Below is a concise, practical comparison of leading AI-powered interview / assessment platforms (strengths, typical use cases, risks, and quick recommendations). I focused on platforms commonly used by TA Teams for screening, coding/technical interviews, and asynchronous video screening. Where useful I’ve cited vendor docs and recent reporting on risks and features.

Quick list of platforms covered

• HireVue (video + AI scoring historically; now limited facial analysis). (Wired.com)
• Modern Hire (enterprise interviewing + simulated interviews / automation). (vendor docs typically describe this product; see context below)
• HackerRank (coding assessments, live interview IDE, AI-assisted features). (HackerRank.com)
• Spark Hire (one-way and live video interviews; mostly non‑automated scoring and transcription/summaries). (sparkhire.com)
• Interviewing.io (anonymous/technical live interviews and on‑demand mock interviews; focused on technical screening). (HackerRank.com)

Note: I used vendor documentation and recent reporting to ensure claims reflect recent changes and controversies (see citations).

Comparison (short form)

1. HackerRank — best for technical hiring and coding assessments

• Core strengths: industry-standard coding tests, rich library of problems, live interview IDE, proctoring and code-quality grading; added AI-assisted IDE, transcripts, scorecard automation and AI-assisted assessments. Good for volume technical screening and measuring coding skill. (HackerRank.com)
• Typical use case: software engineering pre-hire screens, take-home tests, paired-programming interviews.
• Risks/limitations: proctoring and AI-assist features require careful configuration (privacy, AI‑usage disclosure) and reviewing to avoid false positives/negatives; may require engineering buy-in. (support.HackerRank.com)

2. HireVue — best for large-scale video screening (but legal/ethical scrutiny)

• Core strengths: scales one-way video screening and can automate parts of screening workflows. Historically marketed AI scoring of verbal/language features.
• Recent changes/risks: faced scrutiny and regulatory pressure over facial‑expression analysis and bias; publicly discontinued facial analysis features but still uses language/speech analysis in some products — organizations must disclose and provide alternatives. Because of controversies, candidate sentiment and legal risk should be considered. (Wired.com)
• Typical use case: high-volume early-stage screening where recruiters want to triage candidates quickly.
• Risks/limitations: public and regulatory scrutiny about fairness, bias, and transparency — requires consent/disclosure and auditability.

3. Spark Hire — best when you want video interviewing without automated AI decisions

• Core strengths: simple one‑way and live video interviews, transcript and AI-generated summaries (for recruiter efficiency). Spark Hire position: avoid using AI for automated decision-making (candidate-friendly stance). Good UX and lower legal risk because they don’t push automated hiring decisions. (sparkhire.com)
• Typical use case: small-to-medium employers who want to screen on video but avoid AI decisioning to reduce candidate pushback and legal risk.
• Risks/limitations: fewer automation/analytics features than enterprise AI platforms; you’ll still need humans to review and decide.

4. Modern Hire — best for enterprise-level structured interviewing and automation

• Core strengths: enterprise orchestration of staged interviews (simulations, structured interviews, automation). Focus on structured behavioral/situational interviewing plus automation to speed hiring. (Vendor materials emphasize simulation and structured workflows.)
• Typical use case: large enterprises seeking consistent structured interviews across many roles.
• Risks/limitations: complexity, price, and requirement for change management; you must validate automated scoring rubrics for fairness.

5. Interviewing.io (and similar technical-interview marketplaces)

• Core strengths: anonymous technical interviews, practice/mock interviews, and live pairing with engineers; useful to surface technical talent while reducing bias from résumé/identity.
• Typical use case: technical hiring pipelines where anonymized assessment and live technical evaluation are prioritized.
• Risks/limitations: more focused on technical roles; may not replace structured assessments for other functions.

Key operational concerns (apply before buying/deploying)

• Fairness and bias: Several studies and reporting show AI interview tools can mis-transcribe accents, disadvantage neurodiverse candidates, or reflect bias in training data — require audits, diverse test datasets, and external validation. Always require transparency and candidate opt-out/alternative workflows. (theguardian.com)
• Legal & compliance: jurisdictions are introducing or updating rules about use of automated decision systems in hiring. Ensure vendor can provide explainability, documentation, and consents required by local/state law. (Wired.com)
• Candidate experience & employer brand: many candidates want disclosure about AI use and prefer human review for final decisions; choosing platforms that avoid opaque scoring can improve brand perception. (Lifewire.com)
• Security & integrity: proctoring, plagiarism/collusion prevention, and secure IDEs matter for high-stakes assessments—confirm vendor controls and data-retention policies. (HackerRank.com)
• Accessibility: validate that transcripts, alternative assessments, and accommodations are supported and tested with disabled and non‑native speakers. (theguardian.com)

Decision guide — which platform to choose

• If you primarily need reliable coding/technical assessments at scale: HackerRank or Codility (HackerRank shown above with active AI features). Validate proctoring and AI-assist settings. (HackerRank.com)
• If you need high-volume one-way video screening but want to minimize AI decision risk: Spark Hire (transcripts + summaries for reviewers but no automated hiring decisions). (sparkhire.com)
• If you want enterprise structured interviewing and simulations: Modern Hire (enterprise orchestration).
• If you want anonymized, engineer-to-engineer technical screening: Interviewing.io (reduces résumé bias).
• If you plan to use automated scoring from video/audio: proceed cautiously — prefer vendors that document models, allow human review, and offer alternatives for candidates (and be prepared to audit). (Wired.com)

Checklist to evaluate any AI interview vendor (practical items to request)

• Model transparency: summary of features scored, model inputs, training data demographics (or independent audit).
• Error rates by subgroups: transcription accuracy by accent, speech‑impairing conditions, and non‑native English speakers.
• Right to explanation and human oversight: how humans review/override AI decisions.
• Candidate consent and alternatives: how the vendor supports informed consent and offers non‑AI alternatives.
• Security/compliance: SOC2, ISO, data residency, retention, and deletion policies.
• Customization & validation: ability to tune rubrics, run pilot A/B tests, and exportable data for your compliance reviews.
• Accessibility features: transcripts, captioning, time extensions, alternative formats.

If you want, I can:

• produce a short vendor-comparison table with the fields above (features, ideal use, risks, price ballpark, citation links), or
• run a focused procurement checklist with sample RFP questions tailored to your size/industry and applicable US state rules.

Which of the two would you like next?