Paper by Thomas H. Li, and Francisco Barreras: “Human mobility datasets have seen increasing adoption in the past decade, enabling diverse applications that leverage the high precision of measured trajectories relative to other human mobility datasets. However, there are concerns about whether the high sparsity in some commercial datasets can introduce errors due to lack of robustness in processing algorithms, which could compromise the validity of downstream results. The scarcity of “ground-truth” data makes it particularly challenging to evaluate and calibrate these algorithms. To overcome these limitations and allow for an intermediate form of validation of common processing algorithms, we propose a synthetic trajectory simulator and sandbox environment meant to replicate the features of commercial datasets that could cause errors in such algorithms, and which can be used to compare algorithm outputs with “ground-truth” synthetic trajectories and mobility diaries. Our code is open-source and is publicly available alongside tutorial notebooks and sample datasets generated with it….(More)”
Generative Agent Simulations of 1,000 People
Paper by Joon Sung Park: “The promise of human behavioral simulation–general-purpose computational agents that replicate human behavior across domains–could enable broad applications in policymaking and social science. We present a novel agent architecture that simulates the attitudes and behaviors of 1,052 real individuals–applying large language models to qualitative interviews about their lives, then measuring how well these agents replicate the attitudes and behaviors of the individuals that they represent. The generative agents replicate participants’ responses on the General Social Survey 85% as accurately as participants replicate their own answers two weeks later, and perform comparably in predicting personality traits and outcomes in experimental replications. Our architecture reduces accuracy biases across racial and ideological groups compared to agents given demographic descriptions. This work provides a foundation for new tools that can help investigate individual and collective behavior…(More)”.
Why ‘open’ AI systems are actually closed, and why this matters
Paper by David Gray Widder, Meredith Whittaker & Sarah Myers West: “This paper examines ‘open’ artificial intelligence (AI). Claims about ‘open’ AI often lack precision, frequently eliding scrutiny of substantial industry concentration in large-scale AI development and deployment, and often incorrectly applying understandings of ‘open’ imported from free and open-source software to AI systems. At present, powerful actors are seeking to shape policy using claims that ‘open’ AI is either beneficial to innovation and democracy, on the one hand, or detrimental to safety, on the other. When policy is being shaped, definitions matter. To add clarity to this debate, we examine the basis for claims of openness in AI, and offer a material analysis of what AI is and what ‘openness’ in AI can and cannot provide: examining models, data, labour, frameworks, and computational power. We highlight three main affordances of ‘open’ AI, namely transparency, reusability, and extensibility, and we observe that maximally ‘open’ AI allows some forms of oversight and experimentation on top of existing models. However, we find that openness alone does not perturb the concentration of power in AI. Just as many traditional open-source software projects were co-opted in various ways by large technology companies, we show how rhetoric around ‘open’ AI is frequently wielded in ways that exacerbate rather than reduce concentration of power in the AI sector…(More)”.
Flood data platform governance: Identifying the technological and socio-technical approach(es) differences
Paper by Mahardika Fadmastuti, David Nowak, and Joep Crompvoets: “Data platform governance concept focuses on what decision must be made in order to reach the data platform mission and who makes that decision. The current study of the data platform governance framework is applied for the general platform ecosystem that values managing data as an organizational asset. However, flood data platforms are essential tools for enhancing the governance of flood risks and data platform governance in flood platforms is understudied. By adopting a data governance domains framework, this paper identifies the technological and socio-technical approach(es) differences in public value(s) of flood data platforms. Empirically, we analyze 2 cases of flood data platforms to contrast the differences. Utilizing a qualitative approach, we combined web-observations and interviews to collect the data. Regardless of its approach, integrating flood data platform technologies into government authorities’ routines requires organizational commitment that drives value creation. The key differences between these approaches lies in the way the government sectors see this flood data platform technology. Empirically, our case study shows that the technological approach values improving capabilities and performances of the public authority while the socio-technical approach focuses more importantly providing engagement value with the public users. We further explore the differences of these approaches by analyzing each component of decision domains in the data governance framework…(More)”
Shifting Patterns of Social Interaction: Exploring the Social Life of Urban Spaces Through A.I.
