The term Artificial Intelligence (AI) which debuted in 1956 had evolved into a prominent essential in human life. From a robotic figure that is able to solve a particular problem through machine learning to smart appliance that are able to adapt according to the surrounding environment through both machine learning and deep learning, AI technologies will continue to evolve with never ending boundary. The purpose of AI is for the betterment of life by simplifying human effort and helping in making better decisions with far-reaching outcomes. Definitely, the purpose of AI is all great, however what about its implementation and consequences? How far can human trust AI in making critical decisions that may affect the future of their business growth, safety and health, even security and privacy? While there is no one size fit all solution, AI TRiSM had been brought into the limelight as a technology trend that can address this issue. Therefore, how does AI TRiSM work in eliminating AI Trust issues?
At its core, AI TRiSM aims to optimise AI adoption for better reliability, improve functionalities and maintain value integrity of AI system in production that work based on the following components:
Explainability – AI TRiSM should include information that clearly explains the objective of an AI Model adoption. With this component, organisations will be able to understand their AI model behaviour interpretation, as well as the performance of their unique AI model in term of accuracy, accountability and transparency.
ModelOps – Model Operationalisation or ModelOps is an essence in AI TRiSM that focuses on end-to-end lifecycle management and governance of every analytics, AI and decision model which include analytical model, model based on machine learning, knowledge graph, linguistic, rules, optimisation and many more.
Data Anomaly Detection – Data anomaly detection in AI TRiSM involves drift monitoring and detection of anomalies, poisoning and pertubation. This pillar of AI TRiSM helps organisation to improve performance by having full visibility on AI data issues that can affect effective decision making.
Adversarial Attack Resistance – Adversarial attacks are AI threats that involve the use of deceptive data to alter machine learning algorithm to disrupt an AI model functionality. AI TRiSM focuses on detecting and preventing adversarial attacks through attacks detection, attack defense, artifacts localisation and adversarial training.
Data Protection – AI technology generates and utilises abundance amounts of data. Data protection is crucial as it data privacy breach may cause various damaging impacts including financial loss, reputational loss, safety and health threats. AI TRiSM ensures governance in being in compliance to regulations such as General Data Protection Regulation (GDPR) as well as enhance data privacy through the use of synthetic data, differential privacy and secure computing techniques including Secure Multi-party Computation (SMPC) and Full Homomorphic Encryption (FHE).
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