Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Manure substitution improves maize yield by promoting soil fertility and mediating the microbial community in lime concretion black soil

Synthetic nitrogen(N)fertilizer has made a great contribution to the improvement of soil fertility and productivity,but excessive application of synthetic N fertilizer may cause agroecosystem risks,such as soil acidification,groundwater contamination and biodiversity reduction.Meanwhile,organic substitution has received increasing attention for its ecologically and environmentally friendly and productivity benefits.However,the linkages between manure substitution,crop yield and the underlying microbial mechanisms remain uncertain.To bridge this gap,a three-year field experiment was conducted with five fertilization regimes:i)Control,non-fertilization;CF,conventional synthetic fertilizer application;CF_(1/2)M_(1/2),1/2 N input via synthetic fertilizer and 1/2 N input via manure;CF_(1/4)M_(3/4),1/4 N input synthetic fertilizer and 3/4 N input via manure;M,manure application.All fertilization treatments were designed to have equal N input.Our results showed that all manure substituted treatments achieved high soil fertility indexes(SFI)and productivities by increasing the soil organic carbon(SOC),total N(TN)and available phosphorus(AP)concentrations,and by altering the bacterial community diversity and composition compared with CF.SOC,AP,and the soil C:N ratio were mainly responsible for microbial community variations.The co-occurrence network revealed that SOC and AP had strong positive associations with Rhodospirillales and Burkholderiales,while TN and C:N ratio had positive and negative associations with Micromonosporaceae,respectively.These specific taxa are implicated in soil macroelement turnover.Random Forest analysis predicted that both biotic(bacterial composition and Micromonosporaceae)and abiotic(AP,SOC,SFI,and TN)factors had significant effects on crop yield.The present work strengthens our understanding of the effects of manure substitution on crop yield and provides theoretical support for optimizing fertilization strategies.

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Production traits and fertility of reciprocal hybrids between Argopecten irradians irradians and A.i.concentricus

The two bay scallop subspecies,Argopecten irradians irradians(NN)and A.i.concentricus(SS),are fast growing and major cultured bivalves in China.However,their relatively small sizes and decreasing production traits caused by long-term inbreeding have been major concerns to the industry in the last two decades.Hybridization between the two bay scallop subspecies may provide a new approach to breed a new variety with superior production traits for the industry.For this end,in this study,we hybridized the two bay scallop subspecies in order to obtain a new strain that incorporates the genes of both subspecies.No significant difference was found in fertilization rate,hatching rate and metamorphosis rate between the purebred and crossbred cohorts(NN♀×SS♂,denoted as NS;SS♀×NN♂,denoted as SN).Both mating strategy(intra-vs.inter-population crosses)and egg origin had significant effects on growth and survival at the larval stage.Heterosis was observed in the crossbred and was more pronounced in older stages.Genetic diversity of the reciprocal hybrids,especially that of SN,was increased compared with the purebred cohorts.Almost all hybrids were completely fertile and able to reproduce by selffertilization or by backcrossing with either parent.Apparently,male sterile individuals whose gonads were fully occupied by the ovary part at mature stage were found in the hybrids for the first time.The hybrids,especially SN,may provide precious germplasm resources for the production of ternary hybrids with the Peruvian scallop,A.purpuratus.

Sperm function, mitochondrial activity and in vivo fertility are associated to their mitochondrial DNA content in pigs

Background Despite their low abundance in sperm, mitochondria have diverse functions in this cell type, includ-ing energy production, signalling and calcium regulation. In humans, sperm mitochondrial DNA content(mtDNAc) has been reported to be negatively linked to sperm function and fertility. Yet, the association between mtDNAc and sperm function in livestock remains unexplored. For this reason, this study aimed to shed some light on the link between mtDNAc and sperm function and fertilising potential in pigs. A qPCR method for mtDNAc quantification was optimised for pig sperm, and the association of this parameter with sperm motility, kinematics, mitochondrial activity, and fertility was subsequently interrogated.Results First, the q PCR method was found to be sensitive and efficient for mtDNAc quantification in pig sperm. By using this technique, mtDNAc was observed to be associated to sperm motility, mitochondrial activity and in vivo, but not in vitro, fertility outcomes. Specifically, sperm with low mtDNAc were seen to exhibit greater motility but decreased mitochondrial activity and intracellular reactive oxygen species. Interestingly, samples with lower mtD-NAc showed higher conception and farrowing rates, but similar in vitro fertilisation rates and embryo development, when compared to those with greater mtDNAc.Conclusions These findings enrich our comprehension of the association of mtDNAc with sperm biology, and lay the foundation for future research into employing this parameter as a molecular predictor for sperm function and fer-tility in livestock.

