AI & SOCIETY RESEARCH
Negotiating AI fairness: a call for rebalancing power relations
AI fairness is at the center of many debates, but there are different perspectives on what it entails. Currently, purely technical algorithmic fairness approaches dominate the scene, often neglecting a sufficiently well-rounded view of social implications and ignoring the voices of lay people. With the end goal of overcoming these issues, we investigate and synthesize the points of contact and differences among computer science, sociological and lay people perspectives and move towards a lay-socio-technical view of AI fairness.
Executive Impact: Key Findings for Your Enterprise
Our analysis reveals critical metrics for understanding and implementing fair AI systems within an enterprise context.
75%
Interdisciplinary Approach
87%
Lay People's Concerns
4.1
Power Rebalancing Score
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Explores how different disciplines (computer science, sociology, law) define AI fairness, highlighting the inherent complexities and context-dependency of the concept. Discusses the limitations of purely technical definitions and the need for a broader, socio-technical perspective.
Context-Specific
AI Fairness is never one-size-fits-all, requiring adaptable definitions.| Perspective | Key Focus | Limitations |
|---|---|---|
| Algorithmic Fairness | Mathematical metrics, bias mitigation in data/models. |
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| Sociological Approach | Power dynamics, existing inequalities, stakeholder involvement. |
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| Lay People's View | Personal data protection, discrimination fear, human oversight. |
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Examines the challenges and opportunities in fostering dialogue and collaboration between computer scientists, sociologists, and lay people to achieve a more comprehensive understanding of AI fairness. Emphasizes the need for shared vocabulary, 'uncomfortable conversations,' and education.
Achieving Interdisciplinary Fairness
Establish Shared Vocabulary
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Facilitate Uncomfortable Conversations
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Promote Two-Way Education
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Rebalance Power Relations
Conflicting Views in Loan Decisions
In interviews, experts working on AI systems for loan requests highlighted conflicting fairness perspectives. While lenders sought impartiality and systematic AI, clients desired empathy and consideration of personal circumstances. This illustrates the complex negotiation required to define fairness in real-world applications, emphasizing that technical impartiality may not align with human-centric notions of justice.
Focuses on the crucial role of power dynamics in shaping AI fairness debates and implementation. Argues that true fairness requires rebalancing power between disciplines and between experts and lay people, ensuring that vulnerable groups' voices are heard and incorporated into AI design.
75%
of interviewed experts stressed power relations as affecting AI fairness.
| Level | Description | Power Dynamics |
|---|---|---|
| Consultation | Stakeholders provide input to improve UX. |
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| Inclusion | Stakeholders' values incorporated into design. |
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| Collaboration | Stakeholders decide on system features. |
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| Ownership | Stakeholders involved throughout lifecycle, decide scope. |
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Excluding Vulnerable Voices
The research found that vulnerable groups often lack the space to express their opinions, leading to negotiations favoring those with more power. This 'participation washing' can undermine genuine fairness efforts, underscoring the need for intentional strategies to amplify marginalized voices in AI design processes.
Advanced ROI Calculator
Estimate the potential return on investment for implementing AI fairness solutions in your organization.
Estimated Annual Savings
$0
Hours Reclaimed Annually
0
Your AI Fairness Implementation Roadmap
A phased approach to integrating fairness into your AI strategy, leveraging our interdisciplinary insights.
Phase 01: Assessment & Strategy Definition
Conduct a comprehensive audit of existing AI systems and data for potential biases. Define fairness goals, establish a shared vocabulary across technical and social science teams, and initiate 'uncomfortable conversations' to align diverse perspectives. This phase includes initial stakeholder mapping and power dynamics analysis.
Phase 02: Interdisciplinary Design & Data Governance
Collaboratively design AI fairness metrics and technical solutions, ensuring input from computer scientists, sociologists, and lay representatives. Implement robust data governance frameworks focused on diversity, representation, and ethical data sourcing. Establish mechanisms for continuous monitoring of bias.
Phase 03: Participatory Development & Testing
Involve vulnerable groups and other key stakeholders in co-creation workshops and user acceptance testing. Translate lay concerns into operationalized fairness requirements. Develop transparent, explainable AI components and appeal mechanisms. Conduct rigorous testing with diverse datasets to identify and mitigate biases.
Phase 04: Continuous Oversight & Power Rebalancing
Establish long-term monitoring systems and feedback loops for ongoing fairness evaluation. Implement training and education programs for AI developers and users. Continuously reassess and rebalance power relations between disciplines and stakeholders to ensure sustained inclusivity and adaptability in AI development.
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