Guidelines for Ethical AI Use in Forensic Vocational Evaluations

Purpose 
The role of ABVE in relation to Artificial Intelligence (AI) technologies is to offer guidance for members to ensure enhancements to forensic vocational evaluations when used ethically and with expert oversight. These guidelines reflect a commitment to preserving professional integrity, safeguarding individuals’ rights and data and encouraging thoughtful innovation in a rapidly changing technological landscape.

These guidelines are suggested to establish best practices for the ethical, transparent and developmentally appropriate use of AI within the context of forensic vocational evaluations.

The goal is to ensure that AI technologies enhance—rather than replace—expert judgment in evaluating employability, earning capacity and vocational rehabilitation potential within forensic settings. This also serves to guide ongoing evaluation, critique and improvement of AI applications in the field of forensic vocational analysis. 

Scope
This policy applies to all ABVE members (forensic vocational experts, evaluators and analysts) using AI tools to analyze labor market data, administer assessments and support expert opinions in litigation. 

Definitions

AI (Artificial Intelligence): The use of computer systems to perform tasks that typically require human intelligence, such as data analysis, synthesis/summarization and pattern recognition. In forensic vocational evaluations, AI may assist with job matching, wage prediction or analysis of large datasets (e.g., labor market data), but must be used with awareness of ethical and methodological implications. 

Algorithmic Bias: A systematic error in an AI system that results from flawed assumptions, biased data or skewed modeling—leading to unfair or inaccurate outcomes. 

Descriptive Analytics: An analysis of existing data—such as wage statistics, employment trends, worklife expectancy tables and job availability. In the context of forensic vocational evaluations, descriptive analytics refers to the use of data to summarize, organize and interpret past or present information about an individual's vocational profile or the labor market. 

Evaluee: The individual being assessed in a forensic vocational evaluation. 

HIPAA (Health Insurance Portability and Accountability Act): A U.S. federal law that establishes national standards to protect sensitive patient health information from being disclosed without the patient’s consent or knowledge. 

GDPR (General Data Protection Regulation): A regulation enacted by the European Union to govern how personal data of EU citizens is collected, stored, processed, and transferred. Although not directly applicable to most U.S.-based evaluations, GDPR principles emphasize transparency, data minimization, informed consent and the right to access or erase personal data. 

PII (Personally Identifiable Information): Any data that could be used to identify a specific individual, such as name, date of birth, Social Security number, address or medical history. 

Predictive Analytics: A branch of data analytics that uses historical data, machine learning models and statistical algorithms to forecast future outcomes. 

Principles of AI Integration
AI use in forensic vocational evaluations should align with the following guiding principles:

  1. Cautious Appraisal of Results: All AI-generated outputs should be interpreted with professional skepticism, contextual analysis and clinical judgment.
  2. Ethical Considerations: AI should be free from bias based on race, gender, disability or other protected characteristics. The evaluator is responsible for validating and documenting that AI models do not introduce detectable bias.
  3. Human Oversight: Qualified forensic vocational experts should interpret and integrate AI outputs, ensuring contextual accuracy and avoiding overreliance on AI. Outputs should be reviewed and interpreted by a qualified vocational expert.
  4. Informed Consent: Use of AI tools within the evaluation process should be disclosed to the evaluee, with proper consent obtained (where applicable). Evaluators may consider including a consent clause noting that AI-supported tools may be used in labor market analysis or document review, with safeguards for privacy and evaluator oversight. When AI tools are used, evaluators may also consider briefly describing the tool’s role in the report methodology section, including a statement clarifying that final opinions were rendered by the evaluator based on their professional judgment.
  5. Duty of Care: Evaluators bear a professional and ethical obligation to critically evaluate AI outputs and use reasonable judgment when implementing the use of AI in their practice.
  6. Reliability & Validity: AI-generated findings should be based on validated methodologies and cross-checked with traditional vocational evaluation techniques. AI applications should use validated methodologies and be corroborated with established vocational evaluation practices.
  7. Data Privacy & Security: Personal and vocational data used in AI applications should comply with confidentiality regulations, including HIPAA, GDPR and industry best practices. Personally identifiable information (PII) and sensitive client data should be safeguarded in accordance with HIPAA, GDPR and best cybersecurity practices.

AI Applications in Vocational Evaluations
AI may be utilized in, but not limited to, the following areas:

  • Labor Market Analysis: AI-assisted research on job availability, wage trends and worklife expectancy data. 
  • Vocational Interest & Aptitude Testing: AI-supported administration and scoring of standardized vocational assessments, ensuring compliance with psychometric standards. 
  • Predictive Analytics: AI-based models to estimate earning capacity and employability trends based on historical labor data.
  • Speech-to-Text & Translation Services: Assisting evaluations for individuals with limited English proficiency or communication barriers. 
  • Document Review & Summarization: AI-generated synthesis of case documents such as medical records, depositions and labor market data to enhance efficiency in forensic reports and/or analysis; while ensuring adherence to data privacy guidelines. 
  • Job Searching: AI tools may be used to support job search activities by identifying relevant openings, aggregating job market data and streamlining job-matching based on the evaluee’s vocational profile. Such use should ensure transparency, avoid algorithmic bias and be supervised by a vocational expert to confirm the appropriateness and actual existence of the job matches identified.

Limitations on AI Use
The following applications of artificial intelligence (AI) are prohibited due to ethical concerns, risks to data security, and limitations in methodological reliability. These restrictions are intended to preserve the integrity of expert analysis and to ensure compliance with professional, legal, and privacy standards. 