Paper by Arianna Salazar-Miranda, et al: “We analyze changes in pedestrian behavior over a 30-year period in four urban public spaces located in New York, Boston, and Philadelphia. Building on William Whyte’s observational work from 1980, where he manually recorded pedestrian behaviors, we employ computer vision and deep learning techniques to examine video footage from 1979-80 and 2008-10. Our analysis measures changes in walking speed, lingering behavior, group sizes, and group formation. We find that the average walking speed has increased by 15%, while the time spent lingering in these spaces has halved across all locations. Although the percentage of pedestrians walking alone remained relatively stable (from 67% to 68%), the frequency of group encounters declined, indicating fewer interactions in public spaces. This shift suggests that urban residents increasingly view streets as thoroughfares rather than as social spaces, which has important implications for the role of public spaces in fostering social engagement…(More)”.
Privacy guarantees for personal mobility data in humanitarian response
Paper by Nitin Kohli, Emily Aiken & Joshua E. Blumenstock: “Personal mobility data from mobile phones and other sensors are increasingly used to inform policymaking during pandemics, natural disasters, and other humanitarian crises. However, even aggregated mobility traces can reveal private information about individual movements to potentially malicious actors. This paper develops and tests an approach for releasing private mobility data, which provides formal guarantees over the privacy of the underlying subjects. Specifically, we (1) introduce an algorithm for constructing differentially private mobility matrices and derive privacy and accuracy bounds on this algorithm; (2) use real-world data from mobile phone operators in Afghanistan and Rwanda to show how this algorithm can enable the use of private mobility data in two high-stakes policy decisions: pandemic response and the distribution of humanitarian aid; and (3) discuss practical decisions that need to be made when implementing this approach, such as how to optimally balance privacy and accuracy. Taken together, these results can help enable the responsible use of private mobility data in humanitarian response…(More)”.
Moral Imagination for Engineering Teams: The Technomoral Scenario
Paper by Geoff Keeling et al: “Moral imagination” is the capacity to register that one’s perspective on a decision-making situation is limited, and to imagine alternative perspectives that reveal new considerations or approaches. We have developed a Moral Imagination approach that aims to drive a culture of responsible innovation, ethical awareness, deliberation, decision-making, and commitment in organizations developing new technologies. We here present a case study that illustrates one key aspect of our approach – the technomoral scenario – as we have applied it in our work with product and engineering teams. Technomoral scenarios are fictional narratives that raise ethical issues surrounding the interaction between emerging technologies and society. Through facilitated roleplaying and discussion, participants are prompted to examine their own intentions, articulate justifications for actions, and consider the impact of decisions on various stakeholders. This process helps developers to reenvision their choices and responsibilities, ultimately contributing to a culture of responsible innovation…(More)”.
Human-AI coevolution
Paper by Dino Pedreschi et al: “Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users’ choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., scientific, legal and socio-political…(More)”.
Code and Craft: How Generative Ai Tools Facilitate Job Crafting in Software Development
Paper by Leonie Rebecca Freise et al: “The rapid evolution of the software development industry challenges developers to manage their diverse tasks effectively. Traditional assistant tools in software development often fall short of supporting developers efficiently. This paper explores how generative artificial intelligence (GAI) tools, such as Github Copilot or ChatGPT, facilitate job crafting—a process where employees reshape their jobs to meet evolving demands. By integrating GAI tools into workflows, software developers can focus more on creative problem-solving, enhancing job satisfaction, and fostering a more innovative work environment. This study investigates how GAI tools influence task, cognitive, and relational job crafting behaviors among software developers, examining its implications for professional growth and adaptability within the industry. The paper provides insights into the transformative impacts of GAI tools on software development job crafting practices, emphasizing their role in enabling developers to redefine their job functions…(More)”.
Engaging publics in science: a practical typology
Paper by Heather Douglas et al: “Public engagement with science has become a prominent area of research and effort for democratizing science. In the fall of 2020, we held an online conference, Public Engagement with Science: Defining and Measuring Success, to address questions of how to do public engagement well. The conference was organized around conceptualizations of the publics engaged, with attendant epistemic, ethical, and political valences. We present here the typology of publics we used (volunteer, representative sample, stakeholder, and community publics), discuss the differences among those publics and what those differences mean for practice, and situate this typology within the existing work on public engagement with science. We then provide an overview of the essays published in this journal arising from the conference which provides a window into the rich work presented at the event…(More)”.