Araz Taeihagh

View Original

CALL FOR PAPERS - ICPP6 T13P03 - GOVERNANCE AND POLICY DESIGN LESSONS FOR TRUST BUILDING AND RESPONSIBLE USE OF AI, AUTONOMOUS SYSTEMS AND ROBOTICS

CALL FOR PAPERS

 

T13P03 - GOVERNANCE AND POLICY DESIGN LESSONS FOR TRUST BUILDING AND RESPONSIBLE USE OF AI, AUTONOMOUS SYSTEMS AND ROBOTICS

 

https://www.ippapublicpolicy.org/conference/icpp6-toronto-2023/panel-list/17/panel/governance-and-policy-design-lessons-for-trust-building-and-responsible-use-of-ai-autonomous-systems-and-robotics/1390

Abstract submission deadline: 31 January 2023

GENERAL OBJECTIVES, RESEARCH QUESTIONS AND SCIENTIFIC RELEVANCE

Artificial intelligence (AI), Autonomous Systems (AS) and Robotics are key features of the fourth industrial revolution, and their applications are supposed to add $15 trillion to the global economy by 2030 and improve the efficiency and quality of public service delivery (Miller & Sterling, 2019). A McKinsey global survey found that over half of the organisations surveyed use AI in at least one function (McKinsey, 2020). The societal benefits of AI, AS, and Robotics have been widely acknowledged (Buchanan 2005; Taeihagh & Lim 2019; Ramchurn et al. 2012), and the acceleration of their deployment is a disruptive change impacting jobs, the economic and military power of countries, and wealth concentration in the hands of corporations (Pettigrew et al., 2018; Perry & Uuk, 2019).

However, the rapid adoption of these technologies threatens to outpace the regulatory responses of governments around the world, which must grapple with the increasing magnitude and speed of these transformations (Taeihagh 2021). Furthermore, concerns about these systems' deployment risks and unintended consequences are significant for citizens and policymakers. Potential risks include malfunctioning, malicious attacks, and objective mismatch due to software or hardware failures (Page et al., 2018; Lim and Taeihagh, 2019; Tan et al., 2022). There are also safety, liability, privacy, cybersecurity, and industry risks that are difficult to address (Taeihagh & Lim, 2019) and The opacity in AI operations has also manifested in potential bias against certain groups of individuals that lead to unfair outcomes (Lim and Taeihagh 2019; Chesterman, 2021).

These risks require appropriate governance mechanisms to be mitigated, and traditional policy instruments may be ineffective due to insufficient information on industry developments, technological and regulatory uncertainties, coordination challenges between multiple regulatory bodies and the opacity of the underlying technology (Scherer 2016; Guihot et al. 2017; Taeihagh et al. 2021), which necessitate the use of more nuanced approaches to govern these systems. Subsequently, the demand for the governance of these systems has been increasing (Danks & London, 2017; Taeihagh, 2021).

CALL FOR PAPERS

Many studies have highlighted the urgency for and the challenges of governing AI, AS and Robotics (Firlej and Taeihagh 2021; He et al. 2020; Tan and Taeihagh 2021; Tan et al. 2021; Radu 2021; Taeihagh 2021). In this panel, we are interested in governance and policy design lessons for Responsible Use and Building  trust in AI, AS and Robotics by answering the following key research questions:

 

·      What governance and policy design lessons have been learnt so far in addressing risks and unintended consequences of adopting AI, AS and Robotics in different domains and geographies?

 ·      What are the challenges of responsible use of AI, AS and Robotics, particularly in the public sector?

 ·      What are the emerging theoretical, conceptual and empirical approaches to understanding new and unconventional regulatory approaches, governance strategies, institutions and discourses to govern these systems?

 ·      What lessons have been learnt so far from the public and private organisations' standard setting and development of guidelines in managing these systems?

 ·      How can the public and expert viewpoints be better considered for the regulation and governance of AI, AS, and Robotics to increase trust in AI?

 ·    What is the role of governments in promoting trustworthy AI and building trust in AI?

 

Abstract submission deadline (up to 500 words) 31 January 2023

 

REFERENCES

 Buchanan, B. G. (2005). A (very) brief history of artificial intelligence. Ai Magazine, 26(4), 53.

 Chesterman, S. (2021). Through a Glass, Darkly: Artificial Intelligence and the Problem of Opacity. The American Journal of Comparative Law, 69(2), 271-294.

Danks, D. (2019). The value of trustworthy AI. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 521–522.

 Guihot, M., Matthew, A. F., & Suzor, N. P. (2017). Nudging Robots: Innovative Solutions to Regulate Artificial Intelligence. Vand. J. Ent. & Tech. L., 20, 385.

 He, H., Gray, J., Cangelosi, A., Meng, Q., McGinnity, T. M., & Mehnen, J. (2020). The Challenges and Opportunities of Artificial Intelligence for Trustworthy Robots and Autonomous Systems. 2020 3rd International Conference on Intelligent Robotic and Control Engineering (IRCE), 68–74.

 Lim, H. S. M., & Taeihagh, A. (2019). Algorithmic decision-making in AVs: Understanding ethical and technical concerns for smart cities. Sustainability, 11(20), 5791.

 McKinsey. (2020, November 17). Global survey: The state of AI in 2020 | McKinsey.https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/global-survey-the-state-of-ai-in-2020

 Miller, H., & Stirling, R. (2019) The Government Artificial Intelligence (AI) Readiness Index Report 2019.https://www.oxfordinsights.com/ai-readiness2019

 Page, J., Bain, M., & Mukhlish, F. (2018, August). The risks of low level narrow artificial intelligence. In 2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR) (pp. 1-6). IEEE.

 Perry, B., & Uuk, R. (2019). AI governance and the policymaking process: key considerations for reducing AI risk. Big Data and Cognitive Computing, 3(2), 26.

 Pettigrew, S., Fritschi, L., & Norman, R. (2018). The potential implications of autonomous vehicles in and around the workplace. International Journal of Environmental Research and Public Health, 15(9), 1876.

 Radu, R. (2021). Steering the governance of artificial intelligence: national strategies in perspective. Policy and Society, 40(2), 178-193.

 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.

 Scherer, M. U. (2015). Regulating artificial intelligence systems: Risks, challenges, competencies, and strategies. Harv. JL & Tech., 29, 353.

Taeihagh, A. (2021). Governance of artificial intelligence. Policy and Society, 40(2), 137-157.

 Taeihagh, A., & Lim, H. S. M. (2019). Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks. Transport reviews, 39(1), 103-128.

 Taeihagh, A., Ramesh, M., & Howlett, M. (2021). Assessing the regulatory challenges of emerging disruptive technologies. Regulation & Governance, 15(4), 1009-1019.

Tan, S.Y., & Taeihagh, A. (2021). Adaptive governance of autonomous vehicles: Accelerating the adoption of disruptive technologies in Singapore. Government Information Quarterly, 38(2), 101546.

Tan, S.Y., Taeihagh, A., & Tripathi, A. (2021). Tensions and antagonistic interactions of risks and ethics of using robotics and autonomous systems in long-term care. Technological Forecasting and Social Change, 167, 120686.

 Tan, S., Taeihagh, A., & Baxter, K. (2022). The Risks of Machine Learning Systems. arXiv preprint arXiv:2204.09852.