Nothing Special   »   [go: up one dir, main page]
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Computers and Society

arXiv:2303.03929 (cs)
[Submitted on 28 Feb 2023]

Title:Towards Measuring Ethicality of an Intelligent Assistive System

Authors:M. Salman Shaukat, J.-C. Põder, Sebastian Bader, Thomas Kirste
View PDF
Abstract:Artificial intelligence (AI) based assistive systems, so called intelligent assistive technology (IAT) are becoming increasingly ubiquitous by each day. IAT helps people in improving their quality of life by providing intelligent assistance based on the provided data. A few examples of such IATs include self-driving cars, robot assistants and smart-health management solutions. However, the presence of such autonomous entities poses ethical challenges concerning the stakeholders involved in using these systems. There is a lack of research when it comes to analysing how such IAT adheres to provided ethical regulations due to ethical, logistic and cost issues associated with such an analysis. In the light of the above-mentioned problem statement and issues, we present a method to measure the ethicality of an assistive system. To perform this task, we utilised our simulation tool that focuses on modelling navigation and assistance of Persons with Dementia (PwD) in indoor environments. By utilising this tool, we analyse how well different assistive strategies adhere to provided ethical regulations such as autonomy, justice and beneficence of the stakeholders.
Comments: In the 1st AITHICS workshop (Artificial Intelligence and Ethics) held at 44th German Conference on Artificial Intelligence (KI-2021)
Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
Cite as: arXiv:2303.03929 [cs.CY]
  (or arXiv:2303.03929v1 [cs.CY] for this version)
  https://doi.org/10.48550/arXiv.2303.03929

Submission history

From: Muhammad Salman Shaukat [view email]
[v1] Tue, 28 Feb 2023 14:59:17 UTC (5,588 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
license icon view license
Current browse context:
cs.CY
< prev   |   next >
new | recent | 2023-03
Change to browse by:
cs
cs.AI

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)