The Importance of AI Governance in the Life Sciences Industry
It is certainly going to be the case that Life Sciences Boards and ExCos will need to increase the frequency of reviews to keep up with the speed of AI-driven workflows.
The ease of analyses of vast amounts of data means that decision-making processes can be accelerated. However, it is crucial that any decisions aided by AI algorithms are accurate, fair, and unbiased.
The Crucial Role of AI in Drug Development and Data Analysis
In drug development, where the stakes are enormous and mistakes can have major repercussions, not only for people but also for society, AI governance is an absolutely essential component. AI is already being used by Pharma & biotech companies for the development of new drugs, the optimization of production processes, and the analysis of dense biological data, but without, in this writers opinion, enough investment in governance.
Building Trust and Understanding in AI Technology for Biotech Boards
Our industry needs to build trust in AI outputs, and in my view Biotech Boards & decision makers are just not that well educated on how the technology works and what its limitations are. In addition, not enough companies have established governance models that ensure biases are identified and removed from AI-led processes.
The Value of Multidisciplinary Teams in Developing Ethical AI Solutions
Too few of the companies that we see have invested in establishing multidisciplinary teams that include experts in areas such as data science, ethics, and governance. We strongly believe that such teams can work together to develop AI algorithms that are transparent, accountable, and fair. They can also regularly review the performance of AI systems to identify and address any biases or errors.
Prioritizing Transparency and Open Communication with Stakeholders
The Life Science industry can mitigate these threats by prioritising open communication with all stakeholders especially regulators, and exercise individual responsibility, and sound moral judgment. Transparency will be key to ensure that their AI systems’ decisions can be understood and challenged if necessary, and so far the challenges have been relatively few.
The Risks of Inadequate AI Adoption and Insufficient Expertise in the Life Sciences Sector
But if our sector is not fully committed to AI, does not establishing clear business goals, or invest in the right expertise, then there is a real danger of bias, errors, and unforeseen consequences.