talent-acquisition

All posts tagged talent-acquisition

The Psychic Recruiter Fallacy

Posted by Steve on June 17, 2025
Posted in: Recruiting. Tagged: , , . Leave a comment

Our feeds are filled with posts, articles, videos, and memes about this new era of Talent Acquisition that has been ushered in with generative and baby agentic AI. 

How the machines will eventually take over.

How company executives are gleefully pushing their talent leaders to accelerate the human-to-AI changeover.

Nonetheless, we’re still left with humans being involved in a large number of hiring decisions and as such, their influence – good, bad, and ugly – is still felt on most hiring stages.

We all know the common mistakes that recruiter make that introduce bias into the hiring process, often unintentionally, but still very real. These mistakes don’t flitter away with time but stay with those we covet and far too often, find their way into social media, review sites, and shared with friends and family.

Worse, these affect diversity, fairness, and ultimately the quality of the hire.

Why do these perpetuate themselves? Dunno; while I have theories, I’m not a psychotherapist. All I know is that for most people, self-awareness works to change behavior.

The Biases We Know

You know these – so I won’t delve into them here:

  • Over-Reliance on “Culture Fit”
  • Resume Name, Address, or Photo Screening
  • Affinity Bias During Interviews
  • Overemphasis on “Pedigree”
  • Halo and Horns Effect
  • Gendered or Coded Language in Job Descriptions
  • Unstructured Interviews
  • Assuming Career Gaps = Lack of Commitment
  • Ignoring Accent or Language Bias
  • Confirmation Bias in Reference Checks

But there’s one that really takes the cake and isn’t discussed enough.

The “Psychic Recruiter Fallacy”.

This is a highly pervasive and damaging form of bias dressed up as “intuition” or “savvy.”

Want another name for this? How about Assumptive, Projection, or Paternalism Bias?

Not good enough?

Okie dokie – then let’s go with Acting Like a Psychic (“I Just Know This Candidate Won’t…”) 

Typical “Acting Like a Psychic” Behaviors include:

  • “They won’t want to do tactical work – they’ve been a manager too long.”
  • “They wouldn’t be happy with the commute.”
  • “This job’s too junior for someone with that title.”
  • “They’ll probably want more money than we can offer.”
  • “They’ll be bored in six months.”

Why is this a problem?

It removes candidate agency; you’re making decisions for someone based on assumptions rather than data.

It shuts down diversity of motivation; you’re presuming everyone wants the same career path or lifestyle.

It often reflects your own preferences or discomforts projected onto others.

It limits opportunity; some of the best hires come from unexpected fits and choices.

What should you be doing?

Ask, don’t assume; if you’re curious or unsure about commute tolerance, tactical interest, or salary flexibility, just ask.

Present options neutrally; “This role involves a mix of strategic and hands-on work – how and why do you feel about that?”

Let the person opt out, not you; your job is to facilitate a well-informed decision, not make it on their behalf.

The Final Recruiter Reality Check

If you catch yourself saying, “I just know this person won’t want to…”, that’s your cue to pause, question your assumption, and gather evidence.

Sorry but you’re not a psychic…

Talent Acquisition Metrics That Actually Make Sense

Posted by Steve on June 17, 2025
Posted in: Recruiting. Tagged: , , , , , , . Leave a comment

What if the metrics we held near and dear to our Talent Acquisition hearts weren’t the best ways to measure the health and wellness of our hiring initiatives?

What if, as a Talent Advisor, you’ve been tasked with identifying new metrics to offer better insights into your workflows and performances?

Not metrics rolled up into a big mass that is devoid of true actionable insight but metrics that candidate experience, recruiter effectiveness, hiring manager partnership, and executive expectations.

Legend:
🟠 Candidate-Centric Metrics
🟡 Recruiter-Centric Metrics
🔴 Hiring Manager-Centric Metrics
🔵 C-Level / Strategic Metrics

The Metrics…

Time in Status Variance (TISV) 🟠 🟡 🔴
Measures how long people stay in each stage of the hiring process (e.g., sourcing, engagement/application review, interview scheduling), and highlights outliers.
+ People: Reduces ghosting and bottlenecks.
+ Recruiter: Helps identify where they’re slowed down.
+ Hiring Manager: Pinpoints where feedback or decisions lag.
+ C-Level: Reveals process inefficiency and its downstream impact on time to hire.

Net Hiring Experience Score (NHX) 🟠 🟡 🔴 🔵
NPS-style score that blends person, recruiter, and hiring manager feedback after each hiring cycle.
+ All: Holistically reflects satisfaction across all user groups.
+ All: Helps build a culture of accountability and partnership in hiring.

