Mini Track on Ethics and Artificial Intelligence
Mini Track Chair: Dr Caroline Stockman, University of Winchester, UK
Artificial Intelligence has been a long-held goal in the visions of future technology, but now, more than ever, its presence is becoming an everyday reality. Popular consumer products have begun to materialise this future in our homes, while less visible AI technologies are also pervading society in a wide variety of domains, such as medicine, law, the military and education. This new revolution is bringing human life to an unprecedented turning point, which brings about a new effort to understand the ethical principles that should guide decision-making, implementation and legislation of AI. How we treat the ethical implications of AI could determine whether we are headed towards a utopian future or blindly running towards a dystopian society.
Suggested topics include but are not limited to:
- human-technology interaction in AI
- moral obligations towards AI and moral agency of machines
- human dignity
- specific societal impacts in a utopian or dystopian sense
- the role of education
- transparency, responsibility, security and control
- building ethical considerations into the AI
- the “three laws of robotics” in a practical sense
Mini Track on on the Malicious Use of Artificial Intelligence: New Challenges for Democratic Institutions and Political Stability
Mini Track Chair: Prof. Evgeny N. Pashentsev , Lomonosov Moscow State University, Russia
The possibilities for artificial intelligence (AI) are growing at an unprecedented rate. AI has many areas of social utility: from machine translation and medical diagnostics to electronic trading and education. Less investigated are the areas and types of the malicious use of artificial intelligence (MUAI), which should be given further attention. It is impossible to exclude global, disastrous, rapid and latent consequences of MUAI. MUAI implies the possibility of using multiple weaknesses of individual and human civilization as a whole. For instance, AI could integrate with a nuclear or biological attack, and even improve its effectiveness. However, AI could similarly be used as a most efficient defence instrument. The international experience in monitoring online content and predictive analytics indicates the possibility of creating an AI system, based on the information disseminated in the digital environment, that could not only indicate threats to information and psychological security in a timely manner but also offer scenarios of counteraction (including counteracting offensive weapons’ systems).
Suggested topics include but are not limited to:
- Dynamic social and political systems and the malicious use of AI
- AI in civil and military conflicts
- AI enhancing terrorist threats and counter-terrorist response
- Role and practice of the malicious use of AI in contemporary geopolitical confrontation
- Predictive analytics and prognostic weapons
- Risk scenarios of the malicious use of AI
- Spoofing, data extraction, and poisoning of training data to exploit vulnerabilities under the malicious use of AI
- Artificial Intelligence Online Reputation Management (ORM)
- AI in Lethal Autonomous Systems (LAWs):
- Deepfakes and their possible influence on political warfare
- Amplification and political agenda setting
- Emotional AI in political warfare
- Damage reputation through bot activities
- Synthetic information products containing software modules that introduce targeted audiences into depression and/ or encourage people to perform strictly defined actions
- Challenges of the malicious use of AI
- Ways and means to neutralize targeted information and psychological destabilization of democratic institutions using AI.
Mini Track on AI and Cyber Security
Mini Track Chair: Dr. Char Sample, ICF Inc. US, University of Warwick, UK
The growing use of artificial intelligence and machine learning (AI/ML) begs the question what does this mean for cyber security? The outcome in security appears to be, at best uncertain. While repetitive tasks, such as configurations, auditing and scanning will likely become early casualties of AI/ML migration into cyber security, other positions, such as hunter, architect that are less repetitive and require various types of thinking may be less likely. There are several areas that expose the gap between the promise of AI and reality.
Studies of interest for this track include but are not limited to:
- Accurate quantitative metrics for the measurement of AI solutions in cyber security.
- Exploration of the relationship between human cognition and AI related to cyber security classifications, visualizations, and data management.
- AI and machine learning of the cyber security landscape in hostile environments
- Analysis of AI driven security solutions to include strengths, weaknesses and gaps.
- Discussions on improvement of training data (resilient data)
- Curiosity based learning solutions in cyber security.
- Impact of AI solutions on cyber security professionals and what it means for society
Mini Track on Artificial Intelligence for Strategy and Innovation
Mini Track Chair: Maurizio Massaro, Ca’ Foscari University of Venice, Italy, Francesca Dal Mas, La Sapienza University of Rome, Italy.and Dr. Mitt Nowshade Kabir, CEO, Trouvus.com & Flerika.com, New York, USA
The Artificial Intelligence (AI) field is expanding at a rapid pace and having a tremendous transformational impact on many areas. As more new concepts and algorithmic solutions to critical business, scientific and industrial problems emerge in the coming decade the effects of AI will only intensify. Organisations in every industry, whether they are in retail, transport, finance or healthcare will need to rethink their core processes, adopt new ideas and instigate innovative approaches to take advantage of this new realm. AI is not just creating immense value across industries, but is also initiating fierce global competition. As a new type of resource, AI is challenging existing resource-based theories, innovation processes and practices, and business strategies.
Organisations that are proactive and innovative in their quest for improved productivity, value creation and process automation using AI and robotics will have a better chance to dominate in their industry. They will also have better potential in the creation of breakthrough products and services. The disruptive power of AI technologies therefore calls for a better understanding of innovation, business models and management strategies particularly in the areas of business where AI plays a prominent role. This mini track welcomes positional papers and work based on empirical research in the following suggested but not limited to topics:
- The role of AI in strategic management thinking
- How AI shapes new ways of decision making
- How AI technologies foster new business models
- How AI will affect communication and relationships with the stakeholder network in the future
- How automation changes the interaction between humans and machines
- The dark side of AI
- The impact of AI on the context of value creation
- The role of AI technologies as operative resources in the production processes
- How AI can boost the company’s profit model
- The value added that AI could confer to new products and services and its new meanings
- How AI can affect operations management and supply chain management
- To what extent AI can impact society and human well-being
Mini Track on AI to Enhance Education and Learning
Mini Track Chair: Milan Todorovic, RMIT University, Melbourne, Australia
Education is one area where artificial intelligence (AIEd) is poised to make big changes. It can automate basic activities in education, like grading. Educational software can be adapted to student needs and to point out places where courses need to improve. In the near future, students could have additional support from AI tutors which, by providing feedback to both students and educators, could change the role of teachers. Smart data gathering and data mining systems could further revolutionise the educational sector.
Current applications of AI in education are not limited only on smart content, educational data mining or on exploring user interfaces such as natural language processing, speech and gesture recognition, eye-tracking and other physiological sensors. They are designed to support learning the most directly via personal tutors for every learner, through intelligent support for collaborative learning and intelligent virtual reality. Many Intelligent Tutor Systems use machine learning techniques, self-training algorithms based on large data sets and neural networks to enable them to make appropriate decisions about learning content provided to the learner.
In the next phase, AIEd will help learners to gain the 21st century skills. It will improve assessment by providing just-in-time assessments, by shaping the learning and providing new insights into how learning is progressing. Pedagogy, technology and system change must be combined to deliver on the promise of technology to propel learning dramatically forward. AIEd research to date only tackled learning in highly structured domains or applying AI techniques on highly structured datasets such as university administration systems. Although essential they are not enough to bring a step-change in the breath and quality of learning for all learners.
This mini-track welcomes theoretical and empirical contributions focusing on, without being limited to, the following topics:
- trends and issues
- what is the educational perspective
- AI impact on skills and competence demand
- impact on cognitive development
- impact on teaching and pedagogical issues
- insights from learning sciences such as neuroscience and psychology
- interdisciplinary contributions
- policy challenges