  • Automated Vocational Conclusions: AI cannot independently determine disability status, work restrictions, employability or vocational potential without human expert oversight. 
  • Bias-Induced Decision-Making: AI tools must not be used in a way that reinforces or perpetuates bias based on race, gender, disability or socioeconomic status. Evaluators are responsible for identifying and mitigating algorithmic bias through critical review and professional judgment. 
  • Algorithmic Bias Awareness: In vocational evaluations, this may manifest as AI-generated results that disproportionately disadvantage certain populations based on gender, race, disability or socioeconomic status. Evaluators should take any necessary steps to avoid this. 
  • Unaudited Predictive Modeling: AI-generated forecasts, such as earning capacity or worklife expectancy projections, must not be presented without corroboration using accepted forensic methodologies. Unverified predictions should never form the basis of expert opinions. 
  • Ethical Awareness: must be used with awareness of ethical and methodological implications. 
  • Respect for Evaluee’s Rights: When the evaluee’s education, work history, medical condition and functional capacity are analyzed with the support of AI tools, evaluators should ensure their data is handled ethically, securely and without automated decision-making that bypasses professional judgment. 
  • Human Oversight and Clinical Judgment: All AI outputs must be reviewed, interpreted and contextualized by the evaluator. Automated decision-making that bypasses clinical reasoning is ethically unacceptable and inconsistent with forensic best practices. 
  • HIPAA Compliance (U.S.): AI platforms must protect Protected Health Information (PHI) and comply with HIPAA standards for data use, storage and transfer. Evaluators should ensure that all AI vendors or tools meet security and access control requirements, that AI tools do not inadvertently expose Protected Health Information (PHI) and that vendors or platforms used are compliant with HIPAA regulations. 
  • GDPR Awareness (General Data Protection Regulation)/International Considerations: Forensic practitioners working with international cases or using cloud-based AI services must be mindful of GDPR principles, including data minimization, evaluee consent, algorithmic transparency and the right to data access or deletion. 
  • PII (Personally Identifiable Information): When using AI in forensic practice, protecting PII is critical to maintaining confidentiality, data security and compliance with HIPAA or other privacy regulations. 
  • Predictive versus Descriptive Analytics: 
    • Descriptive analytics (e.g., historical wage data, job availability) may be used to summarize current or past information relevant to the case. 
    • Predictive analytics, when employed, must not be used to assign legal conclusions or diagnostic opinions. All predictions must be accompanied by clinical insight, evaluator discretion and validation through case-specific data and traditional vocational methodologies.

These predictions must be contextualized with clinical judgment, human oversight and individual case factors. AI-based predictions must not be used to assign case conclusions (e.g., disability status) and should always be corroborated with traditional methods, professional standards, case-specific data and evaluator judgment. 

Disclosure & Transparency
Forensic vocational experts using AI tools may consider disclosing:

  • The type of AI technology employed in the evaluation.
  • The purpose and limitations of AI assistance.
  • How AI findings were integrated with human expert judgment.
  • Any potential limitations in AI-based labor market or vocational assessments.

Compliance & Oversight
All AI-assisted vocational evaluations should adhere to:

  •  Ethical standards set by the American Board of Vocational Experts (ABVE), the International Association of Rehabilitation Professionals (IARP), the Commission on Rehabilitation Counselor Certification (CRCC), the American Psychological Association (APA) and other professional guidelines applicable to the evaluator. 
  • Legal standards for admissibility under the Federal Rules of Evidence (FRE 702) and Daubert criteria. 
  • Regular audits to ensure AI tools remain compliant with scientific rigor and industry best practices. Evaluators are encouraged to audit AI tools annually or whenever models are updated, and to document evidence of fairness, transparency and lack of harmful bias.

Proficiency and Competency in Use of AI
Forensic vocational evaluators are encouraged to obtain training, experience and professional competency prior to integrating AI technologies into their practice. Proficiency includes: 

  • Understanding of AI Limitations: Evaluators must recognize the scope and boundaries of AI- generated outputs and avoid overreliance on algorithmic conclusions. AI tools must augment— not replace—expert vocational judgment. 
  • Training in Confidentiality and Ethical Use: Evaluators must be knowledgeable about the limits of confidentiality when using AI platforms, including the potential risks of sharing personally identifiable information (PII) with third-party or cloud-based tools. Use must comply with HIPAA, GDPR and other applicable privacy regulations. 
  • Evidence of Expertise: When appropriate, professionals should seek continuing education, vendor training or formal instruction to ensure competent use of specific AI platforms employed in evaluations. At the very least, evaluators should be able to explain the AI tool’s function, data sources and limitations within confined of their use in forensic vocational analysis. 
  • Accountability:  The evaluator retains sole responsibility for all conclusions and recommendations, regardless of the role played by AI. Any misuse or misinterpretation of AI data may be considered a breach of professional ethics.

  • Competence. Evaluators should only utilize AI tools after they have established competence and understand the ethical, legal and methodological considerations associated with their use. 

As artificial intelligence continues to evolve and influence professional practice, the forensic vocational field stands at a pivotal intersection of innovation and responsibility. This document, therefore, reflects a proactive response to the burgeoning role of AI, encouraging thoughtful integration that upholds the highest standards of ethical practice and expert clinical judgment. 

In the spirit of innovation, these guidelines are intended not as static rules, but to provide ongoing support to evaluators as they navigate the new opportunities and nascent complexities of AI-enhanced vocational analysis. 

By fostering competence, transparency and accountability, we aim to ensure that AI serves as a tool to strengthen, rather than substitute, the expertise that defines forensic vocational evaluation.