AI’s social sciences deficit

Sloane, Mona; Moss, Emanuel

doi:10.1038/s42256-019-0084-6

Cited by 53 publications

(28 citation statements)

References 13 publications

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“…Real‐world testing of intelligent technology can be taken to amount to a partial falsification of assumptions formulated in the sociology of AI in that period, insofar as sociologists of technology claimed then that artificial intelligence research, “lacks a social theory” (Woolgar, ). This claim continues to reverberate today: in a recent article on the sociology of robots, Esposito () posits that “the sociological perspective is not involved in designing algorithms, which are programmed without adequate consideration of social and communicative aspects” (see also Sloane & Moss, ). Meister () states that to date sociology has had limited influence on the field of social robotics, and notes artificial intelligence’s reliance on narrow interactional framings of sociality (see also Alač, Movellan, & Tanaka, ).…”

Section: From the Lab To The Street: Beyond The “Social Deficit” Of Imentioning

confidence: 99%

Co‐existence or displacement: Do street trials of intelligent vehicles test society?

Marres

2020

British Journal of Sociology

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This paper examines recent street tests of autonomous vehicles (AVs) in the UK and makes the case for an experimental approach in the sociology of intelligent technology. In recent years intelligent vehicle testing has moved from the laboratory to the street, raising the question of whether technology trials equally constitute tests of society. To adequately address this question, I argue, we need to move beyond analytic frameworks developed in 1990s Science and Technology Studies, which stipulated "a social deficit" of both intelligent technology and technology testing. This diagnosis no longer provides an effective starting point for sociological analysis, as real-world tests of intelligent technology explicitly seek to bring social phenomena within the remit of technology testing. I propose that we examine instead whether and how the introduction of intelligent vehicles into the street involves the qualification and re-qualification of relations and dynamics between social actors. I develop this proposal through a discussion of a field study of AV street trials in three cities in the UK-London, Milton Keynes, and Coventry. These urban trials were accompanied by the claim that automotive testing on the open road will enable cars to operate in tune with the social environment, and I show how iterations of street testing undo this proposition and compel its reformulation. Current test designs are limited by their narrow conception of sociality in terms of interaction between cars and other road users. They exclude from consideration the relational 538 | MARRES O Public Road You express me better than I can express myself You shall be more to me than my poem Walt Whitman, The Open Road 1 | INTRODUC TI ON At least since early 2016, so-called autonomous vehicles, or driverless cars, have been tested on urban roads 1 across the UK, in London, Milton Keynes, Bristol, and Coventry. 2 The most well known of these tests are funded by the UK government, by way of its Centre for Connected and Autonomous Vehicles (CCAV), and among their principal aims is to demonstrate the capacity of intelligent vehicle technologies to operate successfully amidst social complexity, on the open road. As the Department for Transport explained the approach in its 2015 code of practice for such testing:Manufacturers have a responsibility to ensure that highly and fully automated vehicle technologies undergo thorough testing and development before being brought to market. Much of this development can be done in test laboratories or on dedicated test tracks and proving grounds. However to help ensure that these technologies are capable of safely handing the many varied situations that they may encounter throughout their service life, it is expected that controlled "real world" testing will also be necessary. Testing of automated vehicle technologies on public roads or in other places should be facilitated while ensuring that this testing is carried out with the minimum practicable risk. (Department for Transport, 2015, my italics) 3 As the ...

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Section: From the Lab To The Street: Beyond The “Social Deficit” Of Imentioning

confidence: 99%

Co‐existence or displacement: Do street trials of intelligent vehicles test society?

