LIST OF PANELS

Rapid technological advancements in Artificial Intelligence (AI) and Autonomous Systems (AS) are beginning to revolutionise all aspects of society today. AI and AS have been developed in various domains to accrue productivity gains as well as other societal benefits, ranging from search algorithms for online advertising (Goodfellow et al. 2016), signal processing (Karaboga et al. 2014), credit scoring (Tsai & Wu 2008; Brown & Mues 2012), medical diagnosis (Russell & Norvig 2016; Amato et al. 2013), autonomous vehicles in transportation (Fagnant & Kockelman 2015; Milakis et al. 2017; Taeihagh & Lim 2018), robotic assistants in healthcare (Stahl and Coeckelbergh 2016) to lethal autonomous weapon systems in the military (Krishnan 2016). These developments threaten to outpace the regulatory responses of governments around the world, which must grapple with the increasing magnitude and speed of these transformations.
 
While the societal benefits of AI and AS have been widely acknowledged by governments, scholars and the general public (Buchanan 2005; Taeihagh & Lim 2018; Ramchurn et al. 2012), a large body of literature has highlighted many potential unintended consequences that can arise from the incautious and misguided adoption of these new technologies. New risks include and are not limited to unemployment (Acemoglu & Restrepo 2018; Frey & Osborne 2017; Peters 2017; Osoba & Welser IV 2017), safety risks (Taeihagh & Lim 2018; Kalra & Paddock 2016), privacy risks (Russell et al. 2015; Lim & Taeihagh 2018; Litman 2017), liability risks (Marchant & Lindor 2012; Čerka et al. 2015; Taeihagh & Lim 2018) and inequality (Makridakis 2017; Taeihagh & Lim 2018; Acemoglu & Restrepo 2018), which require appropriate governance mechanisms to be mitigated. However, governments face challenges regulating new technologies due to insufficient information on industry developments, technological and regulatory uncertainties, coordination challenges between multiple regulatory bodies (Guihot et al. 2017), as well as the opacity of the underlying technology (Scherer 2016), which render traditional policy instruments ineffective and necessitate the use of more nuanced approaches to govern AI and AS.
 
The urgency for and the challenges of governing AI and AS have been highlighted in recent studies (Arkin 2009; Simshaw et al. 2015; Guihot et al. 2017; Scherer 2016; Krishnan 2016; Taeihagh & Lim 2018;  Lim & Taeihagh 2018) and require further exploration. In light of these issues, several key research questions have yet to be answered in the literature:
· What are the types of unintended consequences and risks that can arise from the adoption of AI and AS in different domains? (e.g. ICT, transport, energy, public sector, healthcare, water management etc.)?
· How can risks and uncertainties arising from the use of AI and AS be effectively managed and governed?
· How can AI and AS be responsibly deployed by the public administrators?
· What are the implications of AI and AS on incumbent industries and how can the relationship between these technologies and incumbent industries be reconciled?
· Theoretical, conceptual and empirical approaches to understand new and unconventional regulatory approaches, governance strategies, institutions and discourses to govern risks arising from the adoption of AI and AS.
· What types of standards or guidelines have been developed in industry and governments to manage the risks and uncertainties stemming from the adoption of AI and AS?
· How are risks from the adoption of AI and AS allocated among different stakeholders vertically through the value chain (manufacturers, third-party service providers, consumers) and horizontally across different domains (transport, healthcare, financial sector, public agencies, ICT, education etc.)?
· Single and comparative case studies of governance responses across different countries, regions and domains to address the risks arising from AI and AS adoption.
 
References:
 
Acemoglu, D., & Restrepo, P. (2018). Artificial Intelligence, Automation and Work (No. w24196). National Bureau of Economic Research.
 
Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Havel, J., Sánchez, C. L., ... & Dridi, I. (2013). 1. Artificial neural networks in medical diagnosis. Journal of Applied Biomedicine, 11(2), 47-113.
 
Arkin, R. (2009). Governing lethal behaviour in autonomous robots. Chapman and Hall/CRC.
 
Brown, I., & Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications, 39(3), 3446-3453.
 
Buchanan, B. G. (2005). A (very) brief history of artificial intelligence. Ai Magazine, 26(4), 53.
 
Čerka, P., Grigienė, J., & Sirbikytė, G. (2015). Liability for damages caused by artificial intelligence. Computer Law & Security Review, 31(3), 376-389.
 
Fagnant, D. J., & Kockelman, K. (2015). Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167-181.
 
Frey, C. B., & Osborne, M. A. (2017). The future of employment: how susceptible are jobs to computerisation?. Technological forecasting and social change, 114, 254-280.
 
Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). Cambridge: MIT Press.
 
Guihot, M., Matthew, A. F., & Suzor, N. P. (2017). Nudging Robots: Innovative Solutions to Regulate Artificial Intelligence. Vand. J. Ent. & Tech. L., 20, 385.
 
Kalra, N., & Paddock, S. M. (2016). Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability? Transportation Research Part A: Policy and Practice, 94, 182-193.
 
Karaboga, D., Gorkemli, B., Ozturk, C., & Karaboga, N. (2014). A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42(1), 21-57.
 
Krishnan, A. (2016). Killer robots: legality and ethicality of autonomous weapons. Routledge.
 
Lim, H. S. M., & Taeihagh, A. (2018). Autonomous Vehicles for Smart and Sustainable Cities: An In-Depth Exploration of Privacy and Cybersecurity Implications.
 
