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Practicing Responsible Artificial Intelligence (AI)
Democratization of technology and the pandemic have fueled adoption of AI/ML technologies across the public sector. Several public health agencies have leveraged AI/ML technologies to harness the power of data driven intelligence to transform several aspects of community healthcare including the identification of vulnerable populations, patient engagement, optimization of care quality, delivery of personalized interventions, and elimination of fraudulent transactions.
While these AI-enabled initiatives have generated new insights and enabled the agencies to improve outcomes, they have also raised concerns regarding the ethical principles and values in AI/ML adoption. There is a renewed focus on ensuring trust, fairness, privacy, accountability, and transparency throughout experimentation to industrialization of AI initiatives.
Governance is a critical aspect of AI/ML adoption. Unfortunately, it hasn’t kept pace with the advancement and rapid adoption of AI/ML technologies. It is important to develop explainable and interpretable AI. It is even more important to practice AI responsibly.
Before scaling AI/ML initiatives, organizations must establish a pre- and post-mortem process to ensure that AI is used responsibly. The predictions from AI/ML models must be fair so that the derived insights, and consequently the actions, do not lead to any unintended consequences related to safety, equity, integrity, fairness, and social justice for individuals and communities that they are designed to help.
From changing regulations to increasing constituent trust and cost of building AI/ML models, data scientists, agency leaders, and policy makers need to understand the challenges, opportunities, and risks associated with AI.
We will be discussing this in detail at the 2022 Health Datapalooza and National Health Policy Conference on April 4, 2022 in Washington DC. Our panel, comprising of experts from the government and the academia, will discuss real-world examples and best practices involved in designing, delivering, and operating AI models based on a human-centric approach.
The panel will also outline a governance framework built on people, process, technology and environment pillars to help organizations institutionalize right controls and processes to plan, implement, and continuously monitor AI systems, in alignment with the commitments to ensure socio-cultural and ethnic diversity.
Organizations that are able to practice AI responsibly minimize risk, enhance efficiency, and serve their constituents more effectively while maximizing positive impact.
Author Details
Dr. Suman De, Principal Consultant and Head, Government Healthcare Analytics Solutions, Infosys Public Services
Dr. Suman is head of government healthcare analytics for Infosys Public Services. He has extensive experience in the public healthcare sector and previously worked for the World Health Organization, UNICEF and the Indian Public Health Association.
At Infosys, Dr. Suman leads the areas of vaccine management, advanced data science and artificial intelligence-enabled population health, social determinants of health analytics, opioid management, care management, and value-based care. He is a frequent public speaker at various healthcare conferences, forums and at major universities, including the Massachusetts Institute of Technology.
Dr. Suman is based in Hartford, Connecticut. He holds a medical degree from the University of Calcutta and master’s degree in healthcare administration from the Tata Institute of Social Sciences in Mumbai, India.