Marres

2020

British Journal of Sociology

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“…In recent years, many psychologists have even pursued ambitious big-I problem-centric approaches that produce rich data that describe and explain diverse problems like climate change (Pearson, Schuldt, Romero-Canyas, Ballew, & Larson-Konar, 2018), misinformation (Vosoughi, Roy, & Aral, 2018), morality, and artificial intelligence (Bonnefon, Shariff, & Rahwan, 2016). We believe triangulating across disciplines ranging from anthropology to climate science and computer science has great potential to improve not just psychological theorizing, but also the practical relevance of psychological research (e.g., Henrich et al, 2010;Pearson, Watson, & Platt, 2014;Pearson, Schuldt, & Romero-Canyas, 2016;Sloane & Moss, 2019;Rahwan et al, 2019;van Rooij & Baggio, 2020).…”

Section: Promises and Perils Of Experimentation 11mentioning

confidence: 99%

Promises and perils of experimentation: The mutual internal validity problem

Lin¹,

Werner²,

Inzlicht³

2020

Preprint

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Experimentation is the cornerstone of the scientific method. Decades of experimental work in psychology, however, has yet to produce reliable and cumulative knowledge. We argue psychology as a discipline not only overuses experimentation, but too often uses it as a theatrical storytelling device. Rather than use experiments to facilitate valid scientific inference, psychologists cleverly design experiments to elicit the very effects they wish to observe, ignoring how these effects may or may not cohere with bodies of knowledge within psychology and across disciplines. This approach leads to ungeneralizable results and an unwillingness to accept and learn from failed, but potentially informative experiments. To make real progress toward understanding human behavior and the mind, psychology needs to embrace big-interdisciplinary (big-I) triangulation, which requires integrating theories, methods, and approaches from diverse disciplines and sources.

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“…The importance of considering social science input when designing applications for AI systems has been noted by a number of authors, e.g. [13]. This is of great importance in smart grid applications as well.…”

Section: Social Science Perspectivesmentioning

confidence: 99%

Consider ethical and social challenges in smart grid research

et al. 2019

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Artificial Intelligence and Machine Learning are increasingly seen as key technologies for building more decentralised and resilient energy grids, but researchers must consider the ethical and social implications of their use Energy grids are undergoing rapid changes, requiring new ways both to process the large amounts of data generated from the power system, but also -increasingly -to take smart operational decisions [1].On the data side, the UK and most EU countries have committed to a target of offering a smart meter to every home by 2020 [2], with similar monitoring being installed in other parts of the energy network. This has led to some to refer to a "data tsunami", requiring development of new machine learning techniques to deal with the ensuing challenge of extracting useful information from this data -often in real time.Another trend is the use of AI techniques (such as those from multi-agent systems, computational game theory and decision making under uncertainty) to take autonomous allocation and control decisions. This is driven increasingly by the moves towards more decentralised energy systems, where prosumers (consumers with own micro-generation and storage) can generate and source their own electricity through peer-to-peer (P2P) trading in local energy markets and community energy schemes.To illustrate these trends from a UK perspective (with which the authors are most familiar), a number of projects are looking at computational techniques suitable for more decentralised energy future -including CESI (the UK National Centre for Energy Systems Integration) [23] and Responsive Flexibility, one of the largest smart energy demonstrators in the UK, focused on the islands of Orkney [26]. In fact, Community Energy Scotland (an organisation supporting such projects in Scotland) lists no less than 300 projects on their website [3]. This has attracted a surge of interest and considerable investments in technologies enabling these settings, such as blockchain, machine learning (ML) and distributed Artificial Intelligence (AI) [4].Yet, there are substantial ethical and social questions to be asked in such developments. The AI community has begun to recognise the challenges posed by rapid adoption of AI in the real world, and a number of general guidelines have been proposed [5]. Similarly, the smart grid community needs to consider the ethical challenges that the rapid adoption of AI and ML techniques to control our energy systems brings. In this piece, we discuss some domainspecific challenges and proposed solutions for adopting AI techniques to automate smart energy grids. We note our starting point is not purely generic AI ethical principles (although we have consulted recent reports, such as Floridi [5], which provide a useful guide). Rather, we consider the trends that have shaped AI adoption in smart grids in recent years, and illustrate the type of ethical challenges involved in each.

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AI’s social sciences deficit

Cited by 53 publications

References 13 publications

Co‐existence or displacement: Do street trials of intelligent vehicles test society?

Co‐existence or displacement: Do street trials of intelligent vehicles test society?

Promises and perils of experimentation: The mutual internal validity problem

Consider ethical and social challenges in smart grid research

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