Dataset Information

The Conflict Between People's Urge to Punish AI and Legal Systems.

ABSTRACT: Regulating artificial intelligence (AI) has become necessary in light of its deployment in high-risk scenarios. This paper explores the proposal to extend legal personhood to AI and robots, which had not yet been examined through the lens of the general public. We present two studies (N = 3,559) to obtain people's views of electronic legal personhood vis-à-vis existing liability models. Our study reveals people's desire to punish automated agents even though these entities are not recognized any mental state. Furthermore, people did not believe automated agents' punishment would fulfill deterrence nor retribution and were unwilling to grant them legal punishment preconditions, namely physical independence and assets. Collectively, these findings suggest a conflict between the desire to punish automated agents and its perceived impracticability. We conclude by discussing how future design and legal decisions may influence how the public reacts to automated agents' wrongdoings.

SUBMITTER: Lima G

PROVIDER: S-EPMC8606795 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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The Conflict Between People's Urge to Punish AI and Legal Systems.

Lima Gabriel G Cha Meeyoung M Jeon Chihyung C Park Kyung Sin KS

Frontiers in robotics and AI 20211108

Regulating artificial intelligence (AI) has become necessary in light of its deployment in high-risk scenarios. This paper explores the proposal to extend legal personhood to AI and robots, which had not yet been examined through the lens of the general public. We present two studies (<i>N</i> = 3,559) to obtain people's views of electronic legal personhood vis-à-vis existing liability models. Our study reveals people's desire to punish automated agents even though these entities are not recogni ...[more]

PMID: 34820425

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Project description:The lack of transparency is one of the artificial intelligence (AI)'s fundamental challenges, but the concept of transparency might be even more opaque than AI itself. Researchers in different fields who attempt to provide the solutions to improve AI's transparency articulate different but neighboring concepts that include, besides transparency, explainability and interpretability. Yet, there is no common taxonomy neither within one field (such as data science) nor between different fields (law and data science). In certain areas like healthcare, the requirements of transparency are crucial since the decisions directly affect people's lives. In this paper, we suggest an interdisciplinary vision on how to tackle the issue of AI's transparency in healthcare, and we propose a single point of reference for both legal scholars and data scientists on transparency and related concepts. Based on the analysis of the European Union (EU) legislation and literature in computer science, we submit that transparency shall be considered the "way of thinking" and umbrella concept characterizing the process of AI's development and use. Transparency shall be achieved through a set of measures such as interpretability and explainability, communication, auditability, traceability, information provision, record-keeping, data governance and management, and documentation. This approach to deal with transparency is of general nature, but transparency measures shall be always contextualized. By analyzing transparency in the healthcare context, we submit that it shall be viewed as a system of accountabilities of involved subjects (AI developers, healthcare professionals, and patients) distributed at different layers (insider, internal, and external layers, respectively). The transparency-related accountabilities shall be built-in into the existing accountability picture which justifies the need to investigate the relevant legal frameworks. These frameworks correspond to different layers of the transparency system. The requirement of informed medical consent correlates to the external layer of transparency and the Medical Devices Framework is relevant to the insider and internal layers. We investigate the said frameworks to inform AI developers on what is already expected from them with regards to transparency. We also discover the gaps in the existing legislative frameworks concerning AI's transparency in healthcare and suggest the solutions to fill them in.

Project description:BackgroundYouth involved in the legal system have disproportionately higher rates of problematic substance use than non-involved youth. Identifying and connecting legal-involved youth to substance use intervention is critical and relies on the connection between legal and behavioral health agencies, which may be facilitated by learning health systems (LHS). We analyzed the impact of an LHS intervention on youth legal and behavioral health personnel ratings of their cross-system collaboration. We also examined organizational climate toward evidence-based practice (EBP) over and above the LHS intervention.MethodsData were derived from a type II hybrid effectiveness trial implementing an LHS intervention with youth legal and community mental health centers (CMHCs) in eight Indiana counties. Using a stepped wedge design, counties were randomly assigned to one of three cohorts and stepped in at nine-month intervals. Counties were in the treatment phase for 18 months, after which they were in the maintenance phase. Youth legal system and CMHC personnel completed five waves of data collection (n=307 total respondents, ranging from 108-178 per wave). Cross-system collaboration was measured via the Cultural Exchange Inventory, organizational EBP climate via the Implementation Climate Scale and Implementation Citizenship Behavior Scale, and intervention via a dummy-coded indicator variable. We conducted linear mixed models to examine: 1) the treatment indicator, and 2) the treatment indicator and organizational EBP climate variables on cross-system collaboration.ResultsThe treatment indicator was not significantly associated with cross-system collaboration. When including the organizational EBP climate variables, the treatment indicator significantly predicted cross-system collaboration. Compared to the control phase, treatment (B=0.41, standard error [SE]=0.20) and maintenance (B=0.60, SE=0.29) phases were associated with greater cross-system collaboration output.ConclusionsThe analysis may have been underpowered to detect an effect; third variables may have explained variance in cross-system collaboration, and, thus, the inclusion of important covariates may have reduced residual errors and increased the estimation precision. The LHS intervention may have affected cross-system collaboration perception and offers a promising avenue of research to determine how systems work together to improve legal-involved-youth substance use outcomes. Future research is needed to replicate results among a larger sample and examine youth-level outcomes.Trial registrationClinicaltrials.gov identifier: NCT04499079. Registered 30 July 2020. https://clinicaltrials.gov/study/NCT04499079 .

Dataset Information

The Conflict Between People's Urge to Punish AI and Legal Systems.

Publications

The Conflict Between People's Urge to Punish AI and Legal Systems.

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