Our Research

Regulating Personal Data Usage in Covid-19 Control Conditions

Published Date: 09/06/2022

Mark Findlay and Nydia Remolina

Doing it Online: Is Mediation Ready for the AI Age?

Published Date: 09/06/2022

Nadja Alexander

Gauging the Acceptance of Contract-Tracing Technology: An Empirical Study of Singapore Residents' Concerns and Trust in Information Sharing

Published Date: 09/06/2022

Ong Ee Ing and Loo Wee Ling

Revaluing Labour? Secondary Data Imperialism in Platform Economies

Published Date: 09/06/2022

Mark Findlay and Josephine Seah

Regulatory Insights on Artificial Intelligence: Research for Policy

Published Date: 09/06/2022

Mark Findlay, Jolyon Ford, Josephine Seah, Dilan Thampapillai

Models and Data Trade Regulation and the Road to an Agreement

Published Date: 09/06/2022

Henry Gao

Rule of Law, Legitimacy, and Effective COVID-19 Control Technologies: Arbitrary Powers and Its Influence on Citizens’ Compliance

Published Date: 08/06/2022

Loo, Jane and Findlay, Mark James, Rule of Law, Legitimacy, and Effective COVID-19 Control Technologies: Arbitrary Powers and Its Influence on Citizens’ Compliance (June 1, 2022). SMU Centre for AI & Data Governance Research Paper Forthcoming, Available at SSRN: https://ssrn.com/abstract=4124676

The Reasonableness Standard of Compliance in The Singapore Personal Data Protection Act

Published Date: 05/06/2022

Warren B Chik (2022) 34 SAcLJ 352

An End-to-End Pipeline from Law Text to Logical Formulas

Published Date: 01/06/2022

Author(s): Aarne RANTA, Chalmers University of Technology, Inari LISTENMAA, Singapore Management University, Jerrold SOH, Singapore Management University, Meng Weng (HUANG Mingrong) WONG, Singapore Management University

This paper develops a pipeline for converting natural English law texts into logical formulas via a series of structural representations. The goal is to study how law-to-logic translation can be achieved with a sequence of well-defined steps. The texts are first parsed using a formal grammar derived from light-weight text annotations designed to minimise the need for manual grammar construction. An intermediate representation, called assembly logic, is then used for logical interpretation and supports translations to different back-end logics and visualisations. The approach, while rule-based and explainable, is also robust: it can deliver useful results from day one, but allows subsequent refinements and variations. While some work is needed to extend the method to new laws, the software presented here reduces the marginal effort necessary. As a case study, we demonstrate our approach on one part of Singapore’s Personal Data Protection Act. Our code is available open-source.

RANTA, Aarne; LISTENMAA, Inari; SOH, Jerrold; and WONG, Meng Weng (HUANG Mingrong). An end-to-end pipeline from law text to logical formulas. (2022). Legal knowledge and information systems: Proceedings of the 35th International Conference, Saarbrücken, Germany, 2022 December 14-16. 362, 237-242.

[White Paper] Rule of Law, Legitimacy and Effective COVID-19 Control Technologies

Published Date: 31/05/2022

Julinda Beqiraj, Akanksha Bisoyi, Chirstian Djeffal, Mark Findlay, Jane Loo, Ong Li Min

Rule of Law, Legitimacy, and Effective COVID-19 Control Technologies: Arbitrary Powers and their Influence on Citizens’ Compliance

Published Date: 31/05/2022

Jane Loo and Mark Findlay

User Guided Abductive Proof Generation for Answer Set Programming Queries

Published Date: 18/05/2022

Author(s): Avishkar MAHAJAN, Martin STRECKER, Meng Weng (HUANG Mingrong) WONG, Singapore Management University

We present a method for generating possible proofs of a query with respect to a given Answer Set Programming (ASP) rule set using an abductive process where the space of abducibles is automatically constructed just from the input rules alone. Given a (possibly empty) set of user provided facts, our method infers any additional facts that may be needed for the entailment of a query and then outputs these extra facts, without the user needing to explicitly specify the space of all abducibles. We also present a method to generate a set of directed edges corresponding to the justification graph for the query. Furthermore, through different forms of implicit term substitution, our method can take user provided facts into account and suitably modify the abductive solutions. Past work on abduction has been primarily based on goal directed methods. However these methods can result in solvers that are not truly declarative. Much less work has been done on realizing abduction in a bottom up solver like the Clingo ASP solver. We describe novel ASP programs which can be run directly in Clingo to yield the abductive solutions and directed edge sets without needing to modify the underlying solving engine.

MAHAJAN, Avishkar; STRECKER, Martin; and WONG, Meng Weng (HUANG Mingrong). User guided abductive proof generation for answer set programming queries. (2022). Proceedings of the 24th International Symposium on Principles and Practice of Declarative Programming, Tbilisi, Georgia, 2022 September 20-22. 1-14.

Automating Defeasible Reasoning in Law

Published Date: 17/05/2022

Author(s): How Khang LIM, Singapore Management University, Avishkar MAHAJAR, Singapore Management University, Martin STRECKER, Meng Weng WONG, Singapore Management University

The paper studies defeasible reasoning in rule-based systems, in particular about legal norms and contracts. We identify rule modifiers that specify how rules interact and how they can be overridden. We then define rule transformations that eliminate these modifiers, leading in the end to a translation of rules to formulas. For reasoning with and about rules, we contrast two approaches, one in a classical logic with SMT solvers, which is only briefly sketched, and one using non-monotonic logic with Answer Set Programming solvers, described in more detail.

