Skill.reDecision Logging: Recording Human Decisions, AI Input, and Reasoning
Overview
Lecture URL: https://skill.re/learn/recruiting/decision-logging-recording-human-decisions-ai-input-and-reasoning.php
TRANSCRIPT: Decision Logging: Recording Human Decisions, AI Input, and Reasoning
Course: AI for Recruiters - Professional Credential
Module: Level 4: Workflow Integration
Section: Chapter 21 -- Process Documentation and Defensibility
Theme: Process Documentation and Defensibility
Lecture: 21.1
Duration: 90 min
Format: Workshop + Case Studies
Audience: Senior recruiters, team leads, recruiting managers
Prerequisites: L3 Certification
What you will learn: Create audit trails for recruiting decisions. Learn how to log who decided what, when, based on what criteria, and what AI informed the decision. Build defensibility through comprehensive decision documentation.
When a candidate is rejected or an offer is extended, you need to be able to explain why. Not just to the candidate, but to yourself months later if a legal question arises. Not just for compliance, but for continuous improvement--understanding whether your decisions were consistent and fair.
Decision logging is how you create this trail. It's how you know why decisions were made. It's how you can defend them if questioned. It's how you can improve over time.
In this session, you'll learn what to log, how to log it, and how to use logged decisions for compliance and improvement.
[WHAT TO LOG]
For each significant hiring decision (advance to phone screen, offer, rejection), log:
Candidate identity: Name, ID, which role they applied for or were sourced for.
Decision: What was decided? (Advanced to phone screen, rejected, offer extended, offer declined)
Timing: When was the decision made?
Decision-maker: Who made the decision? (Recruiter, hiring manager, panel)
Criteria assessed: What was the candidate evaluated against?
Assessment: How did the candidate perform against the criteria? (Examples: "Strong relevant experience, weaker communication skills")
AI input (if applicable): Was AI involved? What did the AI assess? What score did it provide?
Human review of AI: If AI made a recommendation, did a human review it? What was their assessment?
Rationale: Why was this decision made? (Example: "Candidate meets technical requirements and has strong relevant experience. However, communication skills assessment showed gaps that are critical for this role.")
This is not: Subjective impressions, gossip, gut reactions. This is: objective assessment against defined criteria.
[DECISION LOGGING SYSTEMS]
Different systems work for different organizations:
ATS-based logging: Your ATS has fields for decision, criteria, and rationale. Every decision is logged as the workflow progresses.
Structured forms: A form (digital or paper) that captures decisions. Filled out at the time of decision.
Free-form notes with structure: Recruiting team writes notes with a consistent structure (decision, criteria, reasoning).
The key: consistency. Whatever system you use, everyone uses it the same way. A candidate's decision is logged the same way whether they're a referral or direct applicant, whether they're from a certain location, whether they're from a protected group.
[USING LOGGED DECISIONS FOR IMPROVEMENT]
Decision logs aren't just for compliance. They're for improvement:
Calibration: Review decisions together. Do different team members apply criteria consistently? If not, discuss and align.
Accuracy checking: For hired candidates, look back at decision notes. Were the criteria we assessed actually predictive of success? If not, refine criteria.
Fairness analysis: Disaggregate decisions by demographic group. Are women advanced at the same rate as men at each stage? Are people from certain locations treated differently?
Bias detection: Look for patterns in rejections. If all rejections from women cite "weak communication skills" but rejections from men cite "insufficient experience," there might be bias.
Improvement opportunities: If you see patterns of candidates being rejected for gaps that could be trained, maybe your criteria is too strict.
Anti-Pattern 1: Logging Theater
A company implements a decision logging system. Forms are filled out. But the forms capture minimal information. "Rejected" with no explanation. Over time, nobody puts real thought into logging. The logs exist but aren't useful for compliance or improvement.
Why it happens: Logging feels like overhead. So minimal information is captured.
What goes wrong: Logs exist but aren't defensible and don't improve decision-making.
How to avoid it: Design logging to capture what you actually need. Make it efficient but substantial.
Anti-Pattern 2: Logging Bias Instead of Correcting It
A team logs that candidates are rejected for "weak culture fit" disproportionately for women. But instead of investigating whether the assessment is biased, they just log it. They accept that women have weaker culture fit and continue making decisions based on that assessment.
Why it happens: Logging becomes documentation of bias instead of trigger for improvement.
What goes wrong: You document bias without addressing it. This actually increases legal exposure.
How to avoid it: Use logs to identify bias patterns. Then investigate and correct. Logging the bias is just the first step.
Anti-Pattern 3: Inconsistent Logging
Some recruiters log detailed notes explaining decisions. Others log minimal information. Some log assessment against criteria; others log subjective impressions. Inconsistency makes it hard to learn from decisions or defend them.
Why it happens: Logging isn't consistently required or enforced.
What goes wrong: Logs are inconsistent and less useful for compliance or improvement.
How to avoid it: Standardize logging. Train everyone. Make it someone's job to ensure consistency.
[PRACTICE PROMPTS]
- Design a decision logging template for your organization. What information must be captured for each decision?
- Implement logging for one week of decisions. Review what you logged. Is it sufficient for compliance and improvement?
- Pull a sample of logged decisions from your organization. Analyze them for consistency. Do different team members log differently?
- Disaggregate your logged decisions by demographic group. Are decisions made consistently across groups?
- Use logged decisions to identify one improvement opportunity. What would you change based on patterns you see?
- Decision logging is infrastructure for compliance and improvement.
- Log objective assessment against criteria, not subjective impressions.
- Make logging consistent. Everyone uses the same format.
- Use logs for calibration, accuracy checking, fairness analysis, and improvement.
- When logs reveal bias, investigate and correct. Logging is the first step, not the end.
[GLOSSARY]
Decision Log: A documented record of hiring decisions, including who decided, what the decision was, what criteria was assessed, and why.
Calibration: The process of aligning standards across multiple decision-makers. Using logged decisions to check for alignment.
[SYNTHESIS AND APPLICATION]
Comprehensive decision logging enables both compliance and continuous improvement. It's infrastructure, not overhead.
[REFLECTION EXERCISE]
- If you were questioned about a hiring decision from six months ago, could you explain why it was made?
- What information do you most regret not having logged about past decisions?
- How would you motivate your team to log decisions consistently?
- What decision pattern have you noticed that surprised you?
- How would you use logged decisions to improve hiring next quarter?
[CLOSING REMARKS]
Good decision logging is how you learn and improve continuously.
AI for Recruiters Certification Program
Level 4: Workflow Integration | Process Documentation and Defensibility | Lecture 1
A SkillsClinic initiative.
Duration: ~90 minutes | Word Count: ~2,100
[DECISION LOGGING IMPLEMENTATION]
Decision logs are most useful when they capture the actual decision-making, not sanitized versions.
What to log:
- What AI recommended (if AI was involved)
- What human decided
- Why the human agreed or disagreed with AI
- Key information that drove the decision
- Any uncertainty or concerns
Format: Simple narrative. "AI scored 8/10 on technical fit. I agreed and advanced candidate. Communication concern noted for next interview."
Frequency: Log decisions at interview stage and beyond. Don't log every screen.
Use: Analyze logs quarterly to understand how humans are using or overriding AI. Are there patterns? Are overrides justified?
This data is valuable for improving both AI and human decision-making.