AI and housing discrimination: the case of mortgage applications

Zou, Leying; Khern-am-nuai, Warut

doi:10.1007/s43681-022-00234-9

Cited by 7 publications

(2 citation statements)

References 21 publications

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“…The racist implications of AI such as the disproportionately negative impact on Black people in predictive policing (Richardson et al, 2019), recidivism likelihood prediction (Angwin et al, 2016), and mortgage application evaluation (Zou and Khern-am-nuai, 2022), among others, have become textbook examples of algorithmic harm. These AI-facilitated types of discriminatory oversurveillance and exclusion that serve to perpetuate inequities and amplify racial hierarchies have been theorized as the “New Jim Code” (Benjamin, 2019) or “algorithmic racism” (Ali, 2016).…”

Section: The Roots Of Ai Empirementioning

confidence: 99%

AI Empire: Unraveling the interlocking systems of oppression in generative AI's global order

Tacheva,

Ramasubramanian

2023

Big Data & Society

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As artificial intelligence (AI) continues to captivate the collective imagination through the latest generation of generative AI models such as DALL-E and ChatGPT, the dehumanizing and harmful features of the technology industry that have plagued it since its inception only seem to deepen and intensify. Far from a “glitch” or unintentional error, these endemic issues are a function of the interlocking systems of oppression upon which AI is built. Using the analytical framework of “Empire,” this paper demonstrates that we live not simply in the “age of AI” but in the age of AI Empire. Specifically, we show that this networked and distributed global order is rooted in heteropatriarchy, racial capitalism, white supremacy, and coloniality and perpetuates its influence through the mechanisms of extractivism, automation, essentialism, surveillance, and containment. Therefore, we argue that any attempt at reforming AI from within the same interlocking oppressive systems that created it is doomed to failure and, moreover, risks exacerbating existing harm. Instead, to advance justice, we must radically transform not just the technology itself, but our ideas about it, and develop it from the bottom up, from the perspectives of those who stand the most risk of being harmed.

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Section: The Roots Of Ai Empirementioning

confidence: 99%

AI Empire: Unraveling the interlocking systems of oppression in generative AI's global order

Tacheva,

Ramasubramanian

2023

Big Data & Society

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“…While it may not be possible to pin this down perfectly, it need not be a mysterious affair once you work with a concrete scenario. To start with, consider the loan approval case mentioned earlier, as there are a few simulation studies available for this case measuring the financial impact of different choices (Kozodoi et al, 2022;Liu et al, 2018;Zou and Khern-am nuai, 2022).…”

Section: The Difference Principle As a Guide In Trade-offsmentioning

confidence: 99%

Navigating fairness measures and trade-offs

Buijsman

2023

AI Ethics

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To monitor and prevent bias in AI systems, we can use a wide range of (statistical) fairness measures. However, it is mathematically impossible to optimize all of these measures at the same time. In addition, optimizing a fairness measure often greatly reduces the accuracy of the system (Kozodoi et al., Eur J Oper Res 297:1083–1094, 2022). As a result, we need a substantive theory that informs us how to make these decisions and for what reasons. I show that by using Rawls’ notion of justice as fairness, we can create a basis for navigating fairness measures and the accuracy trade-off. In particular, this leads to a principled choice focusing on both the most vulnerable groups and the type of fairness measure that has the biggest impact on that group. This also helps to close part of the gap between philosophical accounts of distributive justice and the fairness literature that has been observed by (Kuppler et al. Distributive justice and fairness metrics in automated decision-making: How much overlap is there? arXiv preprint arXiv:2105.01441, 2021), and to operationalise the value of fairness.

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The fine line between automation and augmentation in website usability evaluation

Esposito,

Desolda,

Lanzilotti

2024

Sci Rep

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Artificial Intelligence (AI) systems are becoming widespread in all aspects of society, bringing benefits to the whole economy. There is a growing understanding of the potential benefits and risks of this type of technology. While the benefits are more efficient decision processes and industrial productivity, the risks may include a potential progressive disengagement of human beings in crucial aspects of decision-making. In this respect, a new perspective is emerging that aims at reconsidering the centrality of human beings while reaping the benefits of AI systems to augment rather than replace professional skills: Human-Centred AI (HCAI) is a novel framework that posits that high levels of human control do not contradict high levels of computer automation. In this paper, we investigate the two antipodes, automation vs augmentation, in the context of website usability evaluation. Specifically, we have analyzed whether the level of automation provided by a tool for semi-automatic usability evaluation can support evaluators in identifying usability problems. Three different visualizations, each one corresponding to a different level of automation, ranging from a full-automation approach to an augmentation approach, were compared in an experimental study. We found that a fully automated approach could help evaluators detect a significant number of medium and high-severity usability problems, which are the most critical in a software system; however, it also emerged that it was possible to detect more low-severity usability problems using one of the augmented approaches proposed in this paper.

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AI and housing discrimination: the case of mortgage applications

Cited by 7 publications

References 21 publications

AI Empire: Unraveling the interlocking systems of oppression in generative AI's global order

AI Empire: Unraveling the interlocking systems of oppression in generative AI's global order

Navigating fairness measures and trade-offs

The fine line between automation and augmentation in website usability evaluation

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