Unlocking responsible AI prescriptions

Unethical AI and data use in pharmaceuticals can lead to decreased productivity, financial losses, and reputational damage.

It is important to prioritize data privacy, security, integrity, and compliance. Organizations often struggle to initiate ethical data management and promote responsible AI practices within their data science teams.

However, integrating responsible AI is essential. For example, US WorldMeds LLC settled a False Claims Act violation by implementing transparent decision-making, bias mitigation, and robust monitoring.

Launching innovative data science initiatives in a way that follows ethical guidelines is vital to fostering responsible practices and shaping a better AI-driven future.

Empowering responsible AI adoption for pharmaceutical innovation

Our client, a global pharmaceutical leader, aimed to establish responsible AI practices in their data science department and extend them across the entire organization. They wanted to foster a culture that values ethical data science, transparency, trust, and accountability in AI systems while becoming a leader in responsible innovation.

Leveraging our expertise in operationalizing responsible AI work in real situations and our understanding of both AI and the rules for pharmaceutical companies, we provide tailored solutions aligned with ethical and legal standards.

We implemented a comprehensive, step-by-step approach to establish responsible AI guidelines for the client’s data science practices. The goal was to empower the client with knowledge, tools, and guidelines to establish robust ethical data science practices replicable across the organization.

Guiding pharmaceutical data scientists on the ethical care journey.

We developed and deployed Responsible AI Guidance, a platform on the client’s internal knowledge repository that will be an easily accessible resource.

Key features of the Responsible AI Guidance solution include the following:

● Connecting data science decisions to real-world consequences to nudge data scientists to reflect on responsible implications.

● Providing evidence-based recommendations through diverse resources like guidelines, open-source notebooks, research papers, and toolkits.

● Ensuring audit readiness and transparency with a comprehensive machine-learning documentation template covering methods, models, mitigation techniques, and evaluation metrics.

● Embedding privacy and security considerations by integrating the client’s established policies and principles into the platform.

● Tailoring guidelines to meet the particular needs of pharmaceutical data scientists by providing industry-specific context.

This approach ensures responsible AI practices throughout the data science lifecycle, empowering clients to adapt to evolving technologies and regulations while fostering trust and compliance.