Subsequent pregnancy outcomes and fertility rates in the case series that underwent bilateral hypogastric artery ligation(BHGAL)due to severe postpartum hemorrhage

Objective:To evaluate the effect of bilateral hypogastric artery ligation(BHGAL)on women's pregnancy outcomes and fertility rates in cases with severe postpartum hemorrhage.Methods:Patients hospitalized in a tertiary care center between 2007 and 2018 were included in the study.The records of cases were obtained from the hospital database,retrospectively.Maternal demographic data,morbidities or complications associated with BHGAL surgery,any additional haemostatic interventional or surgical procedures,preoperative admission and lowest postoperative haematocrit-hemoglobin values,blood products given,type of anaesthesia,intensive care unit(ICU)and length of hospital stay were recorded.In cases without surgical sterilization,future fertility and subsequent pregnancy outcomes were assessed for at least two years after operation.Information was obtained through face-to-face or telephone interviews with patients whose long-term fertility and subsequent pregnancy outcomes were assessed.Results:Sixty-eight patients with BHGAL surgery history were included in the study.26 Of 37 cases(70%)that did not undergo surgical sterilization had a desire for pregnancy after the surgery.Six of these cases(23%)were diagnosed with secondary infertility.The fertility rate of these cases was 77%,and subsequent pregnancy was detected in 20 women.A total of 25 pregnancies,including first and second pregnancies,developed,and 15 of these pregnancies resulted in term delivery,six preterm births,one stillbirth,and three abortions.Conclusions:The postoperative pregnancy rate was found to be higher than the rate of women with infertility problems,and this surgery does not seem to adversely affect pregnancy outcomes.This is a tertiary center experience and cohort studies with large patient series are needed.

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done.

Pro-fertility effect of Ficus carica fruit extract in streptozotocin-induced male rats

Objective:To explore the impact of Ficus carica fruit aqueous extract on fertility parameters in streptozotocin(STZ)-induced male rats.Methods:Twenty-four male Sprague-Dawley rats were divided into four different groups.All groups except a normal control group were induced with 50 mg/kg of streptozotocin(STZ)intravenously to induce diabetes.A positive control group was treated with an antidiabetic drug,metformin(500 mg/kg)whereas a negative control group remained untreated throughout the experiment.Meanwhile,another diabetic rat group received treatment with 400 mg/kg of aqueous Ficus carica fruit extract.Rats in the treatment group were administered Ficus carica fruit aqueous extract daily through forcefeeding via oral gavage for a 21-day period.Assessments included the sperm quality(count,motility and morphology),histology of the testes,serum testosterone and fasting blood glucose(FBG)level.Results:The FBG level of the Ficus carica-treated rats exhibited a significant decrease compared to the negative control group(P<0.05).Sperm quality analysis also indicated that the aqueous Ficus carica extract had significant positive effects on sperm count and motility(P<0.05).The histology of the testes in Ficus caricatreated rats revealed an improved cell arrangement in the germinal cell layer.Furthermore,serum testosterone level showed an increment in the Ficus carica treatment group in comparison to the negative control group.Conclusions:Our findings provide compelling evidence for the profertility and anti-hyperglycemic properties of aqueous Ficus carica fruit extract in diabetic-induced male rats.

A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying capacity.This approach can capture the imprecision and ambiguity often present in human perception.With the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making algorithms.By implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience

Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.

Fertility preservation in patients with gynecologic cancer

In this editorial we comment on the article by Gu et al.We focus and debate the necessity of fertility sparing surgery in young women’s with gynecologic cancers,specifically on those patients with the desire to conceive.This type of individu-alized treatment options is often very difficult,due to the risk of disease evolution and multiple disparities in fertility preservation services among women in di-fferent countries and societies.For this reason national policy interventions are mandatory in order to ensure equitable access this procedures,in women with cancer.

