Branch of machine learning focused on giving explanations of machine learning predictions to humans
To make sure that machine learning models are transparent so that potentially harmful decisions are not taken by machine learning systems.
Determining that the machine learning systems are bias free i.e., not discriminating against people on the basis of socio-economic or demographic characteristics.
Ensure that machine learning systems are compliant with regulations and are ethically sound in the most acceptable manner.
Going beyond the artificial to enhance the human decision-making capability with Machine Learning aided assistance.
With millions of lives that are being treated every day, technological assistance is augmented by interpretable or explainable models. Interpretability provides the medical personnel with explanations that deliver the necessary trust towards machine learning systems. Moreover, it ensures that medical personnel do not take the incorrect decisions by allowing them to interrogate machine learning systems. Explainable AI allows the possibility of new hypothesis generation which can lead to the discovery of new knowledge.Download our research paper
The drive towards greater penetration of machine learning in healthcare is being accompanied by increased calls for machine learning and AI based systems to be regulated and held accountable in healthcare. Explainable machine learning models can be instrumental in holding machine learning systems accountable.
Healthcare offers unique challenges for machine learning where the demands for explainability, model fidelity and performance in general are much higher as compared to most other domains.
KenSci’s Explainable Models in Healthcare tutorial have been presented to over 1000 participants across the world. Sign up for your tutorial today.Sign up
While healthcare ML has demonstrated significant value, one pivotal impediment relates to the black box nature, or opacity, of many machine learning algorithms. Explainable models help move away from the black box nature.
Regulations on machine learning for requiring explanations (GDPR) means that explainability will become a defect requirement.
Speed up adoption of machine learning systems in healthcare by making them more trustable for medical practitioners.
Optimization of machine learning systems by allowing humans to provide feedback into incorrect predictions.
It isn’t just about the result, it is about how they got to the result and the journey that led to the insight.
If there is one thing that can make Artificial Intelligence truly Assistive in healthcare and less robotic it remains the capability to explain the predictions AI makes, so clinicians can trust the algorithm. With Data as the new Fuel and AI as the new electricity (energy), the ability to generate explanations at scale personalized to individual (patients) is the new power-grid: the backbone on which we build the trust in mass consumption of AI and ML.-Ankur Teredesai, CTO, KenSci
Whether you’re a health system looking to kick start your AI journey or an established player with a data science team, looking at building your own machine learning applications, we want to connect with you. Together, we can build explainable models that transform your healthcare business.
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