Self-interest and data protection drive the adoption and moral acceptability of big data technologies: A conjoint analysis approach

Curated on Posted on by Stefaan Verhulst

Paper by Rabia I.Kodapanakka, lMark J.Brandt, Christoph Kogler, and Iljavan Beest: “Big data technologies have both benefits and costs which can influence their adoption and moral acceptability. Prior studies look at people’s evaluations in isolation without pitting costs and benefits against each other. We address this limitation with a conjoint experiment (N = 979), using six domains (criminal investigations, crime prevention, citizen scores, healthcare, banking, and employment), where we simultaneously test the relative influence of four factors: the status quo, outcome favorability, data sharing, and data protection on decisions to adopt and perceptions of moral acceptability of the technologies.

We present two key findings. (1) People adopt technologies more often when data is protected and when outcomes are favorable. They place equal or more importance on data protection in all domains except healthcare where outcome favorability has the strongest influence. (2) Data protection is the strongest driver of moral acceptability in all domains except healthcare, where the strongest driver is outcome favorability. Additionally, sharing data lowers preference for all technologies, but has a relatively smaller influence. People do not show a status quo bias in the adoption of technologies. When evaluating moral acceptability, people show a status quo bias but this is driven by the citizen scores domain. Differences across domains arise from differences in magnitude of the effects but the effects are in the same direction. Taken together, these results highlight that people are not always primarily driven by self-interest and do place importance on potential privacy violations. They also challenge the assumption that people generally prefer the status quo….(More)”.

The Story of Goldilocks and Three Twitter’s APIs: A Pilot Study on Twitter Data Sources and Disclosure

Curated on Posted on by Stefaan Verhulst

Paper by Yoonsang Kim, Rachel Nordgren and Sherry Emery: “Public health and social science increasingly use Twitter for behavioral and marketing surveillance. However, few studies provide sufficient detail about Twitter data collection to allow either direct comparisons between studies or to support replication.

The three primary application programming interfaces (API) of Twitter data sources are Streaming, Search, and Firehose. To date, no clear guidance exists about the advantages and limitations of each API, or about the comparability of the amount, content, and user accounts of retrieved tweets from each API. Such information is crucial to the validity, interpretation, and replicability of research findings.

This study examines whether tweets collected using the same search filters over the same time period, but calling different APIs, would retrieve comparable datasets. We collected tweets about anti-smoking, e-cigarettes, and tobacco using the aforementioned APIs. The retrieved tweets largely overlapped between three APIs, but each also retrieved unique tweets, and the extent of overlap varied over time and by topic, resulting in different trends and potentially supporting diverging inferences. Researchers need to understand how different data sources can influence both the amount, content, and user accounts of data they retrieve from social media, in order to assess the implications of their choice of data source…(More)”.

How to Put the Data Subject's Sovereignty into Practice. Ethical Considerations and Governance Perspectives

Curated on Posted on by Stefaan Verhulst


Paper by Peter Dabrock: “Ethical considerations and governance approaches of AI are at a crossroads. Either one tries to convey the impression that one can bring back a status quo ante of our given “onlife”-era, or one accepts to get responsibly involved in a digital world in which informational self-determination can no longer be safeguarded and fostered through the old fashioned data protection principles of informed consent, purpose limitation and data economy. The main focus of the talk is on how under the given conditions of AI and machine learning, data sovereignty (interpreted as controllability [not control (!)] of the data subject over the use of her data throughout the entire data processing cycle) can be strengthened without hindering innovation dynamics of digital economy and social cohesion of fully digitized societies. In order to put this approach into practice the talk combines a presentation of the concept of data sovereignty put forward by the German Ethics Council with recent research trends in effectively applying the AI ethics principles of explainability and enforceability…(More)”.

