Is it Research or is it Spying? Thinking-Through Ethics in Big Data AI and Other Knowledge Sciences

Berendt, Bettina; Büchler, Marco; Rockwell, Geoffrey

doi:10.1007/s13218-015-0355-2

Cited by 13 publications

(6 citation statements)

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“…Rather, methodological tools and design embed ethical choices (Markham, 2006; Markham et al, 2018). It is worth therefore re-iterating, that when developing or using AI software, AI researchers must remain ethically alert to the fact that all research tools, including AI, have biases and assumptions built into them (Berendt et al, 2015; Elish and Boyd, 2018; Gillespie, 2014; Luka and Millette, 2018; McQuillan, 2018; Vis, 2013; Zimmer, 2018).…”

Section: Discussionmentioning

confidence: 99%

Population health AI researchers’ perceptions of the public portrayal of AI: A pilot study

Samuel¹,

Diedericks

Derrick

2020

Public Underst Sci

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This article reports how 18 UK and Canadian population health artificial intelligence researchers in Higher Education Institutions perceive the use of artificial intelligence systems in their research, and how this compares with their perceptions about the media portrayal of artificial intelligence systems. This is triangulated with a small scoping analysis of how UK and Canadian news articles portray artificial intelligence systems associated with health research and care. Interviewees had concerns about what they perceived as sensationalist reporting of artificial intelligence systems – a finding reflected in the media analysis. In line with Pickersgill’s concept of ‘epistemic modesty’, they considered artificial intelligence systems better perceived as non-exceptionalist methodological tools that were uncertain and unexciting. Adopting ‘epistemic modesty’ was sometimes hindered by stakeholders to whom the research is disseminated, who may be less interested in hearing about the uncertainties of scientific practice, having implications on both research and policy.

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Section: Discussionmentioning

confidence: 99%

Population health AI researchers’ perceptions of the public portrayal of AI: A pilot study

Samuel¹,

Diedericks

Derrick

2020

Public Underst Sci

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“…While this may appear to be a cliché response expected from (some) engineers (Berendt et al, 2015), this remark was made only once in the current set of participants. It was also observed that the computer scientists in group GB became more interested in participating when the discussion turned to practical examples of how engineers build value judgements into their systems.…”

mentioning

confidence: 85%

“…The Ethics of Datafication. Electronic Book Review, Dec 2017. http://electronicbookreview.com/thread/technocapitalism/datafiction - Berendt, B., Büchler, M., & Rockwell, G. (2015). Is it research or is it spying?…”

mentioning

confidence: 99%

(De)constructing ethics for autonomous cars: A case study of Ethics Pen-Testing towards “AI for the Common Good”

Berendt¹

2020

irie

Self Cite

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Recently, many AI researchers and practitioners have embarked on research visions that involve doing AI for “Good”. This is part of a general drive towards infusing AI research and practice with ethical thinking. One frequent theme in current ethical guidelines is the requirement that AI be good for all, or: contribute to the Common Good. But what is the Common Good, and is it enough to want to be good? Via four lead questions, the concept of Ethics Pen-Testing (EPT) identifies challenges and pitfalls when determining, from an AI point of view, what the Common Good is and how it can be enhanced by AI. The current paper reports on a first evaluation of EPT. EPT is applicable to various artefacts that have ethical impact, including designs for or implementations of specific AI technology, and requirements engineering methods for eliciting which ethical settings to build into AI. The current study focused on the latter type of artefact. In four independent sessions, participants with close but varying involvements in “AI and ethics” were asked to deconstruct a method that has been proposed for eliciting ethical values and choices in autonomous car technology, an online experiment modelled on the Trolley Problem. The results suggest that EPT is well-suited to this task: the remarks made by participants lent themselves well to being structured by the four lead questions of EPT, in particular, regarding the question what the problem is and about which stakeholders define it. As part of the problem definition, the need became apparent for thorough technical domain knowledge in discussions of AI and ethics. Thus, participants questioned the framing and the presuppositions inherent in the experiment and the discourse on autonomous cars that underlies the experiment. They transitioned from discussing a specific AI artefact to discussing its role in wider socio-technical systems. Results also illustrate to what extent and how the requirements engineering method forces us to not only have a discussion about which values to “build into” AI systems, the substantive building blocks of the Common Good, but also about how we want to have this discussion at all. Thus, it forces us to become explicit about how we conceive of democracy and the constitutional state and the procedural building blocks of the Common Good.

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“…Indeed, the 'Big Data' narrative-promising unprecedented insights and innovative potential (Mayer-Schonberger and Cukier 2013)-is gradually penetrating all sectors and disciplines, and has resulted in an insatiable hunger for data (Boyd and Crawford 2012). Apart from the challenges this may raise with regard to fundamental rights and ethical values, it may also raise issues for the research and/or innovations itself (Berendt et al 2015). A case in point is provided by R&I tapping into the vast amounts of data generated in the social media context (Schroeder 2014).…”

Section: Data Opportunismmentioning

confidence: 99%

Designing-by-Debate: A Blueprint for Responsible Data-Driven Research & Innovation

Ausloos

Heyman

Bertels

et al. 2018

Responsible Research and Innovation Actions in Science Education, Gender and Ethics

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The emergence and rapid development of ICT-centred research methodologies, and data-driven research and innovation in particular, fundamentally challenge ethical values, human rights and security in the EU and beyond. This is especially-though not exclusively-the result of fragmented legal, ethical and terminological frameworks; a mismatch between rules and how they are applied or disregarded in practice; the privatisation of research data and methods; the fact that these challenges are spread over multiple actors and disciplines and issues raised by data opportunism. These challenges keep Responsible Research and Innovation (RRI) largely hypothetical in many contexts and may lead to social rejection and distorted legislation of emerging research methodologies as well as the huge socio-economic potential they hold. This contribution advances the first blueprint for an innovative approach aimed at overcoming the challenges obstructing the full realisation of RRI. The Designing-by-Debate (DbD) approach provides a systematic model and method for inclusive dialogue through smart stewardship, enabling researchers and the broader stakeholder community to develop, fine-tune and operationalise the framework for RRI to their situation. It is an iterative process based on different forms of participatory debate, aimed at formulating RRI protocols and policies with maximal participation from all stakeholders. The method relies on sharing protocols and guidelines so that they can be used and improved simultaneously through new RRI applications. The DbD approach has different well-defined layers and components, that are aimed at making RRI work in the field. Notwithstanding its greater ambitions, the scope of this contribution is confined to DbD in the context of data-driven research and innovation (and how to align it with ethical, normative, and societal values that are central to the EU identity). DbD, we argue, provides the prerequisites for a holistic yet concrete approach to key legal, ethical and social challenges emerging from ubiquitous use of technology and 'data' to do research and innovation.

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Is it Research or is it Spying? Thinking-Through Ethics in Big Data AI and Other Knowledge Sciences

Cited by 13 publications

References 14 publications

Population health AI researchers’ perceptions of the public portrayal of AI: A pilot study

Population health AI researchers’ perceptions of the public portrayal of AI: A pilot study

(De)constructing ethics for autonomous cars: A case study of Ethics Pen-Testing towards “AI for the Common Good”

Designing-by-Debate: A Blueprint for Responsible Data-Driven Research & Innovation

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