Trustworthy human-centric based Automated Decision-Making Systems
- URL: http://arxiv.org/abs/2401.06161v1
- Date: Fri, 22 Dec 2023 11:02:57 GMT
- Title: Trustworthy human-centric based Automated Decision-Making Systems
- Authors: Marcelino Cabrera and Carlos Cruz and Pavel Novoa-Hern\'andez and
David A. Pelta and Jos\'e Luis Verdegay
- Abstract summary: Automated Decision-Making Systems (ADS) have become pervasive across various fields, activities, and occupations, to enhance performance.
This research paper presents a thorough examination of the implications, distinctions, and ethical considerations associated with digitalization, digital transformation, and the utilization of ADS in contemporary society and future contexts.
- Score: 0.7048747239308888
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Automated Decision-Making Systems (ADS) have become pervasive across various
fields, activities, and occupations, to enhance performance. However, this
widespread adoption introduces potential risks, including the misuse of ADS.
Such misuse may manifest when ADS is employed in situations where it is
unnecessary or when essential requirements, conditions, and terms are
overlooked, leading to unintended consequences. This research paper presents a
thorough examination of the implications, distinctions, and ethical
considerations associated with digitalization, digital transformation, and the
utilization of ADS in contemporary society and future contexts. Emphasis is
placed on the imperative need for regulation, transparency, and ethical conduct
in the deployment of ADS.
Related papers
- Legitimate Power, Illegitimate Automation: The problem of ignoring legitimacy in automated decision systems [0.0]
Machine learning and artificial intelligence have spurred the widespread adoption of automated decision systems (ADS)
This paper shows that theorists often incorrectly conflate legitimacy with either public acceptance or other substantive values such as fairness, accuracy, expertise or efficiency.
arXiv Detail & Related papers (2024-04-24T06:29:54Z) - Online Decision Mediation [72.80902932543474]
Consider learning a decision support assistant to serve as an intermediary between (oracle) expert behavior and (imperfect) human behavior.
In clinical diagnosis, fully-autonomous machine behavior is often beyond ethical affordances.
arXiv Detail & Related papers (2023-10-28T05:59:43Z) - Causal Fairness Analysis [68.12191782657437]
We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings.
The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms.
Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
arXiv Detail & Related papers (2022-07-23T01:06:34Z) - Fairness in Agreement With European Values: An Interdisciplinary
Perspective on AI Regulation [61.77881142275982]
This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them.
We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives.
We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
arXiv Detail & Related papers (2022-06-08T12:32:08Z) - Perceptions of Fairness and Trustworthiness Based on Explanations in
Human vs. Automated Decision-Making [0.0]
Automated decision systems (ADS) have become ubiquitous in many high-stakes domains.
We conduct an online study with 200 participants to examine people's perceptions of fairness and trustworthiness towards ADS.
We find that people perceive ADS as fairer than human decision-makers.
arXiv Detail & Related papers (2021-09-13T09:14:15Z) - Appropriate Fairness Perceptions? On the Effectiveness of Explanations
in Enabling People to Assess the Fairness of Automated Decision Systems [0.0]
We argue that for an effective explanation, perceptions of fairness should increase if and only if the underlying ADS is fair.
In this in-progress work, we introduce the desideratum of appropriate fairness perceptions, propose a novel study design for evaluating it, and outline next steps towards a comprehensive experiment.
arXiv Detail & Related papers (2021-08-14T09:39:59Z) - Overcoming Failures of Imagination in AI Infused System Development and
Deployment [71.9309995623067]
NeurIPS 2020 requested that research paper submissions include impact statements on "potential nefarious uses and the consequences of failure"
We argue that frameworks of harms must be context-aware and consider a wider range of potential stakeholders, system affordances, as well as viable proxies for assessing harms in the widest sense.
arXiv Detail & Related papers (2020-11-26T18:09:52Z) - A survey of algorithmic recourse: definitions, formulations, solutions,
and prospects [24.615500469071183]
We focus on algorithmic recourse, which is concerned with providing explanations and recommendations to individuals who are unfavourably treated by automated decision-making systems.
We perform an extensive literature review, and align the efforts of many authors by presenting unified definitions, formulations, and solutions to recourse.
arXiv Detail & Related papers (2020-10-08T15:15:34Z) - Towards robust sensing for Autonomous Vehicles: An adversarial
perspective [82.83630604517249]
It is of primary importance that the resulting decisions are robust to perturbations.
Adversarial perturbations are purposefully crafted alterations of the environment or of the sensory measurements.
A careful evaluation of the vulnerabilities of their sensing system(s) is necessary in order to build and deploy safer systems.
arXiv Detail & Related papers (2020-07-14T05:25:15Z) - The Visual Social Distancing Problem [99.69094590087408]
We introduce the Visual Social Distancing problem, defined as the automatic estimation of the inter-personal distance from an image.
We discuss how VSD relates with previous literature in Social Signal Processing and indicate which existing Computer Vision methods can be used to manage such problem.
arXiv Detail & Related papers (2020-05-11T00:04:34Z) - The Risk to Population Health Equity Posed by Automated Decision
Systems: A Narrative Review [0.0]
Automated decisions being made have significant consequences for individual and population health.
Reports of issues arising from their use in health are already appearing.
There is a significant risk that use of automated decision systems in health will exacerbate existing population health inequities.
arXiv Detail & Related papers (2020-01-18T06:52:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.