Feedback Loop Closure Rate 🟠 🟡 🔴
Measures the percentage of people (especially post-interview) who receive meaningful feedback, and how fast it’s delivered.
+ People: Directly improves experience and employer brand.
+ Recruiter: Encourages ownership of people journey.
+ Hiring Manager: Reflects commitment to communication.
+ C-Level: Brand equity and values

Interview-to-Decision Velocity 🔴
Tracks time from final interview to offer decision.
+ People: Reduces anxiety, keeps engagement high.
+ Recruiter: Identifies slowdowns in decision-making.
+ Hiring Manager: Encourages prompt evaluation.
+ C-Level: Prevents offer declines due to delays.

Internal Mobility Friction Index 🔴
Measures how often internal employees apply, progress, and are selected versus dropping out early.
+ People: Encourages growth within.
+ Recruiter: Promotes fair, internal-first hiring.
+ Hiring Manager: Highlights openness to internal talent.
+ C-Level: Signals talent retention and cost avoidance.

Recruiter Role Complexity Load (RRCL) 🟡
Weights recruiter requisitions not by volume but by complexity (seniority, scarcity, location, niche skill).
+ People: Reduces burnout, improves communication.
+ Recruiter: Encourages equitable req distribution.
+ Hiring Manager: Helps manage expectations on timelines.
+ C-Level: Aligns headcount strategy with capacity.

Hiring Forecast Accuracy (HFA) 🔵
Compares predicted versus actual hiring outcomes (timing, volume, quality).
+ People: Reduces last-minute changes and cancellations.
+ Recruiter: Promotes proactive planning.
+ Hiring Manager: Builds trust in TA predictability.
+ C-Level: Links hiring plans to business planning reliability.

Requisition Approval to Launch Lag 🟡
Measures time between when a req is approved and when it’s posted.
+ People: Shortens access to opportunities.
+ Recruiter: Highlights systemic delays or bottlenecks.
+ Hiring Manager: Drives urgency and alignment.
+ C-Level: Impacts “revenue or science”-critical headcount plans.

Candidate Sentiment Delta (CSD) 🟠
Compares people sentiment before and after interviews (via short pulse surveys).
+ People: Ensures the process improves perception.
+ Recruiter: Reveals breakdowns in communication or tone.
+ Hiring Manager: Correlates interview quality with engagement.
+ C-Level: Leading indicator of reputation and employer brand.

Offer Drop-Off Signal Index 🟠
Uses signals (delays, counteroffers, communication gaps) to predict risk of offer rejection or ghosting.
+ People: Enables preemptive re-engagement.
+ Recruiter: Improves offer-to-accept ratio.
+ Hiring Manager: Protects time investment.
+ C-Level: Reduces recruiting efficiency ratio and churn.

Hiring Manager Touchpoint Index 🔴
Measures how often and how meaningfully hiring managers engage in the hiring process (intake/discovery, engagement strategy, screening, feedback, interview participation).
+ People: Improves perception of manager involvement.
+ Recruiter: Reduces process drag.
+ Hiring Manager: Encourages ownership of talent outcomes.
+ C-Level: Culture of accountability and leadership quality.

AI Augmentation Ratio 🟡 🔵
Proportion of sourcing, screening, and scheduling tasks automated or AI-assisted versus human effort.
+ People: Faster responses, less delay.
+ Recruiter: Improves strategic focus.
+ Hiring Manager: Shorter time to slate.
+ C-Level: Talent cost efficiency and innovation maturity.

Talent Scarcity Exposure Score 🔵
An index that combines role criticality, geographic constraints, compensation competitiveness, and talent market saturation.
+ People: Better-informed expectations.
+ Recruiter: Supports smarter sourcing prioritization.
+ Hiring Manager: Sets realistic expectations.
+ C-Level: Talent planning for business risk mitigation.

First-Year Success Alignment 🔵
Tracks if new hires meet performance goals aligned to hiring criteria within their first year (no, this is not QoH; it’s objective rather than subjective).
+ People: Encourages onboarding and integration.
+ Recruiter: Links selection quality to outcome.
+ Hiring Manager: Measures hiring fit beyond “time to fill.”
+ C-Level: Business impact of hiring decisions.

Experience Consistency Index 🟠 🔵
Measures variation in experience scores across different locations, functions, and demographic groups.
+ People: Ensures equitable process design.
+ Recruiter: Standardizes best practices.
+ Hiring Manager: Flags inconsistent behavior or support.
+ C-Level: Equity, inclusion, and brand consistency.

