Recent advances in artificial intelligence (AI) and data science have provided an exciting opportunity to improve numerous spheres of economic activities with large sets of data, such as healthcare, finance, and emergency preparedness. Machine learning (ML) in general, and using formal knowledge as part of ML in particular, affects how data is managed, used, and evaluated. These topics are of broad interest to the KDD community. However, the translation of research methods and resources into practice presents a new challenge for the ML and data mining communities. Policymakers and practitioners assert serious usability and privacy concerns that constrain adoption, notably in high-consequence domains, such as healthcare and crisis response. Limitations in output quality and its measurement, and interactive ability including both the provision of explanations and the acceptance of user guidance, result in adoption rates as low as 33% in such domains.
This special issue aims to accelerate our pace towards building responsible intelligent systems by integrating knowledge into contemporary AI and data science methods for improvements in interpretability, proactive user engagement, and, ultimately, trust. At the heart of these user concerns lies a number of technical considerations regarding the scope and assumptions of contemporary methods at all levels of processing. At the outset, the datasets themselves can create model bias and variance (especially for data-limited problems) with adversarial noise and confounds. There are many challenges, such as data heterogeneity including incoherence in unstructured and structured data, challenges in implementation, interpretation of outcomes by frontline providers, sometimes resulting in inconsistencies in reasoning. This results in decision-making errors and necessitates labor-intensive manual verification. Potentially unfair and, worse, hidden, untraceable socio-cultural assumptions discourage the wider adoption of AI. Model evaluation criteria or even well-intentioned accuracy metrics fail to capture the multimodal, multi-view assessment of candidate recommendations of concern to end users, who have no means to query or correct the reasoning. We seek systems capable of human-assisted computing, for example, exploiting knowledge graphs or relational databases for handling biases, generalizing to new populations, and providing outcomes of real applicability.
Topics of interest include, but are not limited to, the following:
- Shallow infusion, semi-deep infusion, and deep infusion of knowledge in neural networks
- Knowledge-enriched deep language models
- Knowledge-enhanced natural language inference and question answering
- Knowledge-infused learning for explainability and interpretability
- Agents with commonsense reasoning using knowledge
- Trust-aware and context-aware computing
- Knowledge graph-enabled reinforcement learning agents
- Human-allied probabilistic learning
- Knowledge-aware causal inference and counterfactual reasoning
- Knowledge graphs for the real world: misinformation, public health, crisis response, education (e-learning), and recommender systems
All submissions must be original manuscripts of fewer than 5,000 words. All manuscripts are subject to peer review on both technical merit and relevance to IEEE Internet Computing’s international readership–primarily practicing engineers and academics who are looking for material that introduces new technology and broadens familiarity with current topics. We do
not accept white papers, and papers that are primarily theoretical or mathematical must clearly relate the mathematical content to a real-life or engineering application. Please read the author instructions. To submit a manuscript, create or access an account on ScholarOne.
Paper submissions due: 1 December 2021
First-round review due: 14 January 2022
Revision due: 18 February 2022
Final decision notification: 25 March 2022
Camera-ready submission due: 8 April 2022
Publication: May/June 2022
Contact the guest editors at firstname.lastname@example.org.
- Shreyansh Bhatt, Amazon, USA
- Manas Gaur, AI Institute, University of South Carolina, USA
- Kalpa Gunaratna, Samsung Research, USA
- Freedy Lecue, INRIA and Thales, France
- Amit Sheth, AI Institute, University of South Carolina, USA