Litman, T. (2017). Autonomous vehicle implementation predictions. Victoria, Canada: Victoria Transport Policy Institute.
 
Marchant, G. E., & Lindor, R. A. (2012). The coming collision between autonomous vehicles and the liability system. Santa Clara L. Rev., 52, 1321.
 
Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60.
 
Milakis, D., Van Arem, B., & Van Wee, B. (2017). Policy and society related implications of automated driving: A review of literature and directions for future research. Journal of Intelligent Transportation Systems, 21(4), 324-348.
 
Osoba, O. A., & Welser IV, W. (2017). The Risks of Artificial Intelligence to Security and the Future of Work. Santa Monica, Calif.: RAND Corp, 7.
 
Peters, M. A. (2017). Technological unemployment: Educating for the fourth industrial revolution, Educational Philosophy and Theory, 49:1, 1-6.
 
Ramchurn, S. D., Vytelingum, P., Rogers, A., & Jennings, N. R. (2012). Putting the'smarts' into the smart grid: a grand challenge for artificial intelligence. Communications of the ACM, 55(4), 86-97.
 
Russell, S., Dewey, D., & Tegmark, M. (2015). Research priorities for robust and beneficial artificial intelligence. Ai Magazine, 36(4), 105-114.
 
Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,.
 
Scherer, M. U. (2015). Regulating artificial intelligence systems: Risks, challenges, competencies, and strategies. Harv. JL & Tech., 29, 353.
 
Simshaw, D., Terry, N., Hauser, K., & Cummings, M. L. (2015). Regulating healthcare robots: Maximizing opportunities while minimising risks. Rich. JL & Tech., 22, 1.
 
Stephan, K. D., Michael, K., Michael, M. G., Jacob, L., & Anesta, E. P. (2012). Social implications of technology: The past, the present, and the future. Proceedings of the IEEE, 100(Special Centennial Issue), 1752-1781.
 
Taeihagh, A., & Lim, H. S. M. (2018). Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks. Transport Reviews, 1-26.
 
Tsai, C. F., & Wu, J. W. (2008). Using neural network ensembles for bankruptcy prediction and credit scoring. Expert systems with applications, 34(4), 2639-2649.

Developments in Artificial Intelligence (AI) and Autonomous Systems (AS) offer various benefits that will revolutionise all aspects of society, ranging from search algorithms for online advertising (Goodfellow et al. 2016), signal processing (Karaboga et al. 2014), credit scoring (Tsai & Wu 2008; Brown & Mues 2012), medical diagnosis (Russell & Norvig 2016; Amato et al. 2013), autonomous vehicles (Fagnant & Kockelman 2015; Milakis et al. 2017; Taeihagh & Lim 2018), robotic medical assistants (Stahl and Coeckelbergh 2016) to autonomous weapon systems in warfare (Krishnan 2016). The rapid adoption of these technologies threaten to outpace the regulatory responses of governments around the world, which must grapple with the increasing magnitude and speed of these transformations.
 
The societal benefits of AI and AS have been widely acknowledged (Buchanan 2005; Taeihagh & Lim 2018; Ramchurn et al. 2012), but these technologies introduce risks and unintended consequences. New risks include and are not limited to unemployment (Acemoglu & Restrepo 2018; Frey & Osborne 2017; Peters 2017; Osoba & Welser IV 2017), safety risks (Taeihagh & Lim 2018; Kalra & Paddock 2016), privacy risks (Russell et al. 2015; Lim & Taeihagh 2018; Litman 2017), liability risks (Marchant & Lindor 2012; Čerka et al. 2015; Taeihagh & Lim 2018) and inequality (Makridakis 2017; Acemoglu & Restrepo 2018), which require appropriate governance mechanisms to be mitigated.  Traditional policy instruments may be ineffective due to insufficient information on industry developments, technological and regulatory uncertainties, coordination challenges between multiple regulatory bodies (Guihot et al. 2017), and the opacity of the underlying technology (Scherer 2016), which necessitate the use of more nuanced approaches to govern AI and AS.
 
Many studies have highlighted the urgency for and the challenges of governing AI and AS (Arkin 2009; Simshaw et al. 2015; Guihot et al. 2017; Scherer 2016; Krishnan 2016; Taeihagh & Lim 2018; Lim & Taeihagh 2018), which need to be addressed by answering the following key research questions:
 
• What are the types of unintended consequences and risks that can arise from the adoption of AI and AS in different domains (e.g. ICT, transport, energy, public sector, healthcare, water management etc.) and how can they be effectively managed and governed?
• How can AI and AS be responsibly deployed by public administrators?
• What are the implications of AI and AS on incumbent industries and how can the relationship between these technologies and incumbent industries be reconciled?
• Theoretical, conceptual and empirical approaches to understand new and unconventional regulatory approaches, governance strategies, institutions and discourses to govern risks arising from AI and AS.
• What types of standards or guidelines have been developed in industry and governments to manage the risks arising from AI and AS?
• How are risks arising from AI and AS allocated among different stakeholders vertically through the value chain (manufacturers, third-party service providers, consumers) and horizontally across different domains (transport, healthcare, financial sector, public agencies, ICT, education etc.)?
• Single and comparative case studies of governance responses across different countries, regions and domains to address the risks arising from AI and AS.