Tackling Algorithmic Disability Discrimination in the Hiring Process: An
Ethical, Legal and Technical Analysis
- URL: http://arxiv.org/abs/2206.06149v1
- Date: Mon, 13 Jun 2022 13:32:37 GMT
- Title: Tackling Algorithmic Disability Discrimination in the Hiring Process: An
Ethical, Legal and Technical Analysis
- Authors: Maarten Buyl, Christina Cociancig, Cristina Frattone, Nele Roekens
- Abstract summary: We discuss concerns and opportunities raised by AI-driven hiring in relation to disability discrimination.
We establish some starting points and design a roadmap for ethicists, lawmakers, advocates as well as AI practitioners alike.
- Score: 2.294014185517203
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Tackling algorithmic discrimination against persons with disabilities (PWDs)
demands a distinctive approach that is fundamentally different to that applied
to other protected characteristics, due to particular ethical, legal, and
technical challenges. We address these challenges specifically in the context
of artificial intelligence (AI) systems used in hiring processes (or automated
hiring systems, AHSs), in which automated assessment procedures are subject to
unique ethical and legal considerations and have an undeniable adverse impact
on PWDs. In this paper, we discuss concerns and opportunities raised by
AI-driven hiring in relation to disability discrimination. Ultimately, we aim
to encourage further research into this topic. Hence, we establish some
starting points and design a roadmap for ethicists, lawmakers, advocates as
well as AI practitioners alike.
Related papers
- Open Problems in Technical AI Governance [93.89102632003996]
Technical AI governance refers to technical analysis and tools for supporting the effective governance of AI.
This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.
arXiv Detail & Related papers (2024-07-20T21:13:56Z) - Implications of the AI Act for Non-Discrimination Law and Algorithmic Fairness [1.5029560229270191]
The topic of fairness in AI has sparked meaningful discussions in the past years.
From a legal perspective, many open questions remain.
The AI Act might present a tremendous step towards bridging these two approaches.
arXiv Detail & Related papers (2024-03-29T09:54:09Z) - Why Fair Automated Hiring Systems Breach EU Non-Discrimination Law [0.0]
Employment selection processes that use automated hiring systems based on machine learning are becoming increasingly commonplace.
Algorithmic fairness and algorithmic non-discrimination are not the same.
This article examines a conflict between the two: whether such hiring systems are compliant with EU non-discrimination law.
arXiv Detail & Related papers (2023-11-07T11:31:00Z) - Queering the ethics of AI [0.6993026261767287]
The chapter emphasizes the ethical concerns surrounding the potential for AI to perpetuate discrimination.
The chapter argues that a critical examination of the conception of equality that often underpins non-discrimination law is necessary.
arXiv Detail & Related papers (2023-08-25T17:26:05Z) - Ethics in conversation: Building an ethics assurance case for autonomous
AI-enabled voice agents in healthcare [1.8964739087256175]
The principles-based ethics assurance argument pattern is one proposal in the AI ethics landscape.
This paper presents the interim findings of a case study applying this ethics assurance framework to the use of Dora, an AI-based telemedicine system.
arXiv Detail & Related papers (2023-05-23T16:04:59Z) - Human-Centric Multimodal Machine Learning: Recent Advances and Testbed
on AI-based Recruitment [66.91538273487379]
There is a certain consensus about the need to develop AI applications with a Human-Centric approach.
Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes.
We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
arXiv Detail & Related papers (2023-02-13T16:44:44Z) - 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) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - Case Study: Deontological Ethics in NLP [119.53038547411062]
We study one ethical theory, namely deontological ethics, from the perspective of NLP.
In particular, we focus on the generalization principle and the respect for autonomy through informed consent.
We provide four case studies to demonstrate how these principles can be used with NLP systems.
arXiv Detail & Related papers (2020-10-09T16:04:51Z) - Bias and Discrimination in AI: a cross-disciplinary perspective [5.190307793476366]
We show that finding solutions to bias and discrimination in AI requires robust cross-disciplinary collaborations.
We survey relevant literature about bias and discrimination in AI from an interdisciplinary perspective that embeds technical, legal, social and ethical dimensions.
arXiv Detail & Related papers (2020-08-11T10:02:04Z) - How Does NLP Benefit Legal System: A Summary of Legal Artificial
Intelligence [81.04070052740596]
Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the legal domain.
This paper introduces the history, the current state, and the future directions of research in LegalAI.
arXiv Detail & Related papers (2020-04-25T14:45:15Z)
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.