A Nested Model for AI Design and Validation (2407.16888v2)

Abstract: The growing AI field faces trust, transparency, fairness, and discrimination challenges. Despite the need for new regulations, there is a mismatch between regulatory science and AI, preventing a consistent framework. A five-layer nested model for AI design and validation aims to address these issues and streamline AI application design and validation, improving fairness, trust, and AI adoption. This model aligns with regulations, addresses AI practitioner's daily challenges, and offers prescriptive guidance for determining appropriate evaluation approaches by identifying unique validity threats. We have three recommendations motivated by this model: authors should distinguish between layers when claiming contributions to clarify the specific areas in which the contribution is made and to avoid confusion, authors should explicitly state upstream assumptions to ensure that the context and limitations of their AI system are clearly understood, AI venues should promote thorough testing and validation of AI systems and their compliance with regulatory requirements.

• The paper introduces a five-layer nested model for AI design that integrates regulatory, domain, data, model, and prediction aspects.
• It employs Explainable AI and Human-Computer Interaction principles to enhance transparency, trust, and ethical compliance.
• Case studies illustrate improved fairness and accountability through audit trails, bias testing, and comprehensive validation practices.

A Nested Model for AI Design and Validation

The paper presents a five-layer nested model for AI design and validation, aiming to streamline the complexities associated with aligning artificial intelligence practices with regulatory frameworks. The model provides a structured approach to address trust, transparency, fairness, and discriminatory challenges within AI applications, which are pivotal for real-world adoption and regulatory compliance, particularly in high-stakes fields such as healthcare.

Approaches and Structure

The proposed model delineates AI design and validation into five distinct layers: regulation, domain, data, model, and prediction. It integrates principles from Explainable AI (XAI) and Human-Computer Interaction (HCI) to capture both technical and ethical dimensions of AI workflows.

1. Regulation Layer: This initial layer focuses on compliance, emphasizing ethical and technical regulations. It categorizes requirements established by bodies like the EU and ensures AI systems align with global regulatory standards.
2. Domain Layer: Here, domain-specific requirements are addressed, with input from domain experts ensuring AI applications remain relevant and effective within their respective fields.
3. Data Layer: Emphasizes data evaluation, including bias mitigation and distribution analysis. It involves collaboration between ML practitioners and domain experts to enhance data understanding through techniques like AI Fairness 360 for bias detection.
4. Model Layer: Focuses on interpretability and parameter analysis to balance performance with transparency. The paper suggests starting with interpretable models and leveraging post hoc methods for black-box models when necessary.
5. Prediction Layer: This layer assesses the reasoning behind predictions, considering the importance of inputs and the implications of modifications.

Novel Contributions

The paper’s core contribution lies in its extension of the XAI-Question Bank (XAI-QB), incorporating regulatory and domain perspectives. By systematically addressing the unique validity threats at each layer, the model facilitates a comprehensive evaluation of AI systems.

Implications and Case Studies

The paper highlights two preliminary case studies that incorporate ethical and theoretical guidelines via software engineering practices. These include implementing audit trails, verification, validation testing, and bias testing to enhance fairness. The nested model proposes addressing both ethical and technical requirements as key components of AI accountability.

Errors in prior AI systems illustrate the model’s applicability, exemplified by Google’s flawed diabetic retinopathy detection and Zillow’s inaccurate home price forecasting. These examples underscore the model’s potential in improving AI reliability through structured design and validation processes.

Future Directions

Looking ahead, the nested model serves as a prescriptive guideline for bridging the gap between disparate fields of AI and regulation. It provides a foundation for harmonizing technical and ethical standards, potentially influencing regulatory alignment across various international landscapes.

Enhancing collaboration between regulatory bodies, AI practitioners, and domain experts remains a promising avenue for future research. The emphasis on developing audience-centric XAI tailored to user needs could further increase AI adoption and trust.

In summary, the nested model offers a concrete framework for aligning AI practices with regulatory expectations. Its structured approach to addressing validity threats across various layers provides valuable insights into achieving comprehensive AI compliance, fairness, and transparency.