AI Black Boxes
Did you know an airplane’s “black box”, its flight recorder, is in fact not black? This device, which records the performance and condition of an aircraft in flight, is painted bright orange so it can be easily traced and recovered.
Just like these misnomered black boxes – an “AI black box”, an artificial intelligence system whose inputs and operations cannot be explained by users (and developers) – brings to mind an impenetrable device. For some AI prediction models highly accurate-no-fuss black boxes can be effective. However, for applications where trust is paramount, there’s a need for a “clear-colored” box that can be easily accessible.
Notwithstanding, AI “white boxes” do exist, but they are often based on simpler models that provide weak predictive capabilities and are not always best in modeling the inherent complexity of a dataset. So, is it possible to harness the accuracy and complexity-adept nature of black boxes and make them clearer and more transparent in order to boost confidence?
The Need for Trust
Healthcare AI is one of the fields where trust is of the highest importance. Although AI has become an inseparable part of advanced medicine today, including patient-doctor online interfaces, diagnostics, risk-management and decision-making, patients and providers still need to feel the artificial intelligence predictions are backed by human intelligence.
Some of the recommendations which prediction models in healthcare may lead to include surgery, hospitalization, changing a specific treatment, or ending a treatment altogether. Needless to stress that such decisions must gain the utmost trust of patients – as well as providers. Often, hypotheses drawn by AI systems should also be validated by subject matter experts that – again – need to understand the logic behind the Medical AI.
And last but not least, regulation and compliance standards are a crucial part of a well functioning healthcare system, and regulatory bodies must get a clear and transparent picture of decision-making and processes that involve artificial intelligence.
“Making healthcare experts trust and work with AI models will make a remarkable impact on the healthcare industry. It will accelerate the development and deployment of new AI solutions for new areas in healthcare. It will reduce the physicians’ burden and allow for better diagnoses and optimal personal care in less time. Hospital managers will be able to utilize their resources better (personnel, medical equipment) and save time and money.”
What is Explainable AI?
So how do we get brighter-colored boxes for the sake of making good AI prediction models – and especially healthcare prediction models – accessible and understandable?
By incorporating advanced explainability techniques, XAI companies such as Demystify, a company that also combines sophisticated proprietary methods, can help domain experts understand the decisions made by complex AI-models and provide insights and explanations about the model predictions for patients, providers and regulators.
XAI can also provide actionable insights and alerts about an AI model’s risks, limitations, blindspots, and bias during development and deployment, offering monitoring and risk-assessment layers for better decision making.
By incorporating AI Explainability tools, AI black boxes are “painted orange” – they become clearer and help boost the trust levels of users, patients, provides and regulators alike.