The unseen Black faces of AI algorithms

Artificial intelligence-based prediction of pathogen emergence and evolution in the world of synthetic biology

The emergence of new techniques in both microbial biotechnology and artificial intelligence (AI) is opening up a completely new field for monitoring and sometimes even controlling the evolution of pathogens. However, the now famous generative AI extracts and reorganizes prior knowledge from large datasets, making it poorly suited to making predictions in an unreliable future. In contrast, an unfamiliar perspective can help us identify key issues related to the emergence of new technologies, such as those arising from synthetic biology, whilst revisiting old views of AI or including generative AI as a generator of abduction as a resource. This could enable us to identify dangerous situations that are bound to emerge in the not-too-distant future, and prepare ourselves to anticipate when and where they will occur. Here, we emphasize the fact that amongst the many causes of pathogen outbreaks, often driven by the explosion of the human population, laboratory accidents are a major cause of epidemics. This review, limited to animal pathogens, concludes with a discussion of potential epidemic origins based on unusual organisms or associations of organisms that have rarely been highlighted or studied.

Beyond the looking glass Shannon Vallor Oxford University Press, 2024. 272 pp

An ethicist advocates a moral framework for technology.

Ethics for AI in Plastic Surgery: Guidelines and Review

INTRODUCTION

Artificial intelligence (AI) holds the potential to revolutionize medicine, offering vast improvements for plastic surgery. While human physicians are limited to one lifetime of experience, AI is poised to soon surpass human capabilities, as it draws on limitless information and continuous learning abilities. Nevertheless, as AI becomes increasingly prevalent in this domain, it gives rise to critical ethical considerations that must be addressed by professionals.

MATERIALS AND METHODS

This work reviews the literature referring to the ethical challenges brought on by the ever-expanding use of AI in plastic surgery and offers guidelines for its application.

RESULTS

Ethical challenges include the disclosure of use of AI by caregivers, validation of decision-making, data privacy, informed consent and autonomy, potential biases in AI systems, the opaque nature of AI models, questions of liability, and the need for regulations.

CONCLUSIONS

There is a lack of consensus for the ethical use of AI in plastic surgery. Guidelines, such as those presented in this work, are needed within each discipline of medicine to respond to important ethical considerations for the safe use of AI.

LEVEL OF EVIDENCE V

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Ethics of artificial intelligence in prenatal and pediatric genomic medicine

This paper examines the ethics of introducing emerging forms of artificial intelligence (AI) into prenatal and pediatric genomic medicine. Application of genomic AI to these early life settings has not received much attention in the ethics literature. We focus on three contexts: (1) prenatal genomic sequencing for possible fetal abnormalities, (2) rapid genomic sequencing for critically ill children, and (3) reanalysis of genomic data obtained from children for diagnostic purposes. The paper identifies and discusses various ethical issues in the possible application of genomic AI in these settings, especially as they relate to concepts of beneficence, nonmaleficence, respect for autonomy, justice, transparency, accountability, privacy, and trust. The examination will inform the ethically sound introduction of genomic AI in early human life.

Event-related potential correlates of implicit processing of own- and other-race faces in children

Human adults typically experience difficulties in recognizing and discriminating individual faces belonging to racial groups other than their own. The origin of this "other-race" effect is set in infancy, but the understanding of its developmental course is fragmented. We aimed to access the mechanisms of the other-race effect in childhood by unraveling the neural time course of own- and other-race face processing during a masked priming paradigm. White 6- and 7-year-old children (N = 19) categorized fully visible Asian (other-race) or White (own-race) target faces according to gender. Target faces were preceded by masked same-identity or different-identity prime faces, matching the target for race and gender. We showed an early priming effect on the N100 component, with larger amplitude to different-face pairs than to same-face pairs, and a later race effect on the N200 component, with larger amplitude to own-race face pairs than to other-race face pairs. Critically, race did not interact with priming at any processing stage (P100, N100, P200, N200, or P300). Our results suggest that race could have a temporally limited impact on face processing and that the implicit and unconscious identity processing of own- and other-race faces could be similar in 6- and 7-year-olds, depicting an immature other-race effect during childhood.

A Critique of Automated Approaches to Code Facial Expressions: What Do Researchers Need to Know?

Facial expression recognition software is becoming more commonly used by affective scientists to measure facial expressions. Although the use of this software has exciting implications, there are persistent and concerning issues regarding the validity and reliability of these programs. In this paper, we highlight three of these issues: biases of the programs against certain skin colors and genders; the common inability of these programs to capture facial expressions made in non-idealized conditions (e.g., "in the wild"); and programs being forced to adopt the underlying assumptions of the specific theory of emotion on which each software is based. We then discuss three directions for the future of affective science in the area of automated facial coding. First, researchers need to be cognizant of exactly how and on which data sets the machine learning algorithms underlying these programs are being trained. In addition, there are several ethical considerations, such as privacy and data storage, surrounding the use of facial expression recognition programs. Finally, researchers should consider collecting additional emotion data, such as body language, and combine these data with facial expression data in order to achieve a more comprehensive picture of complex human emotions. Facial expression recognition programs are an excellent method of collecting facial expression data, but affective scientists should ensure that they recognize the limitations and ethical implications of these programs.

How AI can distort human beliefs

Models can convey biases and false information to users.