Technology, Healthcare, Finance, Public Sector, Manufacturing, Retail
AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development
AI systems often start with high hopes but frequently fail, causing harm and missed opportunities. This issue, termed 'AI Mismatches,' arises when actual system performance doesn't meet the needs for safety and value creation. Current approaches often fix problems post-development. We propose an early-stage 'AI Mismatch' approach, using seven matrices derived from 774 AI cases, to anticipate and mitigate risks before development.
Key Performance Indicators
Our analysis highlights the following key metrics and their impact on enterprise AI initiatives.
85%
AI Initiative Failure Rate
90%
CEOs Prioritizing AI
Significant
AI Concepts Doomed Before Code
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Introduction
Related Work
Methodology
AI Mismatch Matrices
Case Studies
Discussion & Future Work
Discusses the growing interest in AI, its high failure rate, and the emergence of 'AI Mismatches' as a key challenge in AI development.
Reviews existing HCI and Responsible AI research, including 'AI as a Design Material' and FATE research, highlighting the shift towards early-stage intervention.
Details the iterative Research through Design (RtD) approach used, including collection of 774 AI cases, analysis, synthesis of risk factors, and development of seven matrices.
Introduces and explains the seven matrices designed to identify potential AI Mismatches, categorized into high-level and supporting matrices.
Demonstrates the application of the AI Mismatch matrices through comparative case studies in various domains, highlighting how they reveal and clarify risks.
Reflects on the implications of the approach for Responsible AI, including focusing on moderate AI performance, worker-centered design, and cautious transfer of AI concepts across domains, along with future research directions.
After Deployment
Harm Mitigation Focus
Many current AI safety efforts focus on post-deployment fixes, often missing fundamental issues that arise earlier in development, leading to compounded harms.
Research Methodology Flow
Collect & Curate AI Cases (774 cases)
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Analyze AI Cases & Extract Factors
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Synthesize Key Risk Factors
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Develop Seven Matrices
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Stress-Test with Real-World Counterexamples
| Metric Type | Focus | Goal |
|---|---|---|
| Traditional ML Metrics | Predictive accuracy, Statistical performance | Optimize technical benchmarks |
| Human-Centric Performance | Model's ability to fulfill human need, Real-world impact & value | Minimize harm & ensure safety |
Case Study 1: Allegheny Family Screening Tool (AFST) vs. Hello Baby Predictive Risk Model
AFST: Infers likelihood of child being placed into foster care. High-risk decisions, requires excellent performance. Uses sparse, biased public administrative data. Failed due to mismatch, causing harm and criticism.
Hello Baby: Infers likelihood of placement in foster care by age 3, proactively offers support. Lower-risk decisions, moderate performance sufficient. Uses similar data but aligns with human needs, leading to value creation without severe harm. Highlights value of moderate performance in worker-centered design.
Case Study 2: GizmodoBot vs. DuolingoLLM for Content Generation
GizmodoBot: Automatically generates and publishes entertainment articles. Demanded publish-ready content (excellent performance). Struggled with factual errors, context, temporality (LLM limitations), causing reputational harm and journalistic credibility issues due to lack of human oversight.
DuolingoLLM: Assists education experts in generating lesson plans. Accepts moderate performance as experts refine outputs. Integrates human expertise in workflow to filter errors and add pedagogical knowledge. Creates value despite imperfect outputs, demonstrating responsible AI augmentation.
Case Study 3: Thomson Reuters' CLEAR vs. Fraud Detect for Public Services
CLEAR (Financial Fraud): Blocks/quarantines financial transactions based on fraudulent behavior. Uses high-quality financial transaction data. Moderate performance creates value, as human review mitigates errors.
Fraud Detect (Public Benefits): Approves/denies public benefits applications based on fraud likelihood. Uses less reliable public administrative, social media, credit data. High-risk domain requires excellent performance, but low-quality data and unobservables led to denying legitimate claims, causing significant harm to vulnerable populations.
Early-Stage Intervention
Key to Responsible AI
Addressing AI Mismatches early in the ideation and problem formulation stages is crucial to prevent downstream harms and ensure value creation.
AI Mismatch Matrices: Core Questions
Q1: Is AI concept feasible?
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Q2: Can we ensure equitable performance where it matters?
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Q3: Can the AI concept avoid harm?
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