Fairness in AI: challenges in bridging the gap between algorithms and law

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00379068" target="_blank" >RIV/68407700:21230/24:00379068 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/ICDEW61823.2024.00034" target="_blank" >https://doi.org/10.1109/ICDEW61823.2024.00034</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICDEW61823.2024.00034" target="_blank" >10.1109/ICDEW61823.2024.00034</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fairness in AI: challenges in bridging the gap between algorithms and law

Popis výsledku v původním jazyce

In this paper we examine algorithmic fairness from the perspective of law aiming to identify best practices and strategies for the specification and adoption of fairness definitions and algorithms in real-world systems and use cases. We start by providing a brief introduction of current anti-discrimination law in the European Union and the United States and discussing the concepts of bias and fairness from an legal and ethical viewpoint. We then proceed by presenting a set of algorithmic fairness definitions by example, aiming to communicate their objectives to non-technical audiences. Then, we introduce a set of core criteria that need to be taken into account when selecting a specific fairness definition for real-world use case applications. Finally, we enumerate a set of key considerations and best practices for the design and employment of fairness methods on real-world AI applications.

Název v anglickém jazyce

Fairness in AI: challenges in bridging the gap between algorithms and law

Popis výsledku anglicky

In this paper we examine algorithmic fairness from the perspective of law aiming to identify best practices and strategies for the specification and adoption of fairness definitions and algorithms in real-world systems and use cases. We start by providing a brief introduction of current anti-discrimination law in the European Union and the United States and discussing the concepts of bias and fairness from an legal and ethical viewpoint. We then proceed by presenting a set of algorithmic fairness definitions by example, aiming to communicate their objectives to non-technical audiences. Then, we introduce a set of core criteria that need to be taken into account when selecting a specific fairness definition for real-world use case applications. Finally, we enumerate a set of key considerations and best practices for the design and employment of fairness methods on real-world AI applications.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW)

ISBN

979-8-3503-8404-8

ISSN

1943-2895

e-ISSN

—

Počet stran výsledku

9

Strana od-do

217-225

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

New York

Místo konání akce

Utrecht

Datum konání akce

13. 5. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001259407300036