Assessing the Risks Posed by the Convergence of Artificial Intelligence and Biotechnology

Responsible Development of Emerging Technologies: Extensions and Lessons From Nanotechnology for Worker Protection

OBJECTIVES

This paper identifies approaches to the responsible development of emerging technologies to secure worker safety and health.

METHODS

A retrospective analysis was used to describe the history of the responsible development of worker protection from engineered nanomaterials. Lessons from that history were extended and applied to emerging technologies and illustrated in three examples: advanced manufacturing, synthetic biology, and artificial intelligence.

RESULTS

The same principles used to underpin responsible development of nanotechnology can be applied to emerging technologies. Five criterion actions were identified that embody these principles.

CONCLUSION

Responsible development of emerging technologies requires anticipating hazards and risks and ethical issues attendant to them. Occupational and environment health specialists are often called upon to provide guidance on emerging technologies and the approach described here can serve as a basis for that guidance.

Biosecurity Risk Assessment for the Use of Artificial Intelligence in Synthetic Biology

Background

The integration of Artificial Intelligence (AI) with synthetic biology is driving unprecedented progress in both fields. However, this integration introduces complex biosecurity challenges. Addressing these concerns, this article proposes a specialized biosecurity risk assessment process designed to evaluate the incorporation of AI in synthetic biology.

Methods

A set of tailored tools and methodology was developed for conducting biosecurity risk assessments of AI language models used for synthetic biology. These resources were developed to guide risk management professionals through a systematic process of identifying, evaluating, and mitigating potential risks.

Results

The tools and methodology provided offer a structured approach to risk assessment, enabling risk management professionals to comprehensively analyze the biosecurity implications of AI applications in synthetic biology. They facilitate the identification of potential risks and the development of effective mitigation strategies. An example of a risk assessment performed on the large language model "ChatGPT 4.0" is provided here.

Conclusion

AI's role in synthetic biology is rapidly expanding; thus, establishing proactive and secure practices is crucial. The biosecurity risk assessment tools and methodology presented here are the first provided in the literature and will be instrumental steps toward the responsible integration of AI in synthetic biology. By adopting these resources, the biorisk management community can effectively navigate and manage the biosecurity challenges posed by AI, ensuring its responsible and secure application in the field of synthetic biology.

Expectations for Artificial Intelligence (AI) in Psychiatry

PURPOSE OF REVIEW

Artificial intelligence (AI) is often presented as a transformative technology for clinical medicine even though the current technology maturity of AI is low. The purpose of this narrative review is to describe the complex reasons for the low technology maturity and set realistic expectations for the safe, routine use of AI in clinical medicine.

RECENT FINDINGS

For AI to be productive in clinical medicine, many diverse factors that contribute to the low maturity level need to be addressed. These include technical problems such as data quality, dataset shift, black-box opacity, validation and regulatory challenges, and human factors such as a lack of education in AI, workflow changes, automation bias, and deskilling. There will also be new and unanticipated safety risks with the introduction of AI. The solutions to these issues are complex and will take time to discover, develop, validate, and implement. However, addressing the many problems in a methodical manner will expedite the safe and beneficial use of AI to augment medical decision making in psychiatry.

Optimizing Island Refuges against global Catastrophic and Existential Biological Threats: Priorities and Preparations

Human civilization is vulnerable to global catastrophic biological threats and existential threats. Policy to mitigate the impact of major biological threats should consider worst-case scenarios. We aimed to strengthen existing research on island refuges as a mitigating mechanism against such threats by considering five additional factors as well as recent literature on catastrophic risks and resilience. We also analyzed the performance of potential refuge islands during early phases the COVID-19 pandemic. Using a composite indicator (scored from 0-1) based on 14 global macroindices, we present analysis supporting Australia (0.71), New Zealand (0.64), and Iceland (0.58) as the leading candidate island nation refuges to safeguard the survival of humanity and a flourishing technological civilization from the threat of a catastrophic pandemic. Data from the COVID-19 pandemic supports this finding where islands have performed relatively well. We discuss the persisting weaknesses of even the best candidate refuges and the growing literature describing what preparations such a refuge should ensure to enhance resilience. Refuge preparations by Australia and New Zealand, in particular, may additionally provide some immunity against winter-inducing catastrophes such as global nuclear war. Existing disaster resilience frameworks such as the Sendai framework could be worded to mandate preventive measures against global catastrophic and existential threats. The issue of island refuges against certain global catastrophic risks should be raised at relevant international political summits.

Interpretable detection of novel human viruses from genome sequencing data

Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. Further, we develop a suite of interpretability tools and show that it can be applied also to other models beyond the host prediction task. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect regions of interest in novel agents, for example, the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020. All methods presented here are implemented as easy-to-install packages not only enabling analysis of NGS datasets without requiring any deep learning skills, but also allowing advanced users to easily train and explain new models for genomics.