Research co-design in health: a rapid overview of reviews

Curated on Posted on by Stefaan Verhulst

Paper by Peter Slattery, Alexander K. Saeri & Peter Bragge: “Billions of dollars are lost annually in health research that fails to create meaningful benefits for patients. Engaging in research co-design – the meaningful involvement of end-users in research – may help address this research waste. This rapid overview of reviews addressed three related questions, namely (1) what approaches to research co-design exist in health settings? (2) What activities do these research co-design approaches involve? (3) What do we know about the effectiveness of existing research co-design approaches? The review focused on the study planning phase of research, defined as the point up to which the research question and study design are finalised….

A total of 26 records (reporting on 23 reviews) met the inclusion criteria. Reviews varied widely in their application of ‘research co-design’ and their application contexts, scope and theoretical foci. The research co-design approaches identified involved interactions with end-users outside of study planning, such as recruitment and dissemination. Activities involved in research co-design included focus groups, interviews and surveys. The effectiveness of research co-design has rarely been evaluated empirically or experimentally; however, qualitative exploration has described the positive and negative outcomes associated with co-design. The research provided many recommendations for conducting research co-design, including training participating end-users in research skills, having regular communication between researchers and end-users, setting clear end-user expectations, and assigning set roles to all parties involved in co-design…

Research co-design appears to be widely used but seldom described or evaluated in detail. Though it has rarely been tested empirically or experimentally, existing research suggests that it can benefit researchers, practitioners, research processes and research outcomes. Realising the potential of research co-design may require the development of clearer and more consistent terminology, better reporting of the activities involved and better evaluation….(More)”.

The Rise and Fall of Good-Governance Promotion

Curated on Posted on by Stefaan Verhulst

Alina Mungiu-Pippidi at the Journal of Democracy: “With the 2003 adoption of the UN Convention Against Corruption, good-governance norms have achieved—on the formal level at least—a degree of recognition that can fairly be called universal. This reflects a centuries-long struggle to establish the moral principle of “ethical universalism,” which brings together the ideas of equity, reciprocity, and impartiality. The West’s success in promoting this norm has been extraordinary, yet there are also significant risks. Despite expectations that international concern and increased regulation would lead to less corruption, current trends suggest otherwise. Exchanges between countries perceived as corrupt and countries perceived as noncorrupt seem to lead to an increase in corruption in the noncorrupt states rather than its decrease in the corrupt ones. Direct good-governance interventions have had poor results. And anticorruption has helped populist politicians, who use anti-elite rhetoric similar to that of anticorruption campaigners….(More)”.

Community science: A typology and its implications for governance of social-ecological systems

Curated on Posted on by Stefaan Verhulst

Paper by Anthony Charles, Laura Loucks, Fikret Berkes, and Derek Armitage: “There is an increasing recognition globally of the role to be played by community science –scientific research and monitoring driven and controlled by local communities, and characterized by place-based knowledge, social learning, collective action and empowerment. In particular, community science can support social-ecological system transformation, and help in achieving better ‘fit’ between ecological systems and governance, at local and higher levels of decision making.

This paper draws on three examples of communities as central actors in the process of knowledge co-production to present a typology of community science, and to deduce a set of key principles/conditions for success.

The typology involves three social learning models in which the community acquires scientific knowledge by (1) engaging with external bodies, (2) drawing on internal volunteer scientific expertise, and/or (3) hiring (or contracting) in-house professional scientific expertise. All of these models share the key characteristic that the local community decides with whom they wish to engage, and in each case, social learning is fundamental. Some conditions that facilitate community science include: community-driven and community-control; flexibility across leadership models; connection to place and collective values; empowerment, agency and collective action; credible trust; local knowledge; and links to governance.

Community science is not a panacea for effecting change at the local level, and there is need for critical assessment of how it can help to fill governance gaps. Nevertheless, a considerable body of experience globally illustrates how local communities are drawing effectively on community science for better conservation and livelihood outcomes, in a manner compatible with broader trends toward ecosystem-based management and local stewardship….(More)”.

