AI's Affective Impact
Reconfiguring Empathy: Affective Outcomes of AI-Mediated Decision-Making
Author: Eva-Madeleine Schmidt
Publication: Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA '26)
When an AI filter neutralizes a distressed face or a risk model marks a welfare applicant as ineligible, it does not simply modify the data but reshapes the emotional ground on which decisions are made. This research examines how AI systems influence the affective mechanisms of humans that guide decision-making. As AI becomes embedded in institutional and everyday contexts, it increasingly alters empathy, responsibility, and affective engagement. Using controlled studies in administrative decision-making and donation appeals, the dissertation shows that even brief interactions with AI advice or AI-altered faces can shift both discretionary choices and the affective conditions in which these are made, sometimes in subtle and counterintuitive ways. By positioning affect as a critical layer of human-AI interaction, this work offers guidance for designing AI systems that balance affective engagement and support humane, accountable decision-making, contributing insights that HCI is uniquely positioned to integrate across adjacent disciplines.
- AI
- Empathy
- Decision-Making
- Affective Computing
- HCI
- Ethics
Executive Summary: AI's Reshaping of Human Affect
This research reveals how AI-mediated decisions significantly reshape human emotional responses and ethical judgments, impacting prosocial behavior and accountability. AI systems are increasingly shaping critical decisions in public and private sectors, from social media to criminal justice. Understanding AI's impact on human affect is crucial for designing ethical, accountable, and humane AI systems, particularly in contexts involving prosocial and moral judgments. The findings offer actionable insights for policymakers, AI designers, and institutions to mitigate risks and enhance positive human-AI collaboration.
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Shift in Discretionary Choices
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Impact on Empathy Levels
Human-AI
Interaction Paradigm Redefined
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Administrative Decision-Making
Donation Decisions
Overall Implications
AI-Mediated Decision-Making Process
This flowchart illustrates the general process of AI-mediated decision-making studied, highlighting how AI advice or altered information intervenes in human judgment.
Scenario Presented
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AI Feedback (if applicable)
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Human Decision
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Affective Responses
Key Takeaway: AI intervenes at multiple stages, influencing not just the outcome but also the emotional context of decisions.
Impact on Approval Rates
Preliminary data from the administrative decision-making study showed that AI recommendations led to a lower overall approval rate compared to the control group.
Lower Overall Approval Rates with AI AdviceKey Takeaway: AI advice systematically shifts discretionary choices, often leading to more conservative outcomes.
| Affective Condition | Effect of AI Advice |
|---|---|
| Certainty |
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| Psychological Distance |
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| Perceived Responsibility |
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Key Takeaway: AI-mediation alters the emotional and cognitive context of decisions, potentially disengaging human empathy.
AI-Mediated Donation Appeal Process
This flowchart outlines the experimental setup for donation appeals, where participants interacted with AI-altered facial expressions.
Original Video
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AI Filtered Video
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Choice (Original or Filtered)
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Donation Allocation
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Empathy Rating
Key Takeaway: AI can manipulate nonverbal cues, influencing prosocial behavior in unexpected ways.
Counterintuitive Empathy Boost
Contrary to expectations, neutralizing AI filters modestly increased both empathy and donations, rather than reducing them. Sad and angry filters showed negligible effects.
Increased Empathy & Donations with Neutralizing FiltersKey Takeaway: AI-altered expressions can be perceived as more credible or less emotionally demanding, leading to surprising prosocial outcomes.
Reconfiguring Empathy: Sociotechnical Shifts
Context: AI's role extends beyond mere data processing; it actively reshapes how humans perceive, evaluate, and respond to emotional cues. This is a sociotechnical shift, impacting institutional processes and interpersonal encounters. The study advocates for design approaches that preserve affective engagement.
Challenge: Maintaining ethical oversight and human accountability in AI-driven decision systems, particularly where emotional intelligence and empathy are critical for fair and just outcomes.
Solution: Designing AI systems that foreground affect as a critical interaction layer, ensuring transparency in AI mediation, and integrating mechanisms that support reflective judgment and preserve human responsibility, rather than undermining it.
Outcome: Improved human-AI collaboration leading to more humane and accountable decisions, preventing 'empathy erosion' in critical contexts like welfare applications and patient care.
Key Takeaway: AI design must proactively preserve affective engagement and human responsibility to ensure ethical outcomes.
Quantify Your AI Impact
Estimate the potential savings and reclaimed human hours by implementing AI solutions that enhance empathetic decision-making and operational efficiency.
Estimated Annual Savings
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Your AI Implementation Roadmap
A strategic approach to integrate affect-aware AI solutions, ensuring ethical deployment and maximized human-AI collaboration.
Phase 1: Discovery & Strategy
(2-4 Weeks)
- In-depth analysis of existing decision workflows and AI integration points.
- Stakeholder interviews to identify critical empathy-dependent processes.
- Customized AI ethical framework development focusing on affective outcomes.
Phase 2: Pilot Design & Prototyping
(4-8 Weeks)
- Development of AI prototypes for key decision scenarios with affect-preserving features.
- User experience testing with controlled groups to measure shifts in empathy and accountability.
- Iterative refinement based on qualitative and quantitative feedback.
Phase 3: Controlled Rollout & Monitoring
(6-12 Weeks)
- Deployment of AI systems in a controlled environment.
- Continuous monitoring of decision outcomes, user affect, and responsibility metrics.
- Training programs for human operators on human-AI collaboration best practices.
Phase 4: Full Integration & Optimization
(Ongoing)
- Scalable integration across relevant enterprise functions.
- Ongoing performance tuning and ethical audits.
- Establishing a feedback loop for continuous improvement and adaptation to new challenges.
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