This comparison draws in part from “The Ethics of Inaction: Why NOT Using AI Could Violate Our Ethics Code” by Adam Ventura, PhD BCBA (BehaviorLive), and extends it with peer-reviewed research from our library of 27,900+ ABA research articles. The decision framework, BACB ethics code references, and cross-links below are synthesized by Behaviorist Book Club.
View the original presentation →The debate about AI in ABA practice has often been framed as a binary choice between enthusiastic adoption and principled resistance. Ventura's presentation challenges that framing by asking practitioners to apply the same evidence-based reasoning to technology adoption that they apply to intervention selection. Goodhew & Edwards (2026) found that even for a well-established construct like theory of mind, the instrument chosen determines the sensitivity and reliability of the measurement—illustrating that tool selection decisions have real clinical consequences. The comparison below maps the two stances practitioners most commonly take toward AI and the implications of each for client care and professional compliance.
| Factor | Evidence-Based Approach | Traditional Approach |
|---|---|---|
| Decision basis | Evidence-based adoption: Tool evaluated against specific clinical problem; adoption decision driven by outcome data from pilot evaluation | Categorical avoidance: Adoption declined based on general concerns about AI; specific tool capabilities not evaluated against specific clinical problems |
| Code 1.01 compliance | Evidence-based adoption: Practitioner stays current with AI developments in the field and evaluates them against the evidence standard | Categorical avoidance: Practitioner may fall behind the evolving evidence base for AI applications, potentially violating the currency requirement |
| Client outcome risk | Evidence-based adoption: Risk concentrated in adoption errors (incorrect use, insufficient verification); managed through structured evaluation and verification protocols | Categorical avoidance: Risk concentrated in missed efficiency gains and forgone improvements in documentation, assessment, and supervision quality |
| Confidentiality management | Evidence-based adoption: Confidentiality evaluated tool-by-tool; tools that cannot meet HIPAA requirements are not used for client data | Categorical avoidance: Confidentiality concerns generalized from inadequate tools to all tools, including those with documented HIPAA compliance and BAA availability |
| Professional accountability | Evidence-based adoption: Practitioner can articulate the evidence basis for adoption decisions and the safeguards in place for each tool used | Categorical avoidance: Practitioner can articulate principled concerns but not evidence-based evaluation of whether specific tools meet or fail those concerns |
| Supervisory implication | Evidence-based adoption: Supervisors can teach supervisees structured AI evaluation skills applicable across tools and applications | Categorical avoidance: Supervisors model categorical rejection without evidence, which may not serve supervisees who will encounter AI tools throughout their careers |
The ABA Clubhouse has 60+ on-demand CEUs including ethics, supervision, and clinical topics like this one. Plus a new live CEU every Wednesday.
Use this framework when approaching the ethics of inaction: why not using ai could violate our ethics code in your practice:
Does the data support a need for intervention? Is there a meaningful impact on the individual's quality of life, safety, or access to reinforcement?
YES → Proceed to assessment NO → Document reasoning, monitor
A functional assessment should guide intervention selection. Avoid defaulting to standard protocols without individual analysis. Consider environmental variables, setting events, and private events.
YES → Select evidence-based approach matched to function NO → Complete assessment first
Goals should be co-developed. Assent and informed consent are ethical requirements. The individual's preferences and values matter in selecting both goals and methods.
YES → Proceed with collaborative plan NO → Engage in shared decision-making
This course covers the clinical and ethical dimensions in detail with structured learning objectives and CEU credit.
The Ethics of Inaction: Why NOT Using AI Could Violate Our Ethics Code — Adam Ventura · 1 BACB Ethics CEUs · $20
Take This Course →We extended this decision guide with research from our library — dig into the peer-reviewed studies behind each approach, in plain-English summaries written for BCBAs.
258 research articles with practitioner takeaways
239 research articles with practitioner takeaways
239 research articles with practitioner takeaways
1 BACB Ethics CEUs · $20 · BehaviorLive
Research-backed educational guide
Research-backed answers for behavior analysts
All behavior-analytic intervention is individualized. The information on this page is for educational purposes and does not constitute clinical advice. Treatment decisions should be informed by the best available published research, individualized assessment, and obtained with the informed consent of the client or their legal guardian. Behavior analysts are responsible for practicing within the boundaries of their competence and adhering to the BACB Ethics Code for Behavior Analysts.