Unveiling Antiblackness in AI Development Process

Core Concepts

AI development perpetuates antiblack technologies due to the exclusion of Blackness from the concept of humanity.

Abstract

Introduction to Sylvia Wynter's concept of the biocentric Man genre.
Deconstruction of the typical AI development process into 6 stages.
Impact of AI technologies on marginalized communities.
Examples of harmful consequences in healthcare and surveillance.
Analysis of key stages in AI development process and their implications.
Proposal for recentering technology on human experiences.
Discussion on racialized artifacts and potential solutions.

Stats

"Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations." - Nature Medicine, 27(12), 2176–2182.
"Racial discrimination in face recognition technology." - Science in the News.
"Israel escalates surveillance of Palestinians with facial recognition program in West Bank." - The Washington Post.

Quotes

"We continue to produce harmful technologies so long as it is only at the expense of Black bodies."
"Progress for some at the regression of others is not really progress at all."
"Taking reforming actions at this stage is unpopular but we need to become comfortable asking why some things are being made at all."

Key Insights Distilled From

Identifying Potential Inlets of Man in the Artificial Intelligence Development Process

by Deja Workman... at arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.14658.pdf

Deeper Inquiries

How can AI development processes be restructured to prioritize human-centered design?

To restructure AI development processes for human-centered design, it is essential to incorporate diverse perspectives and consider the impact on marginalized communities. One approach is to involve interdisciplinary teams with varied backgrounds in the development process. This ensures that different viewpoints are considered, leading to more inclusive and ethical outcomes. Additionally, conducting thorough research on potential biases in data collection and model training is crucial. Implementing transparency measures throughout the process can help identify and address any discriminatory practices.
Furthermore, integrating feedback loops into the development cycle allows for continuous improvement based on user experiences and societal impacts. Prioritizing explainability and accountability in AI systems ensures that decisions made by algorithms are understandable and justifiable. By centering ethics as a core component of AI development, developers can proactively mitigate harm and promote fairness in technology.

What are the ethical implications of excluding diverse perspectives from AI development?

Excluding diverse perspectives from AI development has significant ethical implications as it perpetuates bias, discrimination, and inequity within technological advancements. When diverse voices are not represented in decision-making processes, there is a higher likelihood of creating systems that reflect the biases of a homogenous group or reinforce existing power structures.
This exclusion can lead to harmful consequences for marginalized communities who may be disproportionately impacted by biased algorithms or discriminatory practices embedded in AI technologies. It also undermines principles of fairness, transparency, and accountability in artificial intelligence systems.
By excluding diverse perspectives, developers risk amplifying systemic inequalities rather than addressing them through innovative solutions. Ethical considerations demand inclusivity at every stage of AI development to ensure that technologies serve all members of society equitably.

How can historical discrimination be addressed within technological advancements?

Addressing historical discrimination within technological advancements requires a multifaceted approach that acknowledges past injustices while actively working towards equitable solutions moving forward.
One key step is promoting diversity and inclusion within tech industries by fostering environments where individuals from underrepresented groups have equal opportunities for participation and leadership roles. This helps bring varied perspectives to the table during decision-making processes related to technology design and implementation.
Additionally, implementing robust ethics guidelines focused on anti-discrimination principles can help prevent biased outcomes in technological developments. Conducting regular audits on datasets used for training machine learning models helps identify any historical biases present in the data which could perpetuate discriminatory practices if left unaddressed.
Moreover, engaging with affected communities through participatory design approaches ensures that their needs are prioritized when developing new technologies or updating existing ones. By actively listening to those impacted by historical discrimination, technologists can create more inclusive solutions that contribute positively towards societal progress.