Decision-Making and Management of Self-Care in Persons with Traumatic Spinal Cord Injuries: A Preliminary Study

Patients and physicians understand the importance of self-care following spinal cord injury (SCI), yet many individuals with SCI do not adhere to recommended self-care activities despite logistical supports. Neurobehavioral determinants of SCI self-care behavior, such as impulsivity, are not widely studied, yet understanding them could inform efforts to improve SCI self-care. We explored associations between impulsivity and self-care in an observational study of 35 US adults age 18 - 50 who had traumatic SCI with paraplegia at least six months before assessment. The primary outcome measure was self-reported self-care. In LASSO regression models that included all neurobehavioral measures and demographics as predictors of self-care, dispositional measures of greater impulsivity (negative urgency, lack of premeditation, lack of perseverance), and reduced mindfulness were associated with reduced self-care. Outcome (magnitude) sensitivity, a latent decision-making parameter derived from computationally modeling successive choices in a gambling task, was also associated with self-care behavior. These results are preliminary;more research is needed to demonstrate the utility of these findings in clinical settings. Information about associations between impulsivity and poor self-care in people with SCI could guide the development of interventions to improve SCI self-care and help patients with elevated risks related to self-care and secondary health conditions.

Effect of Different Rates and Mixtures of Solid Household Waste and Faecal Sludge-Based Composts on Soil Fertility and Productivity of Sunflower (Helianthus annuus L.) in Dschang, West Cameroon

The unbalanced and inadequate use of fertilizers is one of the causes of soil degradation. Combined with the ever-increasing population, it is necessary to find sustainable agricultural production alternatives. The present work aims to determine the effect of different rates and mixtutes of organic amendments on soil fertility and the performance of Sunflower (Helianthus annuus L.). In the field, treatments consisted of solid household waste and faecal sludge in the ratios of 3/5 (V1), and a mixture of faecal sludge and household waste in the ratio of 3/5 with 900 worms (V2). At the end of the composting process, V1, V2 composts and the poultry manure (PM) were applied at rates of 4, 5 and 6 t∙ha−1 in a randomized complete block design with three replications. Soil samples were collected before and after the experiment and analyzed. The main results revealed that at the end of the composting process, there was a progressive improvement in the physico-chemical properties of V1 and V2 composts. In particular, the C/N ratio, phosphorus (P) and total nitrogen (TN) initially at 16.49 ± 0.42 (V1, V2), 21.06 ± 0.07 mg∙kg−1 (V1, V2), 0.76% ± 0.08% (V1, V2) respectively, increased after 60 days to 12.40 ± 0.41 (V1), 9.74 ± 0.28 (V2) for C/N, 21.94 ± 0.63 mg∙kg−1 (V1) and 22.04 ± 0.04 mg∙kg−1 (V2) for P, 0.96% ± 0.0% (V1) and 1.22 ± 0.04 (V2) for TN. The application of 6 t∙ha−1of PM had the greatest influence on the diameter and weight of the flower heads (27.16 ± 4.01 t∙ha−1 and 230.83 ± 2.64 t∙ha−1), while 4 t∙ha−1 of V2 gave the tallest sunflower plants (110.07 ± 73.28 cm) as well as the diameter at the crown (19.30 ± 9.07 cm). However, CEC was most influenced by 4 t∙ha−1 of V1, while 4 t∙ha−1 of PM had the greatest effect on organic carbon and phosphorus. However, 5 t∙ha−1 of PM showed the highest sunflower production and yield (1.67 ± 0.21 t∙ha−1). The combination with 900 earthworms is recommended for composting and 5 t∙ha−1 of PM is recommended to obtain a better sunflower production.

