Public Policy and Regulatory Challenges of Artificial Intelligence (AI)

Abstract

Artificial Intelligence (AI) usage is rapidly expanding in our society. Private sector has already taken the leap of faith in using AI for efficiency and for generating better value for the customers and shareholders. The promise of AI is quite alluring for the governments as well. It promises to be the breakthrough technology which can catapult public sector to hitherto unseen efficiency and productivity. It has the potential to truly transform the public service delivery and the way government interfaces with citizensfrom a demand driven model to a predictive model of public service delivery. However, there are a large number of pitfalls and blind-spots associated with AI, which make its adoption in government particularly challenging. For successful adoption of AI in public sector, governments must understand these challenges clearly and lay down regulatory public policies to ensure that the possible adverse impacts (such as exclusion, bias etc) of AI are mitigated. This paper attempts to systematically explore these challenges with a view to enable public policy makers to respond to them.

Ai Integration in Business Analytics: A Review of Usa and African Trends

“…In the adoption of artificial intelligence (AI), commonalities and disparities exist across different industries and regions. Shared challenges in AI adoption, such as regulatory differences, cultural influences, and economic variations, are evident (Kar et al, 2021;Misra et al, 2020;Sun & Medaglia, 2019). For instance, Misra et al (2020) highlighted the regulatory challenges of AI adoption, emphasizing differences in policy priorities among countries like Germany, India, Japan, and Canada (Misra et al, 2020).…”

“…Shared challenges in AI adoption, such as regulatory differences, cultural influences, and economic variations, are evident (Kar et al, 2021;Misra et al, 2020;Sun & Medaglia, 2019). For instance, Misra et al (2020) highlighted the regulatory challenges of AI adoption, emphasizing differences in policy priorities among countries like Germany, India, Japan, and Canada (Misra et al, 2020). Similarly, Sun & Medaglia (2019) discussed challenges in the public healthcare sector, indicating the need for addressing perceived challenges in e-government areas (Sun & Medaglia, 2019).…”

“…Additionally, Kanti et al (2022) focused on AI adoption in supply chain risk management, indicating the relevance of scale development and validation, which is a common trend in industry-specific AI adoption (Kanti et al, 2022). On the other hand, disparities and unique factors in AI adoption are evident, including regulatory differences, cultural influences, and economic variations (Kar et al, 2021;Misra et al, 2020;Gualdi & Cordella, 2021;. Gualdi & Cordella, 2021; emphasized the question of accountability in AI decision-making, highlighting the disparities in public sector AI adoption and the need for a new approach to accountability (Gualdi & Cordella, 2021).…”

Determinants of Smart Digital Infrastructure Diffusion for Urban Public Services

“…Lee (2010) proposed maturity models involving interaction, transaction, participation and involvement from citizen services perspective that leads to maturity levels of presenting, assimilating, reforming, morphing and finally eGovernance. There are regulatory and policy aspects involving smart technologies like Machine Learning and Artificial intelligence and data security and privacy (Misra et al, 2020). However several deterrents to digitization in social and public sector exist -Hooda-Nandal and Singla (2019) mentioned lack of governments campaign and citizens emotional attachment, Chatterji (2018) cited infrastructure and manpower shortage and complexity in technology implementation, Sharma et al (2021) mentioned inadequate awareness, accessibility and high transaction cost, Shareef et al (2014) highlighted lack of high quality, reliable and secured digital technology platform as some of the major limitations.…”