Reading agendas between the lines, an exercise

Sileno, Giovanni; Boer, Alexander; Engers, Tom van

doi:10.1007/s10506-017-9196-7

Cited by 5 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The OPLEL analysis for AI and robotics is shown in Table 6. Examples of AI in real estate include blockchain taxation, smart property ownership, and automated renting [91]. There is the detection of concrete swap-based tax fraud schemes [92] and unit selling price prediction in Bari Italy [93], and many other applications, including real estate business forecasting [85], sales renaissance by LG to cut off customer visits to service centres [94] and machine learning-based sale and build decisions [95].…”

Section: Artificial Intelligence (Ai) and Roboticsmentioning

confidence: 99%

A Systematic Review of Smart Real Estate Technology: Drivers of, and Barriers to, the Use of Digital Disruptive Technologies and Online Platforms

2018

View full text Add to dashboard Cite

Real estate needs to improve its adoption of disruptive technologies to move from traditional to smart real estate (SRE). This study reviews the adoption of disruptive technologies in real estate. It covers the applications of nine such technologies, hereby referred to as the Big9. These are: drones, the internet of things (IoT), clouds, software as a service (SaaS), big data, 3D scanning, wearable technologies, virtual and augmented realities (VR and AR), and artificial intelligence (AI) and robotics. The Big9 are examined in terms of their application to real estate and how they can furnish consumers with the kind of information that can avert regrets. The review is based on 213 published articles. The compiled results show the state of each technology’s practice and usage in real estate. This review also surveys dissemination mechanisms, including smartphone technology, websites and social media-based online platforms, as well as the core components of SRE: sustainability, innovative technology and user centredness. It identifies four key real estate stakeholders—consumers, agents and associations, government and regulatory authorities, and complementary industries—and their needs, such as buying or selling property, profits, taxes, business and/or other factors. Interactions between these stakeholders are highlighted, and the specific needs that various technologies address are tabulated in the form of a what, who and how analysis to highlight the impact that the technologies have on key stakeholders. Finally, stakeholder needs as identified in the previous steps are matched theoretically with six extensions of the traditionally accepted technology adoption model (TAM), paving the way for a smoother transition to technology-based benefits for consumers. The findings pertinent to the Big9 technologies in the form of opportunities, potential losses and exploitation levels (OPLEL) analyses highlight the potential utilisation of each technology for addressing consumers’ needs and minimizing their regrets. Additionally, the tabulated findings in the form of what, how and who links the Big9 technologies to core consumers’ needs and provides a list of resources needed to ensure proper information dissemination to the stakeholders. Such high-quality information can bridge the gap between real estate consumers and other stakeholders and raise the state of the industry to a level where its consumers have fewer or no regrets. The study, being the first to explore real estate technologies, is limited by the number of research publications on the SRE technologies that has been compensated through incorporation of online reports.

show abstract

Section: Artificial Intelligence (Ai) and Roboticsmentioning

confidence: 99%

A Systematic Review of Smart Real Estate Technology: Drivers of, and Barriers to, the Use of Digital Disruptive Technologies and Online Platforms

2018

View full text Add to dashboard Cite

show abstract

“…[9]): rule based, in which an expert identifies a set of rules that indicate evidence likely to be related to non-compliant activity (see eg. [8]); and machine-learning based, where a dataset of evidence related to usually both compliant or non-compliant activity is used to train a non-compliance classifier over the entire behavioural space [4]. Unfortunately, sample datasets of fraudulent behaviour suffer from class imbalance; there is only a relatively small amount of labeled instances of non-compliance compared to labeled instances of compliance.…”

Section: Introductionmentioning

confidence: 99%

A Compression and Simulation-Based Approach to Fraud Discovery

Fratrič

Sileno

Engers

et al. 2022

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

With the uptake of digital services in public and private sectors, the formalization of laws is attracting increasing attention. Yet, non-compliant fraudulent behaviours (money laundering, tax evasion, etc.)—practical realizations of violations of law—remain very difficult to formalize, as one does not know the exact formal rules that define such violations. The present work introduces a methodological framework aiming to discover non-compliance through compressed representations of behaviour, considering a fraudulent agent that explores via simulation the space of possible non-compliant behaviours in a given social domain. The framework is founded on a combination of utility maximization and active learning. We illustrate its application on a simple social domain. The results are promising, and seemingly reduce the gap on fundamental questions in AI and Law, although this comes at the cost of developing complex models of the simulation environment, and sophisticated reasoning models of the fraudulent agent.

show abstract

Like Circles in the Water: Responsibility as a System-Level Function

Sileno

Boer

Gordon

et al. 2021

AI Approaches to the Complexity of Legal Systems XI-XII

Self Cite

View full text Add to dashboard Cite

What eventually determines the semantics of algorithmic decision-making in not the program artefact, nor-if applicable-the data used to create it, but the preparatory (enabling) and consequent (enabled) practices holding in the environment (computational and human) in which such algorithmic procedure is embedded. The notion of responsibility captures a very similar construct: in all human societies actions are evaluated in terms of the consequences they could reasonably cause, and of the reasons that motivate them. But to what extent does this function exist in computational systems? The paper aims to sketch links between several of the approaches and concepts proposed for responsible computing, from AI to networking, identifying gaps and possible directions for operationalization.

show abstract

Reading agendas between the lines, an exercise

Cited by 5 publications

References 32 publications

A Systematic Review of Smart Real Estate Technology: Drivers of, and Barriers to, the Use of Digital Disruptive Technologies and Online Platforms

A Systematic Review of Smart Real Estate Technology: Drivers of, and Barriers to, the Use of Digital Disruptive Technologies and Online Platforms

A Compression and Simulation-Based Approach to Fraud Discovery

Like Circles in the Water: Responsibility as a System-Level Function

Contact Info

Product

Resources

About