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Immediate AI Adoption vs. Pause-and-Evaluate Approach

Source & Transformation

This comparison draws in part from “Pause Before Proceeding: Ethical Considerations Around the Clinical Use of Artificial Intelligence (AI) and Machine Learning (ML)” by Rebecca Womack, MS, BCBA, LBA (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.

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In This Guide
  1. Side-by-Side Comparison
  2. Clinical Decision Framework
  3. Key Takeaways

One of the most consequential decisions a behavior analyst makes is not just what intervention to use, but how to approach the clinical question in the first place. For pause before proceeding: ethical considerations around the clinical use of artificial intelligence (ai) and machine learning (ml), the difference between an evidence-based, individualized approach and a traditional, protocol-driven one can significantly impact outcomes.

This guide lays out the key factors side by side to support your clinical decision-making.

Side-by-Side Comparison

Factor Evidence-Based Approach Traditional Approach
Speed of technology integration Immediate adoption captures efficiency gains quickly and positions the practice as technologically current Pause-and-evaluate delays efficiency gains but ensures ethical and clinical risks are addressed first
Risk of harm to clients Higher risk if tools contain biases, inaccuracies, or privacy vulnerabilities that were not identified pre-adoption Lower risk because potential harms are identified and mitigated through structured evaluation before implementation
Practitioner competence May outpace practitioner understanding of how the tools work and what their limitations are Allows time for practitioners to develop AI literacy before depending on AI outputs for clinical decisions
Informed consent quality Consent processes may lag behind technology implementation, leaving families uninformed Consent processes are updated proactively as part of the evaluation before any client data enters an AI system
Clinical judgment preservation Risk of premature dependency on AI if independent clinical skills are not deliberately maintained Independent clinical skills are maintained as a prerequisite for evaluating and overriding AI outputs
Organizational reputation May be perceived as innovative but risks reputational damage if AI-related incidents occur May be perceived as cautious but builds reputation for responsible, ethical technology use
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Clinical Decision Framework

Use this framework when approaching pause before proceeding: ethical considerations around the clinical use of artificial intelligence (ai) and machine learning (ml) in your practice:

Step 1: Is intervention warranted?

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

Step 2: Have you conducted an individualized assessment?

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

Step 3: Is the individual/caregiver involved in decision-making?

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

Step 4: Verify your approach

Key Takeaways

Go Deeper With This CEU

This course covers the clinical and ethical dimensions in detail with structured learning objectives and CEU credit.

Pause Before Proceeding: Ethical Considerations Around the Clinical Use of Artificial Intelligence (AI) and Machine Learning (ML) — Rebecca Womack · 1 BACB Ethics CEUs · $20

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Research Explore the Evidence

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.

Measurement and Evidence Quality

279 research articles with practitioner takeaways

View Research →

Brief Behavior Assessment and Treatment Matching

252 research articles with practitioner takeaways

View Research →

Brief Functional Analysis Methods

239 research articles with practitioner takeaways

View Research →

Related

CEU Course: Pause Before Proceeding: Ethical Considerations Around the Clinical Use of Artificial Intelligence (AI) and Machine Learning (ML)

1 BACB Ethics CEUs · $20 · BehaviorLive

Guide: Pause Before Proceeding: Ethical Considerations Around the Clinical Use of Artificial Intelligence (AI) and Machine Learning (ML) — What Every BCBA Needs to Know

Research-backed educational guide

FAQ: 10 Questions About Pause Before Proceeding: Ethical Considerations Around the Clinical Use of Artificial Intelligence (AI) and Machine Learning (ML)

Research-backed answers for behavior analysts

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Clinical Disclaimer

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.

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