Hi all! I am working on an extension of Kohli&Grover's (2012) IT value co-creation theory in multi-firm environments. There is clearly a gap in the literature on the role of human & knowledge artefacts. I was wondering, can you suggest any recent papers (since 2019) linking IT value co-creation research to organizational learning theories?
To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems, as well as comparisons with humans. Over the past hundred years, there has been an abundance of attempts to define and measure intelligence, across both the fields of psychology and AI. We summarize and critically assess these definitions and evaluation approaches, while making apparent the two historical conceptions of intelligence that have implicitly guided them. We note that in practice, the contemporary AI community still gravitates towards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks such as board games and video games. We argue that solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience: unlimited priors or unlimited training data allow experimenters to "buy" arbitrary levels of skills for a system, in a way that masks the system's own generalization power. We then articulate a new formal definition of intelligence based on Algorithmic Information Theory, describing intelligence as skill-acquisition efficiency and highlighting the concepts of scope, generalization difficulty, priors, and experience. Using this definition, we propose a set of guidelines for what a general AI benchmark should look like. Finally, we present a benchmark closely following these guidelines, the Abstraction and Reasoning Corpus (ARC), built upon an explicit set of priors designed to be as close as possible to innate human priors. We argue that ARC can be used to measure a human-like form of general fluid intelligence and that it enables fair general intelligence comparisons between AI systems and humans.
Love is often thought to involve a merging of identities or a sense that a romantic partner is part of oneself. Couples who report feeling more satisfied with their relationships also feel more interconnected. We hypothesized that Facebook profile photos would provide a novel way to tap into romantic partners’ merged identities. In a cross-sectional study (Study 1), a longitudinal study (Study 2), and a 14-day daily experience study (Study 3), we found that individuals who posted dyadic profile pictures on Facebook reported feeling more satisfied with their relationships and closer to their partners than individuals who did not. We also found that on days when people felt more satisfied in their relationship, they were more likely to share relationship relevant information on Facebook. This study expands our knowledge of how online behavioral traces give us powerful insight into the satisfaction and closeness of important social bonds.
We show that malicious COVID-19 content, including racism, disinformation, and misinformation, exploits the multiverse of online hate to spread quickly beyond the control of any individual social media platform. We provide a first mapping of the online hate network across six major social media platforms. We demonstrate how malicious content can travel across this network in ways that subvert platform moderation efforts. Machine learning topic analysis shows quantitatively how online hate communities are sharpening COVID-19 as a weapon, with topics evolving rapidly and content becoming increasingly coherent. Based on mathematical modeling, we provide predictions of how changes to content moderation policies can slow the spread of malicious content.
Online communities provide important functions in their participants’ lives, from providing spaces to discuss topics of interest to supporting the development of close, personal relationships. Volunteer moderators play key roles in maintaining these spaces, such as creating and enforcing rules and modeling normative behavior. While these users play important governance roles in online spaces, less is known about how the work they do is impacted by platform design and culture. r/AskHistorians, a Reddit-based question and answer forum dedicated to providing users with academic-level answers to questions about history provides an interesting case study on the impact of design and culture because of its unique rules and their strict enforcement by moderators. In this article I use interviews with r/AskHistorians moderators and community members, observation, and the full comment log of a highly upvoted thread to describe the impact of Reddit’s design and culture on moderation work. Results show that visible moderation work that is often interpreted as censorship, and the default masculine whiteness of Reddit, create challenges for moderators who use the subreddit as a public history site. Nonetheless, r/AskHistorians moderators have carved a space on Reddit where, through their public scholarship work, the community serves as a model for combating misinformation by building trust in academic processes.
Does the extent to which people are smiling in their Facebook photos predict future life satisfaction? In two longitudinal studies, the authors showed that smile intensity coded from a single Facebook profile photograph from male and female participants’ first semester at college was a robust predictor of self-reported life satisfaction 3.5 years later—as they were about to graduate from college. Controlling for first-semester life satisfaction, the authors also determined that smile intensity was a unique predictor of changes in life satisfaction over time. In addition, the authors demonstrated that the results were not due to extraversion or tosex differences in smile intensity. Finally, the authors showed that participants who exhibited a more intense smile in their Facebook photo had better social relationships during their first semester at college and that the association between smile intensity and life satisfaction 3.5 years later was partially mediated by first-semester social relationships satisfaction.
