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Technical ConsultingRegulatory & Commercial Strategy

Trustworthy AI

By Applied Nanodetectors Ltd

“All participants have a right to understand how their data is being used and how the AI is making decisions. The AI’s algorithms, attributes, and correlations must be open to inspection, and its decisions must be fully explainable." AND Ltd. provide clear and transparent explanations of the reasoning behind the AI’s decisions, such as visualizing the decision process, providing clear explanations and displaying the confidence of the system on its predictions. As well as make the AI system’s decision-making process and logic transparent and explainable to patients, so they can understand how the system arrived at its recommendations “Trustworthy AI systems need to include policies that clearly establish who is responsible and accountable for their output. Key factors to consider include which laws and regulations might determine legal liability and whether AI systems are auditable and covered by existing whistleblower laws." AND Ltd. ensures the system is compliant with regulations and ethical standards that apply to the specific industry, this will give non-technical users confidence in the system. “In order for AI to achieve widespread adoption, it must be at least as robust and reliable as the traditional systems, processes, and people it is augmenting or replacing." AND Ltd. allow consistent human oversight and intervention in the decision-making process to ensure ethical and safe outcomes. “To be trustworthy, AI must be protected from cybersecurity risks that might lead to physical and/or digital harm. Although safety and security are clearly important for all computer systems, they are especially crucial for AI due to AI’s large and increasing role and impact on real-world activities." AND Ltd. implements robust security measures to protect the data and the system against potential cyber-attacks, this will give business owners peace of mind that their data is safe. Including all the backend is implemented within AWS and their suggested security measures within data flow and structures.