Ethical considerations for the use of AI-based analysis of BD for regulating immunity and advancing immunotherapy
| Challenges | Approach |
|---|---|
| Autonomy | Informed consent of the participating subjects should be the cornerstone of data collection, storage, and usage. Enhanced risk of confidentiality breaches should be further emphasized and mitigated. |
| Public engagement at all stages | A diversity of experts and all potential end-users should be actively consulted in the development and implementation of these new analytical tools. Given the open-ended nature of BD, impact assessment should be an ongoing and adaptive process. |
| Equity and managing biases | The AI-based analysis should give adequate weight to all the relevant populations and socio-environmental determinants of immunoregulation to avoid biases and potential injustice. |
| Protect vulnerable populations | The new analytics should strive to create equitable opportunities by researching illnesses that especially affect vulnerable populations and develop treatments that are well-tailored to their means and values. At the same time, these efforts must not add a greater burden on vulnerable populations. |
| Reliability and trust | The AI-generated models and treatments are based on partially opaque processes and offer a limited understanding of the mechanisms involved. Unless high standards in research and care are maintained, this has the potential to hinder reliability and trust toward experts and institutions using AI-assisted analyses and decision-making. |
Strategic recommendations for the use of AI-based analysis of BD for regulating immunity and advancing immunotherapy
| Strategy | Recommendation to respond |
|---|---|
| Streamline BD repositories | Develop guidelines that will streamline current and future immunological data repositories to help workflow and transparency. |
| Establish decision-making strategies for the use of AI-based big omics data analysis | Develop guidelines for decision-making as to how the analysis of “omics” data by AI/BD will use biomarkers for immunotherapy and regulation of immunity. |
| Advanced patient-centric “Precision Medicine” approaches | Develop approaches that are primarily patient-centric and do not depend uniquely on aggregated data from BD sets for AI analysis. |
| Develop strategies to address unforeseen adverse effects | Develop guidelines as to what steps and alternatives should be considered if AI-guided analysis predicts undocumented side effects or fails to predict side effects. |
| Incorporate the role of microbiota-immune cell interaction in immunoregulation | Develop complementary tracks to analyze BD involving interaction between innate and adaptive immune cells with microbiota. |
| Develop strategies to use AI/BD analysis to address knowledge gaps in the regulation of immunity and advancing immunotherapy | Develop strategies to use the analysis of omics and other immunological datasets by AI/BD tools to understand molecular and cellular mechanisms to address knowledge gaps for regulating immunity and immunotherapy in health and disease states. |