AI: Vendor Engagement Before And After Deployment of Technology
Event Details
Abstract
As artificial intelligence (AI) tools increasingly enter applied behavior analysis (ABA) service delivery, behavior analysts must ensure that innovation does not outpace ethical responsibility, clinical integrity, and regulatory compliance. This presentation provides a structured framework for evaluating and embedding AI systems into ABA organizational workflows using four core pillars identified in the CASP AI guidance: data security, technical transparency, evaluation frameworks, and domain expert involvement.
Participants will examine how AI tools intersect with client privacy protections, professional accountability, and Generally Accepted Standards of Care (GASC). The session will review essential safeguards such as encryption, audit trails, and regulatory compliance (e.g., HIPAA), as well as indicators of technical transparency including explainability, bias mitigation, validation practices, and risk management. Attendees will also explore how structured evaluation frameworks and ongoing domain expert oversight protect against clinical misuse, deskilling, and ethical drift.
Through guided discussion and applied vendor-evaluation questions, participants will learn how to critically assess AI products to ensure they enhance—rather than replace—clinical judgment. This presentation supports ethical decision-making and responsible technology integration in ABA practice while maintaining alignment with professional standards and client welfare.
Learning Objectives
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Define and describe the four core pillars for embedding AI into ABA workflows—data security, technical transparency, evaluation frameworks, and domain expert involvement—and explain their relevance to protecting clients and maintaining compliance with legal and ethical standards
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Identify and evaluate key data security safeguards (e.g., encryption, access controls, audit trails, regulatory compliance) necessary to protect protected health information (PHI) across the AI lifecycle in ABA service delivery
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Analyze indicators of technical transparency in AI systems, including explainability, auditability, bias mitigation, and validation practices, to determine whether a tool supports—rather than replaces—clinical judgment
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Apply structured evaluation questions to assess whether an AI vendor’s tool aligns with evidence-based practice, Generally Accepted Standards of Care (GASC), and ethical accountability through the involvement of qualified domain experts

This webinar offers 1.0 BACB Learning CEU.
Cost
- CASP Members - Free!
- Non-Members - $20.00
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Presenters
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Summer Gainey, BCBA-D, LBA
Missy Olive, PhD, BCBA-D, LBA
Alexandra Tomei, BCBA, LBA
Adam Hahs, PhD, LBA, BCBA-D
