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AI-Assisted Hiring Research

Enhancing effective and equitable AI-assisted hiring through human-centered design and comprehensive user research

Duration
10 weeks
My Role
UX Researcher
Team
4 members
Client
Indeed

Executive Summary

Hiring has always been the heartbeat of workforce development. But today it faces unique challenges—traditional hiring struggles to keep pace with shifting demands. AI offers a transformative opportunity to expand talent pools for those historically overlooked.

Indeed, despite its global reach and volume, found that employers do not trust AI-generated candidate recommendations. Our research—spanning literature reviews, Reddit analysis, user interviews, and competitive benchmarking—reveals key flaws in AI-assisted hiring: employers don't trust AI fully because they don't understand why candidates are ranked or excluded.

The real question isn't whether bias exists—it's how we manage it to create fairer, smarter hiring processes.

Project Context & Goals

Indeed is a global leader in the job marketplace, connecting over 350 million unique visitors monthly and supporting 525 million global job seeker profiles. Despite this reach, the platform faces critical challenges where employers struggle to find high-quality, relevant candidates, and AI-powered candidate summaries are often seen as too generic, undermining employer trust.

🎯
Reduce Key Bottlenecks
Address the lack of visibility into best-fit talent despite millions of profiles and job listings
🔍
Enhance AI Transparency
Improve decision support by providing comprehensible explanations for AI recommendations
⚙️
Optimize User Workflow
Align AI processes to target user workflows and daily hiring practices
⚖️
Mitigate Implicit Bias
Acknowledge and address various forms of bias through ongoing efforts and design

Research Methodology

Our research approach combined multiple methodologies to gain comprehensive insights into AI-assisted hiring challenges and opportunities:

📚
Literature Review
Analyzed existing research on AI hiring, ethical concerns, efficiency gains, and user adoption challenges
💬
Reddit Analysis
Scraped Reddit threads to identify real-world perceptions, pain points, and expectations regarding AI in recruitment
🎤
User Interviews
Conducted interviews with 8 recruiters and hiring managers to understand experiences and attitudes toward AI tools
🔬
Competitive Analysis
Gathered information about 4 corporations and startups to identify opportunities and market gaps

Key Research Insights

1
Ideal Candidate Mental Framework
Employers tend to have an "Ideal Candidate" mental framework that guides their job postings and candidate selection processes
2
Collaborative Hiring Process
Collaboration among hiring managers, recruiters, and leaders is preferred and crucial throughout the hiring process
3
LLM Assistance in Job Descriptions
Large Language Models could effectively help employers develop and review job descriptions, especially for compliance
4
Holistic Matching Approach
Matching isn't about 100% perfect matching—consider transferable skills and adopt a whole person viewpoint
5
Trust Through Transparency
AI Transparency and Human Control are key points to build up trust of AI systems among users
6
Fairness as Priority
AI Fairness is the key focus in academic research to ensure correct application in industry settings

Design Solutions

Based on our research insights, we developed three key design solutions to address the core challenges in AI-assisted hiring:

🎯
Role Calibration
A structured flow to validate employer intentions by translating human hiring intent into structured signals for AI-assisted candidate screening
🔍
Smart Screen
Advanced screening tools that provide evidence-based data to increase confidence before moving candidates further into the pipeline
📄
Resume Citation
Enhanced trust through direct resume citations supporting AI results, providing transparent explanations for AI recommendations

Project Impact

Our research and design solutions addressed critical gaps in AI-assisted hiring, providing actionable insights for Indeed's product strategy:

Key Outcomes

Enhanced AI Transparency and Trust

Our resume citation system directly addresses the core issue of employer distrust in AI recommendations. By providing clear, traceable connections between AI decisions and candidate qualifications, we enable employers to understand and validate AI suggestions rather than dismissing them as "black box" outputs.

Streamlined Intent Capture Process

The role calibration workflow we developed transforms vague hiring preferences into structured, actionable criteria that AI systems can interpret consistently. This reduces the gap between what employers want and what AI delivers, leading to more relevant candidate matches.

Holistic Candidate Evaluation

Our research revealed that perfect keyword matching often misses qualified candidates with transferable skills. The smart screening tools we designed encourage employers to consider candidates' full potential, potentially expanding diversity in hiring while maintaining quality standards.

Strategic Value for Indeed

Product Roadmap Clarity

Our findings provide Indeed with a clear pathway for improving their Smart Sourcing platform. The three integrated solutions address the complete hiring workflow while building incrementally on Indeed's existing AI capabilities.

Competitive Differentiation

This positions Indeed to differentiate from competitors through trustworthy, transparent AI tools that prioritize human agency while leveraging automation efficiency.

Responsible AI Framework

We established clear principles for human-AI collaboration that acknowledge bias as an inherent challenge while providing concrete mechanisms for mitigation, ensuring accountability remains with human decision-makers.

Industry Impact

Beyond Indeed's immediate needs, our research contributes to the broader conversation about ethical AI implementation in high-stakes decisions like hiring. The methodologies and principles we developed can inform industry standards and regulatory approaches to AI fairness in employment contexts.

Our human-centered approach to AI transparency and bias mitigation provides a replicable framework that other organizations can adapt for responsible AI deployment in hiring and beyond.

Research Outcomes

8
Recruiter Interviews
6
Key Insights
3
Design Solutions
10
Week Timeline

Ethical Considerations & Social Impact

Our research identified several critical ethical considerations that must be addressed in AI-assisted hiring systems:

Stakeholder Alignment
Job seekers and employers sometimes have different expectations about recruiting tools, requiring balanced design approaches
Data Privacy & Consent
Resume citations raise questions about consent and data usage, requiring clear policies on data sourcing and retention
Accessibility Concerns
While AI may help some candidates, those unfamiliar with AI language parsing may be disadvantaged
Human Judgment Balance
Need to balance AI efficiency with human judgment to avoid over-reliance on automated screening

Reflection & Future Work

This project provided valuable insights into the complexities of AI-assisted hiring. Key learnings and future directions include:

👥
Broader User Research
Future work should include more job seeker perspectives to create balanced solutions that serve all stakeholders
📊
Quantitative Validation
Incorporate survey methods and quantitative research to validate design concepts with larger user groups
⚖️
Fairness Definition
Learned that fairness in AI hiring is contextual and social, requiring ongoing dialogue with all stakeholders
🔧
Partial Automation
Discovered that recruiters prefer AI assistance rather than full automation, valuing their domain expertise

*Disclaimer: The Indeed logo is a trademark of Indeed, Inc. This case study was conducted for academic purposes as part of a university research project. All research findings and design recommendations are independent work and do not represent official Indeed positions or products.