We invite contributions to a Special Issue on AI for People, to be published by the AI & Society Journal of Culture, Knowledge and Communication (Springer) https://www.springer.com/journal/146.
This Special Issue was born out of the idea of shaping Artificial Intelligence technology around human and societal needs. We believe that technology should respect the anthropocentric principle. It should be at the service of the people, not vice-versa. In order to foster this idea, we need to narrow the gap between civil society and technical experts. This gap is one in knowledge, in action and in tools for change.
Special Issue Themes
The ‘social good’ is something which benefits the general public, being ‘for’ the people, and at the same time, it is something which reflects and respects their wishes, being ‘from’ the people. The ‘social good’ can be envisioned as global citizens uniting to unlock the potential of individuals through collaboration to create positive societal impact. It is about engagement, shareability and bringing people together to change the world for the better. Concurrently, Artificial Intelligence (AI) research is mature enough for stable algorithms and approaches to be used and play a crucial role in the aforementioned ‘social good’, enabling the deployment of revolutionary services and applications. It is envisioned that both the social interaction and the integration with smart devices will meaningfully impact societal development and sustainability. Nowadays, advances in research on AI systems have yielded a far-reaching discourse about the applicability of the AI Ethics principles when designing, developing, integrating or using AI systems. AI Ethical principles are guidelines put forward by policy makers that can act as abstractions, as normative constraints on the do’s and don’ts of algorithmic use in society. Themes of transparency, justice and fairness, non-maleficence, responsibility and privacy must be taken into account when deploying real-world AI systems.
This Special Issue will focus on the following AI Ethics principles:
1. Accuracy and Robustness: algorithmic conclusions are probabilities and therefore not infallible and they also might incur in errors during execution. This can lead to unjustified actions. 2. Explainability and Transparency: a lack of interpretability and transparency can lead to algorithmic systems that are hard to control, monitor, and correct. This is the commonly called ‘black-box’ issue. 3. Bias and Fairness: conclusions can only be as reliable (but also as neutral) as the data they are based on, and this can lead to bias. An action could be found to be discriminatory if it has a disproportionate impact on one group of people. 4. Privacy: algorithmic activities, like profiling, can lead to challenges for autonomy and informational privacy. 5. Accountability: it is hard to assign responsibility to algorithmic harms and this can lead to issues with moral responsibility. 6. Safety and Security: AI systems need to respect and support privacy rights and data protection while ensuring the security of data.
The aim of this Special Issue is, however broad, mostly twofold. On one hand, it entails the technical realization of these AI Ethics principles (one or more) in practice. For example, this might refer to specific techniques to ensure principles like ‘fairness’ in an algorithm, with their related practical challenges. On the other hand, it entails addressing these principles from a conceptual standpoint. For example, this might entail, among other things, theorizing, analysing, criticizing and/or further developing the AI principles themselves. Contributions are welcomed from a host of different disciplines, spanning from the sciences, the social sciences and the humanities.
We welcome contributions across the following formats: ● Original papers (max 10k words): substantial contribution, theory, method, application. Contributions may be experimental, based on case studies, or conceptual discussions of how AI systems affect organisations, society and humans. Original papers will be double blind peer-reviewed by two reviewers and the editorial team. ● Open Forum papers (max 8k words): research in progress, ideas paper. Contributions may come from researchers, practitioners and others interested in the topics of the special issue. Contributions might be, but not limited to, discussion papers, literature reviews, case studies, working papers, features, and articles on emerging research. Papers published in the open forum target a broad audience i.e. academics, designers as well as the average reader. Open Forum contributions will be double blind peer-reviewed by two reviewers and the editorial team. ● Student papers (max 6k words): research in progress. Contributions may come from post-graduate students and Ph.D. students interested in the topics of the special issue. For articles that are based primarily on the student’s dissertation or thesis, it is recommended that the student is usually listed as principal author. Papers are double blind peer-reviewed by one reviewer and the editorial team. ● Curmudgeon papers (max 1k words): short opinionated column on trends in technology, science and society, commenting on issues of concern to the research community and wider society. Whilst the drive for artificial intelligence promotes potential benefits to wider society, it also raises deep concerns of existential risk, thereby highlighting the need for an ongoing conversation between technology and society. At the core of Curmudgeon concern is the question: What are the political-philosophical concepts regarding the present sphere of AI technology? Curmudgeon articles will be reviewed by the Journal editors.
– Abstract submission: January 31, 2021 – Manuscript submission: April 30, 2021 – Notifications: July 30, 2021 – Submission final versions: November 30, 2021
You can find more information about formatting under the section “Submission guidelines” https://www.springer.com/journal/146. For inquiries and to submit your abstract and manuscript, please contact: email@example.com
Special Issue Editors
Angelo Trotta, Department of Computer Science and Engineering, University of Bologna, Italy, firstname.lastname@example.org Vincenzo Lomonaco, Department of Computer Science and Engineering, University of Bologna, Italy, email@example.com Marta Ziosi, Oxford Internet Institute, University of Oxford, UK, firstname.lastname@example.org
Angelo Trotta is a Research Associate with the Department of Computer Science and Engineering, University of Bologna. He received his Ph.D. degree in computer science and engineering in 2017 from University of Bologna, Bologna, Italy. He was a Visiting Researcher with the Heudiasyc Laboratory, Sorbonne Universities, UTC, Compiègne, France, and with Genesys-Laboratory, Northeastern University, Boston, MA, USA. He is co-founder of ‘AI for People’, an international association whose aim is to use Artificial Intelligent technologies for the social good. His current research includes nature inspired algorithms for self-organizing multi-robots wireless systems, drone swarm, reinforcement learning and IoT/WoT. Vincenzo Lomonaco is a Postdoctoral Researcher at the University of Bologna, Italy and President of ContinualAI, a non-profit research organization and the largest open community on Continual Learning for AI. Currently, he is also a Co-founder and Board Member of AI for People and a Research Affiliate at AI Labs. Vincenzo obtained his PhD at UniBo in early 2019 with a dissertation titled “Continual Learning with Deep Architectures”: a natural continuation of his master’s thesis on bio-inspired deep architectures he started in 2014. For more than 3 years he has been working as a teaching assistant for the “Machine Learning” and “Computer Architectures” courses in the Department of Computer Science of Engineering (DISI) in the same university. He has been a visiting research scientist at Purdue University, USA in 2017, at ENSTA ParisTech Grande École, France in 2018 and at Numenta, USA in 2019. His main research interests include open science and ethical AI developments, continual/lifelong learning with deep architectures, multi-task learning, knowledge distillation and transfer, and their applications to embedded systems, robotics and internet-of-things. Marta Ziosi is a PhD candidate at the Oxford Internet Institute. Marta Ziosi is also the co-founder of AI for People (https://www.aiforpeople.org/), a non-profit research organization that promotes the use of AI with respect for human and social needs. Marta holds a BSc in mathematics and philosophy from University College Maastricht and a MSc in philosophy and public policy from the London School of Economics. She spent a semester abroad at UC Berkeley, where her interest in topics at the crossroads of technology and social sciences further developed. In recent years, she has worked on AI Policy for The Future Society and for DG CNECT at the European Commission. She also contributed to computational creativity research at University College Dublin and taught Philosophy of Science at University College Maastricht. Her interests lie in the interdisciplinary approaches to Machine Learning and in the mutual influence that AI and Society exert on each other, especially in the field of algorithmic fairness.