57 research outputs found
Science in the Era of ChatGPT, Large Language Models and AI: Challenges for Research Ethics Review and How to Respond
Large language models of artificial intelligence (AI) such as ChatGPT find
remarkable but controversial applicability in science and research. This paper
reviews epistemological challenges, ethical and integrity risks in science
conduct. This is with the aim to lay new timely foundations for a high-quality
research ethics review in the era of AI. The role of AI language models as a
research instrument and subject is scrutinized along with ethical implications
for scientists, participants and reviewers. Ten recommendations shape a
response for a more responsible research conduct with AI language models
Consensus-based Participatory Budgeting for Legitimacy: Decision Support via Multi-agent Reinforcement Learning
The legitimacy of bottom-up democratic processes for the distribution of
public funds by policy-makers is challenging and complex. Participatory
budgeting is such a process, where voting outcomes may not always be fair or
inclusive. Deliberation for which project ideas to put for voting and choose
for implementation lack systematization and do not scale. This paper addresses
these grand challenges by introducing a novel and legitimate iterative
consensus-based participatory budgeting process. Consensus is designed to be a
result of decision support via an innovative multi-agent reinforcement learning
approach. Voters are assisted to interact with each other to make viable
compromises. Extensive experimental evaluation with real-world participatory
budgeting data from Poland reveal striking findings: Consensus is reachable,
efficient and robust. Compromise is required, which is though comparable to the
one of existing voting aggregation methods that promote fairness and inclusion
without though attaining consensus.Comment: 13 Pages, 8 Figures, 3 Tables, Accepted in International Conference
on Machine Learning, Optimization, and Data Science, 202
Mobile Link Prediction: Automated Creation and Crowd-sourced Validation of Knowledge Graphs
Building trustworthy knowledge graphs for cyber-physical social systems
(CPSS) is a challenge. In particular, current approaches relying on human
experts have limited scalability, while automated approaches are often not
accountable to users resulting in knowledge graphs of questionable quality.
This paper introduces a novel pervasive knowledge graph builder that brings
together automation, experts' and crowd-sourced citizens' knowledge. The
knowledge graph grows via automated link predictions using genetic programming
that are validated by humans for improving transparency and calibrating
accuracy. The knowledge graph builder is designed for pervasive devices such as
smartphones and preserves privacy by localizing all computations. The accuracy,
practicality, and usability of the knowledge graph builder is evaluated in a
real-world social experiment that involves a smartphone implementation and a
Smart City application scenario. The proposed knowledge graph building
methodology outperforms the baseline method in terms of accuracy while
demonstrating its efficient calculations on smartphones and the feasibility of
the pervasive human supervision process in terms of high interactions
throughput. These findings promise new opportunities to crowd-source and
operate pervasive reasoning systems for cyber-physical social systems in Smart
Cities
SMOTEC: An Edge Computing Testbed for Adaptive Smart Mobility Experimentation
Smart mobility becomes paramount for meeting net-zero targets. However,
autonomous, self-driving and electric vehicles require more than ever before an
efficient, resilient and trustworthy computational offloading backbone that
expands throughout the edge-to-cloud continuum. Utilizing on-demand
heterogeneous computational resources for smart mobility is challenging and
often cost-ineffective. This paper introduces SMOTEC, a novel open-source
testbed for adaptive smart mobility experimentation with edge computing. SMOTEC
provides for the first time a modular end-to-end instrumentation for
prototyping and optimizing placement of intelligence services on edge devices
such as augmented reality and real-time traffic monitoring. SMOTEC supports a
plug-and-play Docker container integration of the SUMO simulator for urban
mobility, Raspberry Pi edge devices communicating via ZeroMQ and EPOS for an
AI-based decentralized load balancing across edge-to-cloud. All components are
orchestrated by the K3s lightweight Kubernetes. A proof-of-concept of
self-optimized service placements for traffic monitoring from Munich
demonstrates in practice the applicability and cost-effectiveness of SMOTEC.Comment: 6 pages and 6 figure
Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence
Collective privacy loss becomes a colossal problem, an emergency for personal
freedoms and democracy. But, are we prepared to handle personal data as scarce
resource and collectively share data under the doctrine: as little as possible,
as much as necessary? We hypothesize a significant privacy recovery if a
population of individuals, the data collective, coordinates to share minimum
data for running online services with the required quality. Here we show how to
automate and scale-up complex collective arrangements for privacy recovery
using decentralized artificial intelligence. For this, we compare for first
time attitudinal, intrinsic, rewarded and coordinated data sharing in a
rigorous living-lab experiment of high realism involving >27,000 real data
disclosures. Using causal inference and cluster analysis, we differentiate
criteria predicting privacy and five key data-sharing behaviors. Strikingly,
data-sharing coordination proves to be a win-win for all: remarkable privacy
recovery for people with evident costs reduction for service providers.Comment: Contains Supplementary Informatio
- …