Algorithmic Bias and Dialect Equity
AI speech recognition systems are trained predominantly on Standard American English. Research has documented substantially higher error rates for speakers of African American English, and similar disparities exist for other dialects and language varieties including Appalachian English, Southern American English, and code-meshed speech. In clinical settings, this creates real risk that dialect differences could be misidentified as disorders.
EASI's clinician-attestation model is the architectural safeguard against this risk. EASI never auto-scores from raw ASR output. The clinician — who knows the child, knows the family, and understands the dialect — reviews and attests every transcription before any scoring occurs. Dialect differences are differences, not disorders, and EASI's workflow ensures the clinician makes that determination, not the machine.
Beyond attestation, EASI's scoring pipeline filters through age first, then language exposure and bilingual status, then diagnosis and health context. Scores without context are dangerous misinformation. Every metric EASI produces carries the clinical context required to interpret it responsibly.
Neurodiversity-Affirming Practice
A growing movement within speech-language pathology is reshaping how clinicians approach autism and neurodivergent communication. This movement emphasizes strengths-based goals, client-led therapy, and respect for diverse communication styles. AI tools that generate goals aimed at "normalizing" neurodivergent communication are increasingly incompatible with this direction.
EASI is a clinical data platform, not a therapeutic philosophy. MySLP generates suggestions, not directives. Clinicians write their own goals using EASI's data. The platform provides the infrastructure for comprehensive assessment — including pragmatics, social communication profiling, and narrative assessment — but the clinical interpretation belongs to the clinician. EASI supports whatever therapeutic philosophy the clinician brings to the work, including neurodiversity-affirming approaches.
Culturally and Linguistically Responsive Assessment
Only about 8-9% of ASHA-certified professionals self-identify as multilingual service providers, yet they serve increasingly diverse populations. AI trained on monolingual English norms can reinforce this mismatch, producing metrics that penalize linguistic diversity rather than accounting for it.
Language sample analysis — the methodology EASI automates — is widely recognized as one of the most ecologically valid and least biased forms of assessment compared to standardized tests normed predominantly on monolingual English-speaking children. By making LSA practical and fast, EASI removes the time barrier that forces clinicians to rely on instruments with narrower normative samples. Dynamic assessment, ethnographic interviewing, and converging evidence approaches are complementary to EASI's workflow and align with ASHA's current professional development content areas around cultural responsiveness.
Data Privacy as an Ethical Commitment
The children whose data flows through speech therapy systems are disproportionately from communities that have historically had the least power over how their information is used. Protecting their data is not just a regulatory checkbox — it is a matter of professional responsibility. EASI treats data privacy as an ethical stance, not merely a compliance requirement.
EASI is fully HIPAA and FERPA compliant with a Business Associate Agreement. All data runs on encrypted AWS healthcare infrastructure. Conversational data in MySLP has a 24-hour TTL. Patient data is never used to train AI models. Your students' data never leaves the secure system and is never shared with third parties.
Equitable Access
The Bureau of Labor Statistics projects 15% growth in SLP positions from 2024 to 2034, classified as "much faster than average," yet persistent understaffing continues — especially in rural communities and Title I schools where caseloads are highest and resources are thinnest. The clinicians who need efficiency tools the most are often the ones who can least afford enterprise-priced platforms.
EASI's $199/year price point is intentionally accessible — designed so individual clinicians and smaller districts can afford it, not just large systems with enterprise budgets. Reducing the time burden of documentation means clinicians can spend more time doing the work that actually matters: working with children.