näkemys - Machine Learning - # Participatory Machine Learning Challenges

From Fitting Participation to Forging Relationships: The Challenges of Participatory ML

Q: How can brokers effectively balance power dynamics between participants and data scientists?

Brokers can effectively balance power dynamics by first acknowledging the inherent power differentials that exist within Participatory ML projects. To address this, brokers should strive to create a collaborative environment where all voices are heard and respected. This can be achieved through transparent communication, setting clear expectations for all parties involved, and actively seeking feedback from both participants and data scientists. One strategy is to involve participants in decision-making processes related to the project. By empowering participants to have a say in key aspects of the ML development process, such as problem formulation, data collection, model evaluation, and deployment decisions, brokers can ensure that their perspectives are valued and integrated into the project outcomes. Additionally, brokers should advocate for training programs or workshops that educate both participants and data scientists about each other's roles and expertise. This mutual understanding can help bridge any knowledge gaps and foster a more equitable collaboration between all stakeholders involved in the project.

Q: How can participatory approaches be extended beyond established engineering processes?

To extend participatory approaches beyond established engineering processes, brokers need to take on an activist role in advocating for meaningful participation throughout the entire ML development lifecycle. This involves not only "fitting" participants into existing processes but also forging long-term relationships with them to envision alternative futures through ML. One way to achieve this is by focusing on education and advocacy for end users. Brokers should engage with end users early on in the process to provide them with information about ML systems' functionalities and potential applications. By empowering end users with knowledge about how AI works and involving them in speculative design activities or policy discussions related to technology use cases, brokers can ensure that their interests are represented throughout the development process. Furthermore, brokers should pay attention to indirect stakeholders who may feel frustrated or marginalized by AI systems' deployment decisions. By creating mechanisms for these stakeholders to provide feedback on system performance or express concerns about algorithmic biases or ethical implications of AI technologies used within their communities, brokers can bridge the gap between frustration and constructive feedback from these groups. In summary, extending participatory approaches beyond established engineering processes requires a shift towards activism among participation brokers—advocating for inclusive practices across all stages of ML development while prioritizing relationships built on trust,respect,and shared decision-making among diverse stakeholder groups involved in AI projects.

Keskeiset käsitteet

The author argues for the evolution of the role of brokers in Participatory ML projects to balance value creation for design teams and participants, advocating for more equitable partnerships.

Tiivistelmä

The content explores the challenges faced in integrating messy contextual information with structured data formats in Participatory ML projects. It emphasizes the need for brokers to become educators and advocates for end users while managing power dynamics and frustration from indirect stakeholders.

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arxiv.org

Tilastot

Our findings demonstrate the inherent challenges of integrating messy contextual information generated through participation with the structured data formats required by ML workflows.
Several interviewees created infrastructure to support the data collection stage, including tutorials and training.
Brokers reported difficulty engaging with indirect stakeholders relative to end users.
Interviewees noted a gap in literature regarding how brokers facilitate meaningful participation and manage power dynamics in successful Participatory ML projects.
Brokers encountered two sets of tensions that made collaborative engagement with participants difficult: conﬂict between messy contextual information generated through participation and neat, machine-readable forms of information required in ML; uneven power dynamics within project contexts.

Lainaukset

"We advocate for evolution in the role of brokers to more equitably balance value generated in Participatory ML projects."
"Brokers must become educators and advocates for end users, while attending to frustration and dissent from indirect stakeholders."
"Interviewees reported that participants became frustrated when conversations narrowed to the data formats required for classification models."

Tärkeimmät oivallukset

From Fitting Participation to Forging Relationships

by Ned Cooper,A... klo arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06431.pdf

From Fitting Participation to Forging Relationships

Syvällisempiä Kysymyksiä

How can brokers effectively balance power dynamics between participants and data scientists?

Brokers can effectively balance power dynamics by first acknowledging the inherent power differentials that exist within Participatory ML projects. To address this, brokers should strive to create a collaborative environment where all voices are heard and respected. This can be achieved through transparent communication, setting clear expectations for all parties involved, and actively seeking feedback from both participants and data scientists.
One strategy is to involve participants in decision-making processes related to the project. By empowering participants to have a say in key aspects of the ML development process, such as problem formulation, data collection, model evaluation, and deployment decisions, brokers can ensure that their perspectives are valued and integrated into the project outcomes.
Additionally, brokers should advocate for training programs or workshops that educate both participants and data scientists about each other's roles and expertise. This mutual understanding can help bridge any knowledge gaps and foster a more equitable collaboration between all stakeholders involved in the project.

How can participatory approaches be extended beyond established engineering processes?

To extend participatory approaches beyond established engineering processes, brokers need to take on an activist role in advocating for meaningful participation throughout the entire ML development lifecycle. This involves not only "fitting" participants into existing processes but also forging long-term relationships with them to envision alternative futures through ML.
One way to achieve this is by focusing on education and advocacy for end users. Brokers should engage with end users early on in the process to provide them with information about ML systems' functionalities and potential applications. By empowering end users with knowledge about how AI works and involving them in speculative design activities or policy discussions related to technology use cases, brokers can ensure that their interests are represented throughout the development process.
Furthermore, brokers should pay attention to indirect stakeholders who may feel frustrated or marginalized by AI systems' deployment decisions. By creating mechanisms for these stakeholders to provide feedback on system performance or express concerns about algorithmic biases or ethical implications of AI technologies used within their communities, brokers can bridge the gap between frustration and constructive feedback from these groups.
In summary, extending participatory approaches beyond established engineering processes requires a shift towards activism among participation brokers—advocating for inclusive practices across all stages of ML development while prioritizing relationships built on trust,respect,and shared decision-making among diverse stakeholder groups involved in AI projects.