Sense
Sense is a conversational AI talent engagement platform that screens, scores, matches, and schedules candidates for high-volume recruiting via chatbot, SMS/WhatsApp, and voice AI on top of a customer's ATS.
§ 01 — Score breakdown
§ Score breakdown
Category scoring
Weighted contribution shown to the right of each bar.
- 01
Article 11 Technical Documentation
Weight 20%60
+12.0
- 02
Bias Audit Transparency
Weight 18%72
+13.0
- 03
FRIA Support
Weight 15%38
+5.7
- 04
Data Governance Disclosure
Weight 15%58
+8.7
- 05
Human Oversight Design
Weight 12%64
+7.7
- 06
Post-Market Monitoring
Weight 12%52
+6.2
- 07
Customer Documentation
Weight 8%62
+5.0
§ 02 — Strongest · weakest
Strongest category
Bias Audit Transparency
Raw score 72 · contributes 13.0 to total.
Weakest category
Customer Documentation
Raw score 62 · contributes 5.0 to total.
§ 03 — Cited evidence
§ Evidence
Cited per category
Every score is backed by at least one cited piece of evidence.
§ 04 — Editorial notes
Company overview
Sense (legally Sense Talent Labs, Inc.) is a San Francisco-based talent engagement platform founded in 2016 that automates high-volume recruiting through conversational AI. Its products span an AI chatbot, SMS/WhatsApp messaging, conversational Voice AI, interview scheduling, and an AI Candidate Matching/scoring engine that shortlists applicants on top of ATSs like Bullhorn, Greenhouse, iCIMS, and Workday. The company is venture-backed (Series D, $90M+ raised from SoftBank Vision Fund, GV, and Accel) and serves 5 of the 10 largest global staffing firms, placing it firmly in the enterprise tier.
Regulatory exposure
Sense Candidate Matching and Chatbot are screening AEDTs squarely in scope for NYC Local Law 144, Colorado SB 205, Illinois, and the EU AI Act's high-risk Annex III employment category. Sense is unusually well-prepared for a platform of its size: it completed an early Holistic AI bias audit (~2024) and now publishes a public Warden AI assurance dashboard (trust.warden-ai.com/sense) with downloadable, continuously updated reports mapped to NYC LL 144, Colorado SB 205, the EU AI Act, and California FEHA. Its main exposure is that the Warden reports are conducted on Warden's synthetic test dataset and carry a 'for demonstration purposes only' disclaimer, and that no EU AI Act FRIA template or Article 27 deployer assessment is offered.
Path to a higher score
To raise its scores, Sense should publish (or clearly link) a customer-facing model card / instructions-for-use pack and pursue ISO 42001 to lift its Article 11 standing; convert the Warden 'demonstration' audits into employer-applicable or production-data audits and keep two consecutive dated public cycles; and add explicit EU AI Act deployer guidance, an Article 27 FRIA template, and a published DPA and status/incident page to close the FRIA, data-governance, and post-market-monitoring gaps.
Conflicts of interest
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