In a recent research study, it was noted, “Without transparent and understandable AI/ML, the ambition to harness AI for improving mental health seems very distant.” This reminds us of the urgent need for simplicity and clarity in the seemingly complex and vast world of AI and machine learning, especially in healthcare.
The core pillars of clarity in AI/ML – transparency, interpretability, explainability, and understandability – not only demystify the enigma of technology but also construct a conduit to global health equity.
Transparency dismantles the opaque ‘black box’ of AI, showcasing the inner mechanics of an AI/ML model. It offers a clear line of sight into how data is processed, which helps to illuminate the otherwise “black box” nature of advanced AI/ML models.
Interpretability simplifies complex processes into manageable insights, offering a key to unlocking the reasons behind AI’s decision-making process. It plays a pivotal role in distilling complex AI/ML processes into manageable pieces of information, thus ensuring that the insights generated are relatable and useful for stakeholders.
Explainability imparts AI/ML models the capability to articulate their reasoning in human-comprehensible terms. It is the ability of an AI/ML model to justify its outputs in a manner that non-tech-savvy humans can understand.
Understandability, the culmination of transparency, interpretability, and explainability, makes AI/ML outputs coherent and easily grasped by humans. It is the extent to which AI/ML output can be comprehended by a human, which is essential for building trust and driving practical actions.
When these pillars are weak or absent, the impact is profound. Lack of transparency can lead to misuse or mistrust of AI/ML models. A lack of interpretability and explainability can result in important insights being overlooked or misinterpreted. If a model is not understandable, it may not be utilized effectively or could lead to incorrect decision-making.
In healthcare, where decisions often hold life-or-death consequences, these pillars are particularly essential. We need to understand why an AI algorithm made a mistake, by tracing its decision-making process. This isn’t always possible with most AI models available currently, hindering our ability to correct and prevent future issues.
In conclusion, the pursuit of clarity in AI/ML isn’t just a quest for scientific advancement, but a journey towards global health equity. In unravelling the intricate webs of AI decision-making, we foster transparency, interpretability, explainability, and understandability. We light the path for more confident and informed decision-making across business, technology, and healthcare sectors.
By embracing these core pillars, we not only demystify the complexities of AI/ML but pave the way for a healthier, more equitable world. The world where the benefits of AI are not just concentrated in technologically advanced societies, but are universally accessible, strengthening the bonds of global health equity, and reminding us that progress in AI/ML is, ultimately, progress for humanity.
Implications and Actionable Insights for Stakeholders:
Business Stakeholders:
- Investors: AI transparency showcased by target investments, e.g. health tech startups, could improve investment stability – Consider businesses that prioritize model transparency and regular audits as part of their governance model.
- Entrepreneurs: Superior AI interpretability could provide a competitive advantage – Leverage interpretability in AI/ML models during the early stages of product development to improve traceability and accelerate adoption.
- Project Managers: Explainability of AI models enhances project execution and improves team understanding of “path to output” – Implement training modules for your teams to improve AI explainability and understand it’s significance.
Technology Stakeholders:
- Chief Information Officer: Ensuring transparency in AI/ML models can streamline the process of troubleshooting – Establish documentation protocols and open sharing of model development processes.
- Data Scientists: AI model interpretability reduces failure rates and enhances the reliability of the output-generating engine – Develop and apply rigorous evaluation metrics for AI model interpretability.
- IT Infrastructure Manager: Explainability in AI reduces risks related to system interactions – Include AI models that provide clear decision-making explanations in your system design.
- Developers: Increased user engagement through understandable AI can enhance product adoption – Implement AI visualization tools in the interface design that help users understand the decision-making process (if prompted by the user).
Healthcare Stakeholders:
- Patients: Trust in AI-driven health diagnoses is enhanced with transparent AI/ML models – Advocate for AI-based health solutions that provide clear explanations of their decision-making.
- Physicians: AI interpretability forms a key foundation for effective treatment plans – Show preference for AI/ML tools with high interpretability in clinical practice and advocate the need for more transparency.
Source: https://www.nature.com/articles/s41746-023-00751-9#Fig2