LIM, How Khang; MAHAJAR, Avishkar; STRECKER, Martin; and WONG, Meng Weng. Automating defeasible reasoning in law with answer set programming. (2022). ICLP 2022: Proceedings of the International Conference on Logic Programming 2022 Workshops: Haifa, Israel, 31 July - 1 August: Workshop on Goal-Directed Execution of Answer Set Programs 2nd GDE 2022, August 1. 1-11.

Towards CNL-based Verbalization of Computational Contracts

Published Date: 04/05/2022

Author(s): Inari LISTENMAA, Singapore Management University, Maryam HANAFIAH, Singapore Management University, Regina CHEONG, Singapore Management University, Andreas KALLBERG, Singapore Management University

At CCLAW, our work aims at computerizing legal reasoning in a manner that is both formally precise and intuitively accessible to legal experts. The cornerstone is a domain specific language (DSL) for the legal domain, called L4, which can be converted to natural language but is also the basis for formal verification procedures.

L4's applied focus places it within the "Rules as Code" movement (e.g. OpenFisca, Catala) that itself draws on early computational law thinking. But rather than focusing on encoding laws into existing programming languages, we devise an external DSL designed for legal specification. From this specification, we generate a range of output formats. Key augmentations include IDE support, formal verification engines, transpilation to operational rule engines, and natural language generation (NLG). The latter is done via a controlled natural language (CNL) implemented in Grammatical Framework (GF), and is the focus of this paper.

LISTENMAA, Inari; HANAFIAH, Maryam; CHEONG, Regina; and KALLBERG, Andreas. Towards CNL-based verbalization of computational contracts. (2021). CNL 2020/21 7th International Workshop in Controlled Natural Language: Workshop Proceedings, 2021, September 8-9. 1-7.

Democratizing AI

Published Date: 03/03/2022

Dr Mark Findlay

Digitised Justice: The New Two Tiers?

Published Date: 26/02/2022

Loo, J., Findlay, M. Digitised Justice: The New Two Tiers?. Crim Law Forum (2022). https://doi.org/10.1007/s10609-022-09431-x

Data Reuse and its Impacts on Digital Labour Platforms

Published Date: 17/10/2021

Choo, Mabel, Zi Ling and Findlay, Mark James, Data Reuse and its Impacts on Digital Labour Platforms (October 18, 2021). SMU Centre for AI & Data Governance Research Paper No. 13

The Promises and Perils of Robo-Advisers: Challenges and Regulatory Approaches

Published Date: 26/09/2021

Gurrea-Martínez, Aurelio and Wan, Wai Yee, The Promises and Perils of Robo-Advisers: Challenges and Regulatory Approaches (September 27, 2021). SMU Centre for AI & Data Governance Research Paper No. 2021/11, Available at SSRN: https://ssrn.com/abstract=3931448

AI, Data and Private Law: The Theory-Practice Interface

Published Date: 22/09/2021

Gary CHAN Kok Yew and YIP Man

Robots in Community

Published Date: 11/09/2021

Wong, Willow and Findlay, Mark James, Robots in Community (September 12, 2021). SMU Centre for AI & Data Governance Research Paper No. 11/2021, Available at SSRN: https://ssrn.com/abstract=3929295 or http://dx.doi.org/10.2139/ssrn.3929295

Digital Contact Tracing – An Examination of Uptake in UK and Germany

Published Date: 06/09/2021

Wee, Alicia and Findlay, Mark James, Digital Contact Tracing – An Examination of Uptake in UK and Germany (September 1, 2021). SMU Centre for AI & Data Governance Research Paper No. 10, Available at SSRN: https://ssrn.com/abstract=3915303 or http://dx.doi.org/10.2139/ssrn.3915303

The Vulnerability Project : The Impact of COVID-19 on Vulnerable Groups

Published Date: 30/08/2021

Mark Findlay, Jane Loo, Josephine Seah, Alicia Wee, Sharanya Shanmugam, and Mabel Choo An epub copy can be downloaded from our Dropbox. 

Introducing the Vulnerability Project

Published Date: 17/08/2021

Josephine Seah and Mark Findlay

Rethinking what we owe each other

Published Date: 12/08/2021

Loo, Jane and Wong, Yasmine, Rethinking What We Owe Each Other (August 13, 2021). SMU Centre for AI & Data Governance Research Paper No. 08/2021, Available at SSRN: https://ssrn.com/abstract=3906442 or http://dx.doi.org/10.2139/ssrn.3906442

Globalisation, Populism, Pandemics and the Law: The Anarchy and the Ecstasy

Published Date: 31/07/2021

Mark Findlay, Professor, Yong Pung How School of Law and Director, Centre for AI and Data Governance, Singapore Management University, Honorary Professor, College of Law, Australian National University, Visiting Professorial Fellow, Law Faculty, University of New South Wales, Australia and Honorary Fellow, School of Law, University of Edinburgh, UK Publication Date: 2021 | ISBN: 978 1 78897 684 8 | Extent: 240 pp