Are any of these good? Bad? Meh?

I’m really interested in what you think.

~Steve

The Talent Acquisition Ethicist

Posted by Steve on June 17, 2025
Posted in: Recruiting. Tagged: , , , , . Leave a comment

This Talent Acquisition post was inspired by Adam Gordon‘s post in Hung Lee‘s Recruiting Brainfood group on FB about The Adecco Group‘s announcement of a new company to integrate human and digital talent.

Yes, it was very odd typing “digital talent” – yet here we are in the light of the AI Era of Talent.

As we were bantering back and forth on the topic, it dawned on me: In the world of talent, we don’t have any formal role containing the word, “Ethicist.”

  • Medical & Bioethics has Medical Ethicist, Clinical Ethicist, Bioethicist, Hospital Ethicist
  • Technology & AI has AI Ethicist, Data Ethics Officer, Responsible AI Lead, Algorithmic Fairness Researcher
  • Government & Policy has Ethics Officer, Government Ethicist, Policy Ethicist
  • Scientific Research & Academia has Research Ethicist, Academic Ethicist, Institutional Review Board Member
  • Corporate has Corporate Ethicist, Chief Ethics Officer, Ethics and Compliance Officer
  • Environmental & Global Justice has Environmental Ethicist, Humanitarian Ethicist, Climate Justice Consultant
  • Journalism, Media, & Communications has Media Ethicist, Journalism Ethics Advisor

With the assistance of ChatGPT and reading a bit more about Medical Ethics, AI Ethics, and Talent Enablement, here is a proposed Job Description for a Talent Acquisition Ethicist, along with the roles scorecard and assessment rubric. I’m sure you can easily see how this role can be expanded into a much larger “Talent Ethicist” focus.

Yes, it needs work but I’m very interested in what you think about it.

Comments please!

Talent Acquisition Ethicist

Reports to: Head of Talent Acquisition

✳️ Role Summary

As our company scales the use of AI and data-driven decision-making across the hiring lifecycle, we are establishing a new and essential role – Talent Acquisition Ethicist. This role is responsible for ensuring that while our TA function evolves technologically, it never loses its human core. Drawing from principles in AI and medical ethics, the TA Ethicist will act as both a strategic enabler and moral compass, shaping how our tools, systems, and people work together to hire inclusively, transparently, and responsibly. 

You will lead initiatives that embed ethical thinking into our talent systems, design operational frameworks for equitable AI use, and ensure that every candidate experience – human or machine-mediated – honors fairness, dignity, and psychological safety. This role blends deep thinking, often new thinking, with real-world application and is ideal for someone passionate about shaping the future of hiring in a way that is both high-tech and high-touch.

✳️ The Core

⛛ Ethical Risk Governance & Framework Design

  • Lead ethical audits of all AI-driven and automated talent acquisition platforms, including sourcing algorithms, résumé parsers, and candidate scoring tools.
  • Develop TA-specific ethical frameworks and risk mitigation protocols in alignment with corporate values, international labor standards, and local regulations (e.g., EU AI Act, NYC AEDT Law, GDPR).
  • Evaluate third-party vendors and internal tools for ethical integrity, focusing on bias detection, transparency, candidate agency, and data protection.
  • Partner with Legal, DEI, and People Analytics to build an ethical governance roadmap for TA operations.

⛛ Human-Centered Technology Deployment

  • Act as a decision-making partner in the adoption, customization, and rollout of new AI-powered TA tools – ensuring enhancements empower, rather than displace, recruiter judgment and human connection.
  • Define “human-in-the-loop” criteria for systems that automate screening, ranking, or rejection workflows.
  • Lead the creation of ethical use policies for tools such as automated interviews, generative AI assistants, and chatbots.

⛛ Candidate Experience & Psychological Safety

  • Design safeguards to ensure informed consent, transparency, and fairness when candidates interact with AI or automated decision-making systems.
  • Monitor and enhance candidate sentiment metrics related to fairness, empathy, and perceived agency.
  • Collaborate with Employer Branding to ensure public-facing narratives about hiring tech align with ethical practices. 

⛛ Enablement, Training & Cultural Integration

  • Develop ethical literacy training programs for recruiters, sourcers, branders, coordinators, and TA leadership – covering bias mitigation, responsible prompt design, inclusive automation, and ethical AI use.
  • Contribute ethical guidance to recruiter onboarding and continuous learning programs.
  • Serve as an internal advisor to the TA Enablement team in designing scalable, values-aligned hiring processes.

