paraphrase this text as one paraphrase:

Question

In the context of the combined use of AI/ML and genomics, the following
framework for criteria may, for example, be used as a screening process
to identify scientific and technological uncertainty, and most importantly,
their impact on human life and health to decide whether to apply the
precautionary principle:
• risks and harms posed to public and/or individual health and life
and physical integrity
• degree and type of scientific and technological uncertainty
• presence or absence of morally acceptable harm
• impact of genomic secondary finding disclosure on fundamental
rights of individuals and/or community
• reliability, accuracy, and bias of AI/ML-enabled genomic predictions
and predictive recommendations
• benefit to individuals and/or society
• scientific and technological doubts about quality, accuracy,
applicability, and transparency of data
• power asymmetries
• general violation of fundamental human rights
• novel, unintended, unforeseeable, unprecedented, or adverse impacts
• clear violation of risk-based concentration thresholds or standards
• scientifically and technological founded doubts on theory, model
sufficiency, or applicability
• divergent individual or institutional perceptions of risk
• ethical, legal and social concerns, distributional issues or political
mobilisation

Answer 1

In the realm of utilizing AI/ML alongside genomics, a framework can be established to evaluate criteria for screening purposes, focusing on scientific and technological uncertainties and their potential effects on human life and health, which helps in determining the application of the precautionary principle. This framework includes considerations such as risks to public or individual health, the extent and nature of uncertainties surrounding science and technology, the moral acceptability of potential harm, the consequences of disclosing secondary genomic findings on individual and community rights, the reliability and bias of AI/ML-driven genomic predictions, the benefits for individuals or society, uncertainties regarding data quality and transparency, power imbalances, violations of fundamental human rights, unexpected adverse effects, breaches of established risk thresholds, scientifically-founded doubts about the validity of theories or models, varying perceptions of risk, and ethical, legal, social, and political concerns regarding distribution and mobilization.