When a Nudge Backfires: Combining (Im)Plausible Deniability with Social and Economic Incentives to Promote Behavioral Change

Curated on Posted on by Stefaan Verhulst

Paper by G. Bolton, E. Dimant, and U. Schmidt: “Both theory and recent empirical evidence on nudging suggest that observability of behavior acts as an instrument for promoting (discouraging) pro-social (anti-social) behavior. We connect three streams of literature (nudging, social preferences, and social norms) to investigate the universality of these claims. By employing a series of high-powered laboratory and online studies, we report here on an investigation of the questions of when and in what form backfiring occurs, the mechanism behind the backfiring, and how to mitigate it. We find that inequality aversion moderates the effectiveness of such nudges and that increasing the focus on social norms can counteract the backfiring effects of such behavioral interventions. Our results are informative for those who work on nudging and behavioral change, including scholars, company officials, and policy-makers….(More)”

Enchanted Determinism: Power without Responsibility in Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

Paper by Alexander Campolo and Kate Crawford: “Deep learning techniques are growing in popularity within the field of artificial intelligence (AI). These approaches identify patterns in large scale datasets, and make classifications and predictions, which have been celebrated as more accurate than those of humans. But for a number of reasons, including nonlinear path from inputs to outputs, there is a dearth of theory that can explain why deep learning techniques work so well at pattern detection and prediction. Claims about “superhuman” accuracy and insight, paired with the inability to fully explain how these results are produced, form a discourse about AI that we call enchanted determinism. To analyze enchanted determinism, we situate it within a broader epistemological diagnosis of modernity: Max Weber’s theory of disenchantment. Deep learning occupies an ambiguous position in this framework. On one hand, it represents a complex form of technological calculation and prediction, phenomena Weber associated with disenchantment.

On the other hand, both deep learning experts and observers deploy enchanted, magical discourses to describe these systems’ uninterpretable mechanisms and counter-intuitive behavior. The combination of predictive accuracy and mysterious or unexplainable properties results in myth-making about deep learning’s transcendent, superhuman capacities, especially when it is applied in social settings. We analyze how discourses of magical deep learning produce techno-optimism, drawing on case studies from game-playing, adversarial examples, and attempts to infer sexual orientation from facial images. Enchantment shields the creators of these systems from accountability while its deterministic, calculative power intensifies social processes of classification and control….(More)”.

Housing Search in the Age of Big Data: Smarter Cities or the Same Old Blind Spots?

Curated on Posted on by Stefaan Verhulst

Paper by Geoff Boeing et al: “Housing scholars stress the importance of the information environment in shaping housing search behavior and outcomes. Rental listings have increasingly moved online over the past two decades and, in turn, online platforms like Craigslist are now central to the search process. Do these technology platforms serve as information equalizers or do they reflect traditional information inequalities that correlate with neighborhood sociodemographics? We synthesize and extend analyses of millions of US Craigslist rental listings and find they supply significantly different volumes, quality, and types of information in different communities.

Technology platforms have the potential to broaden, diversify, and equalize housing search information, but they rely on landlord behavior and, in turn, likely will not reach this potential without a significant redesign or policy intervention. Smart cities advocates hoping to build better cities through technology must critically interrogate technology platforms and big data for systematic biases….(More)”.

Whose Side are Ethics Codes On?

Curated on Posted on by Stefaan Verhulst

Paper by Anne L. Washington and Rachel S. Kuo: “The moral authority of ethics codes stems from an assumption that they serve a unified society, yet this ignores the political aspects of any shared resource. The sociologist Howard S. Becker challenged researchers to clarify their power and responsibility in the classic essay: Whose Side Are We On. Building on Becker’s hierarchy of credibility, we report on a critical discourse analysis of data ethics codes and emerging conceptualizations of beneficence, or the “social good”, of data technology. The analysis revealed that ethics codes from corporations and professional associations conflated consumers with society and were largely silent on agency. Interviews with community organizers about social change in the digital era supplement the analysis, surfacing the limits of technical solutions to concerns of marginalized communities. Given evidence that highlights the gulf between the documents and lived experiences, we argue that ethics codes that elevate consumers may simultaneously subordinate the needs of vulnerable populations. Understanding contested digital resources is central to the emerging field of public interest technology. We introduce the concept of digital differential vulnerability to explain disproportionate exposures to harm within data technology and suggest recommendations for future ethics codes….(More)”.