline avenues for future research. (shrink)

Firms use algorithms to make important business decisions. To date, the algorithmic accountability literature has elided a fundamentally empirical question important to business ethics and management: Under what circumstances, if any, are algorithmic decision-making systems considered legitimate? The present study begins to answer this question. Using factorial vignette survey methodology, we explore the impact of decision importance, governance, outcomes, and data inputs on perceptions of the legitimacy of algorithmic decisions made by firms. We find that many of the procedural governance (...) mechanisms in practice today, such as notices and impact statements, do not lead to algorithmic decisions being perceived as more legitimate in general, and, consistent with legitimacy theory, that algorithmic decisions with good outcomes are perceived as more legitimate than bad outcomes. Yet, robust governance, such as offering an appeal process, can create a legitimacy dividend for decisions with bad outcomes. However, when arbitrary or morally dubious factors are used to make decisions, most legitimacy dividends are erased. In other words, companies cannot overcome the legitimacy penalty of using arbitrary or morally dubious factors, such as race or the day of the week, with a good outcome or an appeal process for individuals. These findings add new perspectives to both the literature on legitimacy and policy discussions on algorithmic decision-making in firms. (shrink)

The importance of data literacy and the need of raising and improving it through formal educational channel or public engagement has repeatedly been flagged up in each of the past Economic and Social Research Council-funded Data-Psst! Seminar I attended in 2014–2016. There is a real demand for action taking. I took advantage of the knowledge I learned from the Data-Psst seminars and devised a module teaching Level 5 undergraduate media students about critical issues in today’s data-centric digital society, including privacy (...) and surveillance. In this article, I share how the class activities were devised and carried out, and how guided engagement with the current debate in privacy and surveillance were realised. I also draw on relevant pedagogical theories to discuss my educational approaches, student performance, the challenges of the project, and evaluate and reflect upon the outcomes. This report from the field provides fresh first-hand information about the data ethics of the younger public who are practising media arts and their behaviours and attitudes towards privacy and surveillance. This article shall open up the discussion about the role educators play in enriching public engagement with critical thinking about Big Data. The lessons learned can also contextualise the pedagogical implication of the recent scholarly research on Big Data and privacy, and provide a framework for constructing future collaborative or creative projects. (shrink)

With a mass digitisation programme underway and the addition of non-print legal deposit and web archive collections, the National Library of Scotland is now both producing and collecting data at an unprecedented rate, with over 5PB of storage in the Library’s data centres. As well as the opportunities to support large scale analysis of the collections, this also presents new challenges around data management, storage, rights, formats, skills and access. Furthermore, by assuming the role of both creators and collectors, libraries (...) face broader questions about the concepts of ‘collections' and ‘heritage', and the ethical implications of collecting practices. While the ‘collections as data’ movement has encouraged cultural heritage organisations to present collections in machine-readable formats, new services, processes and tools also need to be established to enable these emerging forms of research, and new modes of working need to be established to take into account an increasing need for transparency around the creation and presentation of digital collections. This commentary explores the National Library of Scotland's new digital scholarship service, the implications of this new activity and the obstacles that libraries encounter when navigating a world of Big Data. (shrink)

ABSTRACTThe ubiquity of digital communication channels such as social media platforms, video sites, and mobile apps has transformed the relational experience between audiences and brand entities. Whether engaging within a consumer goods market, a professional services industry, or a news and infotainment source, audiences and brand entities have inhabited a new ecosystem that has challenged the acceptance of the classic purchase funnel model pioneered by Elias St. Elmo Lewis. This article reviews the conception of a postpurchase funnel media environment and (...) seeks to moralize its transaction-driven expediency in 3 ways. First, it explores the legitimacy of an audience-centric ecosystem as a theoretical substitute for the purchase funnel model. Second, it introduces MacIntyre’s concept of virtue ethics as a grounding moral philosophy in the postpurchase funnel environment. Finally, it reviews two cases of postpurchase funnel marketing abuses and recommends strategies that commit brand entities to a bilatera... (shrink)

This contribution discusses the development of the Data Ethics Decision Aid (DEDA), a framework for reviewing government data projects that considers their social impact, the embedded values and the government’s responsibilities in times of data-driven public management. Drawing from distinct qualitative research approaches, the DEDA framework was developed in an iterative process (2016–2018) and has since then been applied by various Dutch municipalities, the Association of Dutch Municipalities, and the Ministry of General Affairs (NL). We present the DEDA framework as (...) an effective process to moderate case-deliberation and advance the development of responsible data practices. In addition, by thoroughly documenting the deliberation process, the DEDA framework establishes accountability. First, this paper sheds light on the necessity for data ethical case deliberation. Second, it describes the prototypes, the final design of the framework, and its evaluation. After a comparison with other frameworks, and a discussion of the findings, the paper concludes by arguing that the DEDA framework is a useful process for ethical evaluation of data projects for public management and an effective tool for creating awareness of ethical issues in data practices. (shrink)

