Evaluating Potential Harms in Behavioral Sensing Technologies for Wellbeing Prediction

Core Concepts

Behavioral sensing technologies for wellbeing prediction, such as depression detection and student engagement prediction, can introduce potential harms to users due to a lack of context sensitivity in their design. These harms can manifest as identity-based biases against marginalized groups and situation-based biases arising from differences in data collection contexts.

Abstract

This paper proposes a framework for designing context-sensitive behavioral sensing technologies to identify and mitigate potential harms. The framework consists of six steps:

Comprehensively understand the context, including users' diverse backgrounds and situated settings.
Establish criteria for evaluating harms, such as fairness metrics and statistical methods to ensure biases are not due to random chance.
Collect inclusive datasets that represent the comprehensive context.
Develop context-sensitive algorithms that are aware of potential harms and can adapt to contextual changes.
Conduct harm evaluation and analysis, incorporating user feedback to mitigate identified biases.
Implement continuous maintenance strategies to update data and algorithms for responsible deployment.

The authors apply this framework to evaluate two real-world behavioral sensing technology studies: depression detection and student engagement prediction. The evaluations provide empirical evidence of potential identity-based and situation-based harms in these technologies, highlighting the importance of the proposed framework in designing more context-sensitive and responsible behavioral sensing systems.

Stats

"Only 5% of the research published in a leading journal of sensing technology, PACM IMWUT, from 2018 to 2022 addressed identity-based harm to users with sensitive attributes such as gender or race."
"Algorithms trained exclusively with data from iOS-based smartphones might exhibit bias against individuals of lower socioeconomic status who predominantly use less expensive Android-based smartphones."

Quotes

"A technology is context-sensitive when it accounts for diverse user backgrounds, needs, and situations of use to provide value to users."
"Adopting a theory-driven approach, we propose a framework for designing context-sensitive behavioral sensing technologies, aiming to identify and mitigate potential harms to users."

Key Insights Distilled From

Illuminating the Unseen: A Framework for Designing and Mitigating Context-induced Harms in Behavioral Sensing

by Han Zhang,Ve... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14665.pdf

Deeper Inquiries

How can the proposed framework be extended to address potential harms in other domains of sensing technologies beyond wellbeing prediction?

The proposed framework for designing context-sensitive behavioral sensing technologies can be extended to address potential harms in other domains by adapting the framework to the specific context and requirements of the new domain. Here are some ways to extend the framework:

Context Understanding: Begin by comprehensively understanding the context specific to the new domain. This involves identifying the diverse user backgrounds, needs, and situational factors that may impact the technology's performance and potential harms.

Establishing Evaluation Criteria: Develop criteria for evaluating potential harms in the new domain. This may involve identifying sensitive attributes, potential biases, and fairness metrics relevant to the specific context of the domain.

Data Collection: Collect inclusive datasets that capture the comprehensive contextual information necessary for the technology in the new domain. Ensure that the data represents a diverse range of demographics and situational factors.

Algorithm Design: Develop context-sensitive algorithms that are aware of potential harms and biases specific to the new domain. Consider how the algorithms can adapt to different contexts and mitigate biases effectively.

Harm Evaluation and Mitigation: Evaluate the behavioral sensing technology for potential harms and biases, incorporating user feedback and techniques to mitigate these issues. Engage with stakeholders in the new domain to understand their concerns and insights.

Continuous Maintenance: Implement strategies for the regular maintenance and update of data and algorithms to ensure responsible deployment in the new domain. Monitor the technology's performance over time and make necessary adjustments to address emerging issues.

By following these steps and customizing the framework to the unique characteristics of the new domain, technology builders can effectively design context-sensitive sensing technologies that mitigate potential harms and biases across a wide range of applications beyond wellbeing prediction.

What are the potential trade-offs and challenges in balancing fairness across multiple sensitive attributes when mitigating algorithmic biases?

Balancing fairness across multiple sensitive attributes when mitigating algorithmic biases can present several challenges and potential trade-offs. Some of the key considerations include:

Intersectionality: Individuals may belong to multiple sensitive groups simultaneously, leading to complex interactions between different attributes. Balancing fairness across these intersecting identities can be challenging and may require nuanced approaches to avoid reinforcing biases.

Fairness Metrics: Different fairness metrics may prioritize certain attributes over others, leading to trade-offs in achieving fairness across all sensitive attributes. For example, optimizing for demographic parity may inadvertently disadvantage certain groups in favor of others.

Data Quality and Representation: Ensuring fair treatment across multiple sensitive attributes relies on the quality and representativeness of the data. Biases in the data collection process or underrepresentation of certain groups can hinder efforts to balance fairness effectively.

Algorithmic Complexity: Balancing fairness across multiple attributes can increase the complexity of algorithm design and implementation. Trade-offs may arise between model performance and fairness considerations, requiring careful optimization to achieve a satisfactory balance.

User Perception and Acceptance: Mitigating biases across multiple sensitive attributes may impact user perceptions of the technology. Striking a balance between fairness and usability is crucial to ensure user acceptance and trust in the algorithmic decision-making process.

Regulatory and Ethical Considerations: Compliance with regulations and ethical guidelines related to fairness and non-discrimination poses additional challenges. Ensuring alignment with legal requirements while addressing biases across multiple attributes requires a comprehensive approach.

Navigating these trade-offs and challenges requires a holistic understanding of the intersectional nature of sensitive attributes, careful selection of fairness metrics, robust data collection practices, transparent algorithmic design, and ongoing evaluation and refinement to achieve fairness across multiple dimensions.

How can the continuous maintenance and update of data and algorithms for responsible deployment be effectively implemented in real-world settings?

Continuous maintenance and update of data and algorithms for responsible deployment in real-world settings can be effectively implemented through the following strategies:

Establish Clear Processes: Define clear processes and protocols for monitoring and updating data and algorithms on a regular basis. This includes setting up regular review cycles, identifying key performance indicators for monitoring, and establishing roles and responsibilities for maintenance tasks.

Automate Monitoring: Implement automated monitoring systems that track the performance of the technology in real-time. Automated alerts can flag potential issues or deviations from expected outcomes, prompting timely intervention and updates.

Feedback Mechanisms: Incorporate feedback mechanisms from users, stakeholders, and domain experts to gather insights on the technology's performance and potential areas for improvement. User feedback can provide valuable input for updating algorithms and data collection practices.

Adaptive Algorithms: Develop algorithms that can adapt to changing contexts and new data inputs. Machine learning models with adaptive capabilities can learn from new data and adjust their behavior to maintain effectiveness and fairness over time.

Regular Audits: Conduct regular audits of the data and algorithms to ensure compliance with ethical standards, regulatory requirements, and fairness principles. Independent audits can provide an objective assessment of the technology's performance and identify areas for enhancement.

Version Control and Documentation: Implement robust version control and documentation practices to track changes in data and algorithms over time. Maintaining a clear record of updates, modifications, and their impact on the technology's performance is essential for accountability and transparency.

Collaboration and Knowledge Sharing: Foster collaboration and knowledge sharing among multidisciplinary teams involved in the maintenance and update process. Encouraging cross-functional collaboration can facilitate the exchange of expertise and best practices for responsible deployment.

By implementing these strategies and fostering a culture of continuous improvement and responsible deployment, technology builders can ensure that their behavioral sensing technologies remain up-to-date, adaptive to changing contexts, and aligned with ethical and fairness considerations in real-world settings.