A Legal Framework for AI Training Data

Hacker, Philipp

doi:10.2139/ssrn.3556598

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…Among others, Sandra Wachter, Brent Mittelstadt and Chris Russell have argued that the intuitive understanding of discrimination in algorithmic systems is part of why the European non-discrimination law is not equipped to deal with it. This causes difficulties for individuals who first must be able to recognize they are being put to disadvantage and then contest the treatment (also Hacker, 2018 ; Wachter et al, 2021 ; see n AI training data, Hacker, 2021b ).…”

Section: From Transparency To Human Oversight: Framings Of Algorithmi...mentioning

confidence: 99%

Research agenda for algorithmic fairness studies: Access to justice lessons for interdisciplinary research

Kontiainen

Koulu

Sankari

2022

Front. Artif. Intell.

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Access to justice is one of the fundamental legitimating principles underlying all modern Western legal systems, yet its role in critical algorithm studies remains underdeveloped. In historical and methodological terms, the access to justice movement showcased multi- and interdisciplinary research on legal phenomena. We argue that interdisciplinary research on AI ethics and regulation, datafication of society, and algorithmic governance could benefit from adopting access to justice as a vantage point for bridging the different approaches in the context of administering justice. To this end, we explore technological, legal, and societal intersections to demonstrate how law, social sciences, and algorithm studies could benefit from a historically more informed and holistic approach facilitating more “cost-effective” interdisciplinary research collaboration. Such approach could assist the substantive study of algorithmic fairness to contribute actionable systemic solutions on what we perceive as systemic challenges. We propose utilizing access to justice as a boundary object for interdisciplinary dialogue over algorithmic fairness while respecting the epistemic diversity of disciplines.

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Section: From Transparency To Human Oversight: Framings Of Algorithmi...mentioning

confidence: 99%

Research agenda for algorithmic fairness studies: Access to justice lessons for interdisciplinary research

Kontiainen

Koulu

Sankari

2022

Front. Artif. Intell.

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Implicaciones legales del web scraping en el entrenamiento de modelos de inteligencia artificial generativa

Pacheco Chaparro,

Barrero Ramírez

2024

propin

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El web scraping es una técnica que se usa para recopilar datos en Internet y almacenarlos en una base de datos. Ese proceso se usa, entre otras cosas, para entrenar modelos de inteligencia artificial generativa y ha generado controversia alrededor del mundo debido a sus riesgos legales. En este artículo se analizará la viabilidad legal del uso de técnicas de web scraping y se abordarán tensiones relacionadas con asuntos contractuales de los términos de servicio de las páginas web, los riesgos legales que se desprenden de estas técnicas y, en particular, del uso de obras protegidas en el entrenamiento de modelos de inteligencia artificial generativa, de protección de datos personales y de implicaciones penales; las licencias open source, open access y de Creative Commons, así como también los datos de dominio público y en cabeza del Estado colombiano. Este artículo pretende ser un marco teórico inicial para la discusión del web scraping en modelos de inteligencia artificial generativa, dado que, a la fecha de elaboración de este artículo, el desarrollo normativo y jurisprudencial sobre este tema es aún incipiente.

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A Legal Framework for AI Training Data

Cited by 2 publications

References 29 publications

Research agenda for algorithmic fairness studies: Access to justice lessons for interdisciplinary research

Research agenda for algorithmic fairness studies: Access to justice lessons for interdisciplinary research

Implicaciones legales del web scraping en el entrenamiento de modelos de inteligencia artificial generativa

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