Our synthetic review of the relevant and related literatures on the ethics and effects of using AI in education reveals five qualitatively distinct and interrelated divides associated with access, representation, algorithms, interpretations, and citizenship. We open our analysis by probing the ethical effects of algorithms and how teams of humans can plan for and mitigate bias when using AI tools and techniques to model and inform instructional decisions and predict learning outcomes. We then analyze the upstream divides that feed into (...) and fuel the algorithmic divide, first investigating access and then representation. After that, we analyze the divides that are downstream of the algorithmic divide associated with interpretation and citizenship. At present, lacking ongoing reflection and action by learners, educators, educational leaders, designers, scholars, and policymakers, the five divides collectively create a vicious cycle and perpetuate structural biases in teaching and learning. However, increasing human responsibility and control over these divides can create a virtuous cycle that improves diversity, equity, and inclusion in education. We conclude the article by looking forward and discussing ways to increase educational opportunity and effectiveness for all by mitigating bias through a cycle of progressive improvement. (shrink)

The legal basis for processing personal data and some other types of Big Data is often the informed consent of the data subject involved. Many data controllers, such as social network sites, offer terms and conditions, privacy policies or similar documents to which a user can consent when registering as a user. There are many issues with such informed consent: people get too many consent requests to read everything, policy documents are often very long and difficult to understand and users (...) feel they do not have a real choice anyway. Furthermore, in the context of Big Data refusing consent may not prevent predicting missing data. Finally, consent is usually asked for when registering, but rarely is consent renewed. As a result, consenting once often implies consent forever. At the same time, given the rapid changes in Big Data and data analysis, consent may easily get outdated. This paper suggests expiry dates for consent, not to settle questions, but to put them on the table as a start for further discussion on this topic. Although such expiry dates may not solve all the issues of informed consent, they may be a useful tool in some situations. (shrink)

There are growing discontinuities between the research practices of data science and established tools of research ethics regulation. Some of the core commitments of existing research ethics regulations, such as the distinction between research and practice, cannot be cleanly exported from biomedical research to data science research. Such discontinuities have led some data science practitioners and researchers to move toward rejecting ethics regulations outright. These shifts occur at the same time as a proposal for major revisions to the Common Rule—the (...) primary regulation governing human-subjects research in the USA—is under consideration for the first time in decades. We contextualize these revisions in long-running complaints about regulation of social science research and argue data science should be understood as continuous with social sciences in this regard. The proposed regulations are more flexible and scalable to the methods of non-biomedical research, yet problematically largely exclude data science methods from human-subjects regulation, particularly uses of public datasets. The ethical frameworks for Big Data research are highly contested and in flux, and the potential harms of data science research are unpredictable. We examine several contentious cases of research harms in data science, including the 2014 Facebook emotional contagion study and the 2016 use of geographical data techniques to identify the pseudonymous artist Banksy. To address disputes about application of human-subjects research ethics in data science, critical data studies should offer a historically nuanced theory of “data subjectivity” responsive to the epistemic methods, harms and benefits of data science and commerce. (shrink)

Social media offers an attractive site for Big Data research. Access to big social media data, however, is controlled by companies that privilege corporate, governmental, and private research firms. Additionally, Institutional Review Boards’ regulative practices and slow adaptation to emerging ethical dilemmas in online contexts creates challenges for Big Data researchers. We examine these challenges in the context of a feminist qualitative Big Data analysis of the hashtag event #WhyIStayed. We argue power, context, and subjugated knowledges must each be central (...) considerations in conducting Big Data social media research. In doing so, this paper offers a feminist practice of holistic reflexivity in order to help social media researchers navigate and negotiate this terrain. (shrink)

Big data represents a significant challenge for the social sciences. From a philosophy-of-science perspective, it is important to reflect on related theories and processes for developing them. In this paper, we start by examining different views on the role of theories in big data-related social research. Then, we try to show how big data is related to standards for evaluating theories. We also outline how big data affects theory- and data-based research approaches and the process of theory building. Discussions include (...) a summary of lines of reasoning, limitations, and a proposal for future developmental steps. (shrink)

Integrative Social Contracts Theory is a popular framework to assist managers in making decisions on international moral dilemmas. Although the theory has been praised for its comprehensiveness and sophistication, commentators have raised concerns regarding the justification and identification of substantive hypernorms, fundamental moral principles valid across cultures. This paper introduces the deliberative test, a new method for testing the cross-cultural validity of ethical norms in ISCT. The test relies on the concept of Deliberative Capacity, arising from new developments in system-level (...) deliberative democracy research, which studies deliberation at the level of national societies and over time. Based on this recent stream of research, the deliberative test has the potential of providing better guidance for managers to decide which norms to follow in case of conflicting standards between home and host countries. (shrink)

In this 60th anniversary of Business & Society essay, we seek to make three main contributions at the intersection of governance and artificial intelligence. First, we aim to illuminate some of the deeper social, legal, organizational, and democratic challenges of rising AI adoption and resulting algorithmic power by reviewing AI research through a governance lens. Second, we propose an AI governance framework that aims to better assess AI challenges as well as how different governance modalities can support AI. At the (...) heart of our framework lies the governance forces that apply to institutions, organizations, and individuals, who ultimately provide, regulate, and use AI decision-making. We discuss how businesses may harness AI’s economic power through governance solutions without creating or amplifying societal biases and inequalities. Third, as part of our section on future research, we identify a set of governance trade-offs in AI adoption, suggest future research avenues to conceptually strengthen research on the governance of AI, and lay out key policy recommendations. (shrink)

In this panel, we explore the future of value sensitive design (VSD). The stakes are high. Many in public and private sectors and in civil society are gradually realizing that taking our values seriously implies that we have to ensure that values effectively inform the design of technology which, in turn, shapes people’s lives. Value sensitive design offers a highly developed set of theory, tools, and methods to systematically do so.