In recent years, cryptocurrencies have increasingly gained interest. The underlying technology, Blockchain, shifts the responsibility for securing assets to the end-user and requires them to manage their (private) keys. Little attention has been given to how cryptocurrency users handle the challenges of key management in practice and how they select the tools to do so. To close this gap, we conducted semi-structured interviews (N=10). Our thematic analysis revealed prominent themes surrounding motivation, risk assessment, and coin management tool usage in practice. We found that the choice of tools is driven by how users assess and balance the key risks that can lead to loss: the risk of (1) human error, (2) betrayal, and (3) malicious attacks. We derive a model, explaining how risk assessment and intended usage drive the decision which tools to use. Our work is complemented by discussing design implications for building systems for the crypto economy.
Blockchain is an emerging infrastructural technology that is proposed to fundamentally transform the ways in which people transact, trust, collaborate, organize and identify themselves. In this paper, we construct a typology of emerging blockchain applications, consider the domains in which they are applied, and identify distinguishing features of this new technology. We argue that there is a unique role for the HCI community in linking the design and application of blockchain technology towards lived experience and the articulation of human values. In particular, we note how the accounting of transactions, a trust in immutable code and algorithms, and the leveraging of distributed crowds and publics around vast interoperable databases all relate to longstanding issues of importance for the field. We conclude by highlighting core conceptual and methodological challenges for HCI researchers beginning to work with blockchain and distributed ledger technologies.
The wearing of the face masks appears as a solution for limiting the spread
of COVID-19. In this context, efficient recognition systems are expected for
checking that people faces are masked in regulated areas. To perform this task,
a large dataset of masked faces is necessary for training deep learning models
towards detecting people wearing masks and those not wearing masks. Some large
datasets of masked faces are available in the literature. However, at the
moment, there are no available large dataset of masked face images that permits
to check if detected masked faces are correctly worn or not. Indeed, many
people are not correctly wearing their masks due to bad practices, bad
behaviors or vulnerability of individuals (e.g., children, old people). For
these reasons, several mask wearing campaigns intend to sensitize people about
this problem and good practices. In this sense, this work proposes three types
of masked face detection dataset; namely, the Correctly Masked Face Dataset
(CMFD), the Incorrectly Masked Face Dataset (IMFD) and their combination for
the global masked face detection (MaskedFace-Net). Realistic masked face
datasets are proposed with a twofold objective: i) to detect people having
their faces masked or not masked, ii) to detect faces having their masks
correctly worn or incorrectly worn (e.g.; at airport portals or in crowds). To
the best of our knowledge, no large dataset of masked faces provides such a
granularity of classification towards permitting mask wearing analysis.
Moreover, this work globally presents the applied mask-to-face deformable model
for permitting the generation of other masked face images, notably with
specific masks. Our datasets of masked face images (137,016 images) are
available at https://github.com/cabani/MaskedFace-Net.
Complex dynamical fluctuations, from molecular noise within cells, collective intelligence, brain dynamics or computer traffic have been shown to display noisy behaviour consistent with a critical state between order and disorder. Living close to the critical point can have a number of adaptive advantages and it has been conjectured that evolution could select (and even tend to) these critical states. One way of approaching such state is by means of so called self-organized criticality (SOC) where the system poises itself close to the critical point. Is this the case of living cells? It is difficult to test this idea given the enormous dimensionality associated with gene and metabolic webs. In this paper we present an alternative approach: to engineer synthetic gene networks displaying SOC behaviour. This is achieved by exploiting the presence of a saturation (congestion) phenomenon of the ClpXP protein degradation machinery in cells. Using a feedback design that detects and then reduces ClpXP congestion, a is built from a two-gene network system, where SOC can be successfully implemented. Both deterministic and stochastic models are used, consistently supporting the presence of criticality in intracellular traffic. The potential implications for both cellular dynamics and designed intracellular noise are discussed.