⛛ Metrics, Policy & Compliance

  • Define and track key ethical performance indicators (e.g., fairness audit scores, candidate opt-out rates, bias remediation cycles).
  • Maintain documentation and policy standards for the responsible use of AI in hiring.
  • Respond to and prepare for emerging AI regulations and industry-wide ethical hiring benchmarks.

⛛ Thought Leadership & Stakeholder Influence

  • Represent Talent Acquisition in cross-functional ethics councils, responsible AI working groups, and executive-level conversations on digital transformation.
  • Develop internal guidance papers and best practice documentation for ethical recruiting.
  • Stay abreast of trends and research across AI ethics, labor market equity, digital privacy, and the future of work.

✳️ Core Success Metrics

  • % of tools or vendors that pass ethical review and audit process.
  • Completion and sentiment scores from recruiter ethics training.
  • Candidate NPS and fairness sentiment post-AI interaction.
  • Resolution rate and speed for flagged ethical concerns or system escalations.
  • TA process changes implemented based on ethical findings.
  • Compliance readiness for external audits or new legislation. 

✳️ Core Qualifications

  • 12+ years of experience in Talent Acquisition, TA Operations, People Analytics, HR Tech, or related fields.
  • 5+ years of experience in a high-level leadership capacity in any Talent function.
  • Demonstrated understanding of AI/ML applications in hiring workflows.
  • Strong grasp of ethical theory (e.g., fairness, transparency, autonomy, beneficence) and practical application.
  • Experience conducting impact assessments or governance projects.
  • Exceptional written and verbal communication skills, particularly when translating complex ideas across technical and non-technical stakeholders.

✳️ Preferred Qualifications

  • Graduate-level education in Ethics, Philosophy, Organizational Psychology, HR Technology, or Data Science.
  • Background in AI ethics, medical ethics, or compliance frameworks.
  • Familiarity with ethical sourcing and inclusive design.
  • Certifications in Responsible AI, DEI strategy, and/or HR data privacy.
  • Experience working with global, multi-jurisdictional teams spanning “mail room” to “Board room”.

✳️ The Ideal Person Is…

  • A principled systems thinker with the operational savvy and emotional intelligence to drive change.
  • Committed to designing fair and inclusive hiring experiences at scale.
  • Skilled at pushing back against over-automation and caustic people strategies, policies, and processes, while enabling innovation.
  • Comfortable navigating ambiguity in a fast-paced, tech-enabled environment.
  • Equal parts philosopher, policymaker, and talent operations strategist.
  • Adept at embracing the “Tow ears, one mouth” Proportionality Rule. 

✳️ Talent Acquisition Ethicist – Role Scorecard

Mission: Ensure that our AI-enabled talent acquisition systems are ethically sound, transparent, and human-centered.

Outcomes: Operational ethical frameworks in place, measurable bias reduction in AI-assisted tools, high candidate satisfaction with transparency.

Competencies: Ethical risk assessment, TA tech enablement, cross-functional collaboration, regulatory alignment, change leadership.

Culture Fit: Empathetic, integrity-driven, systems thinker, educator, challenger of status quo.

✳️ Performance Rubric – Talent Acquisition Ethicist 

⛛ Ethical Governance & Risk Assessment

Minimal: Fails to identify or assess ethical risks in tools or processes.

Basic: Recognizes major risks but lacks proactive assessments or remediation planning.

Strong: Conducts thorough impact assessments and integrates ethical checkpoints into vendor/tech reviews.

Superb: Proactively leads organizational AI ethics governance; anticipates ethical dilemmas before they arise.

⛛ Human-Centered Design of TA Workflows

Minimal: Automates without consideration of candidate experience or psychological safety.

Basic: Makes some efforts to preserve human touchpoints, but inconsistently.

Strong: Builds intentional workflows balancing tech efficiency and human judgment.

Superb: Designs model TA experiences where empathy and automation coexist seamlessly.

⛛ Training & Ethical Literacy

Minimal: Provides no ethical guidance or enablement.

Basic: Conducts occasional ad hoc training sessions.

Strong: Implements consistent training with relevant case studies and adoption metrics.

Superb: Creates a learning culture where ethical hiring is core to TA identity.

⛛ Stakeholder Influence & Leadership

Minimal: Rarely consulted on tech or policy decisions; lacks influence.

Basic: Participates in discussions but struggles to drive outcomes.

Strong: Respected advisor and frequent contributor to strategic decisions.

Superb: Trusted ethics leader shaping policy, product direction, and cultural norms across TA.