Evaluation of Soil Fertility Quality and Environmental Driving Factors in Different Soil Types of Artificial Forests

The aim was to clarify the environmental driving factors of soil fertility indicators in artificial forests of Guangxi and comprehensively evaluate the soil fertility level.By collecting data on the current status of soil in artificial forests,the spatial distribution of major soil fertility indicators was analyzed,and the distribution map of the fertility index of artificial forests in the entire region and the comprehensive fertility index of artificial forests of different soil types were obtained.Canonical correspondence analysis method was used to analyze soil fertility indicators and environmental factors,and the environmental driving factors of soil fertility indicators for artificial forests of the main soil types in Guangxi were obtained.The results showed that over 90%of the soil fertility index of artificial forests in the entire region was between 0.20 and 0.50.The order of soil fertility index of different soil types of artificial forests from high to low was yellow brown soil>yellow red soil>yellow soil>red soil>limestone soil>latosolic red soil>laterite.In artificial forests of latosolic red soil,the correlation between soil alkaline nitrogen and organic matter,annual average temperature was high,while the correlation between soil available phosphorus and organic matter,pH was high,and the correlation between soil available potassium and environmental factors such as slope,altitude,rainfall,accumulated temperature,and slope aspect was high.In artificial forests of red soil,the correlation between soil alkaline nitrogen and slope,altitude was high,while the correlation between soil available phosphorus and accumulated temperature,rainfall was high,and the correlation between soil available potassium and pH was high.In artificial forests of limestone soil,there was a high correlation between soil alkaline nitrogen and slope,organic matter,a high correlation between soil available phosphorus and accumulated temperature,rainfall,and a high correlation between soil available potassium and pH.

Research on Public Engineering Emergency Decision-Making Based on Multi-Granularity Language Information

To effectively deal with fuzzy and uncertain information in public engineering emergencies,an emergency decision-making method based on multi-granularity language information is proposed.Firstly,decision makers select the appropriate language phrase set according to their own situation,give the preference information of the weight of each key indicator,and then transform the multi-granularity language information through consistency.On this basis,the sequential optimization technology of the approximately ideal scheme is introduced to obtain the weight coefficient of each key indicator.Subsequently,the weighted average operator is used to aggregate the preference information of each alternative scheme with the relative importance of decision-makers and the weight of key indicators in sequence,and the comprehensive evaluation value of each scheme is obtained to determine the optimal scheme.Lastly,the effectiveness and practicability of the method are verified by taking the earthwork collapse accident in the construction of a reservoir as an example.

A Blind Spot in the Reframing of a Universe of Possibles: Towards a Suitable Model for Decision-Making Theory and A.I.

Bayesian inference model is an optimal processing of incomplete information that, more than other models, better captures the way in which any decision-maker learns and updates his degree of rational beliefs about possible states of nature, in order to make a better judgment while taking new evidence into account. Such a scientific model proposed for the general theory of decision-making, like all others in general, whether in statistics, economics, operations research, A.I., data science or applied mathematics, regardless of whether they are time-dependent, have in common a theoretical basis that is axiomatized by relying on related concepts of a universe of possibles, especially the so-called universe (or the world), the state of nature (or the state of the world), when formulated explicitly. The issue of where to stand as an observer or a decision-maker to reframe such a universe of possibles together with a partition structure of knowledge (i.e. semantic formalisms), including a copy of itself as it was initially while generalizing it, is not addressed. Memory being the substratum, whether human or artificial, wherein everything stands, to date, even the theoretical possibility of such an operation of self-inclusion is prohibited by pure mathematics. We make this blind spot come to light through a counter-example (namely Archimedes’ Eureka experiment) and explore novel theoretical foundations, fitting better with a quantum form than with fuzzy modeling, to deal with more than a reference universe of possibles. This could open up a new path of investigation for the general theory of decision-making, as well as for Artificial Intelligence, often considered as the science of the imitation of human abilities, while being also the science of knowledge representation and the science of concept formation and reasoning.

Crossing the Achilles Heel of Algorithms:Identifying the Developmental Dilemma of Artificial Intelligence-Assisted Judicial Decision-Making

In the developmental dilemma of artificial intelligence(AI)-assisted judicial decision-making,the technical architecture of AI determines its inherent lack of transparency and interpretability,which is challenging to fundamentally improve.This can be considered a true challenge in the realm of AI-assisted judicial decision-making.By examining the court’s acceptance,integration,and trade-offs of AI technology embedded in the judicial field,the exploration of potential conflicts,interactions,and even mutual shaping between the two will not only reshape their conceptual connotations and intellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of the judicial trial system.