AI Organizations Research Articles

AI-Powered Information Governance: Balancing Automation and Human Oversight for Optimal Organization Productivity

This study employs a mixed-methods approach to examine the optimal balance between AI-powered automation and human oversight in information governance frameworks, aiming to enhance organizational productivity, efficiency, and compliance. Quantitative data collected from 384 respondents were analyzed using Pearson correlation, regression models, and Structural Equation Modeling (SEM). The results reveal strong positive correlations between AI automation levels and both organization size (r = 0.55, p < .01) and AI adoption duration (r = 0.62, p < .01). Regression analysis indicates that higher levels of AI automation significantly improve error reduction (β = 1.12, p < .001) and compliance (β = 1.05, p < .001), especially in larger organizations with longer AI adoption periods. SEM findings highlight that human oversight positively impacts error reduction (β = 0.65, p < .001) and compliance improvement (β = 0.72, p < .001), and the interaction between human oversight and AI automation further enhances these outcomes (error reduction: β = 0.32, p < .001; compliance improvement: β = 0.35, p < .001). The qualitative analysis, involving thematic extraction from industry reports, reveals ethical challenges such as data quality issues, algorithmic bias, and privacy concerns. Hence, it is necessary to integrate human oversight to ensure ethical standards and build stakeholder trust in AI-driven systems. The study concludes with practical recommendations for organizations: establishing transparent AI governance frameworks, investing in continuous training for employees, and regularly auditing AI processes to mitigate risks. By addressing both the technological and ethical dimensions, organizations can implement AI-powered information governance that not only boosts productivity and efficiency but also ensures compliance and ethical integrity.

Human Resource Management in the Transformative Digital Era

Digitalization in human resource management helps companies to modernize HR functions and gives them a competitive edge. At the same time, it requires a change in work style and require changes in demand for HR competencies. Study this aims to introduce the phenomenon of digitalization in literature, explore its current benefits and key risks, and analyze the competencies and role of HR professionals. Innovative strategies that can be applied in human resource management (HRM) to enhance organizational performance in the digital era. With rapid changes in technology and the business environment, organizations need to develop innovative approaches in managing their human resources. This research method is a qualitative research method with a descriptive approach. This research operationally examines various sources of literature relevant to the context of the above research. The data taken, identified in the following order: (1) data collection (2) data sorting (3) data analysis (4) conclusion making. As for data analysis, there is a predetermined sequence in accordance with the empirical steps taken, namely as follows: (1) Examination of data (2) suspected data findings, (3) Data confirmation (4) Diagnosis, (5) Action. The results obtained show that HR professionals tend to be a little reluctant to adopt technology the. The results of this research confirm the importance of digitalization in recent years for human resources and increasing demand for digital skills. Every organization should develop its digital strategy to improve productivity. In the present scenario, DHRM is considered a more significant and growing research topic as well as an important topic for a business organization. With the use of DHR practice and through social media, the internet, AI, and other technology organization can maintain their performance and employees' quality standard for the smooth running of the organization. In exploring "Strategic Human Resource Planning in the Era of Digital Transformation" through the Systematic Literature Review method, this research reveals important findings. Technology integration, especially through artificial intelligence, big data analysis and technology platforms, is proven to be a deep foundation in responsive and adaptive HR planning in the era of digital transformation. The success of this planning also depends heavily on the involvement of key stakeholders, particularly upper management and business units, who contribute to the identification of skills relevant to technological change.

DETEK-AI: A Web-based Deepfake Detection System

AI-driven deepfake bots pose a serious risk to society, security, and trust, which necessitates the development of reliable techniques to detect and mitigate their threat, as their proliferation leads to disinformation, deceit, and harm. Deepfake detection systems can improve cybersecurity, safeguard digital identities, mitigate misinformation, protect reputations of individuals and organizations, assure ethical use of AI technology, empower people and organizations, support regulatory efforts, and contribute to a safer digital ecosystem. Detek AI is a frontrunner in innovative technology aimed at combating deepfake content, safeguarding individuals and organizations from misinformation, and upholding the integrity of online platforms. Keyword— Deepfake Technology, Deepfake Detection, Convolutional Neural Networks, Artificial Intelligence bots, Machine Learning Artificial Intelligence, Face Morphing Detection, Deep Learning, Benford’s Law.

Minds and machines: A look at employee attitudes and work engagement regarding AI in large organizations

This study investigates how workers in huge firms view and feel about AI innovation. Utilizing Prolific.co, a particular group of workers with first-hand information using AI innovation was employed. The study posed inquiries about the adequacy of AI, its benefits and drawbacks, client acknowledgment, morals, straight forwardness and individual strengthening. Specialized staff (40%) and the executive’s staff (60%) made up the fragments of respondents. For the various decision questions, a measurable examination was finished, and for the genuine inquiries, a topical investigation. The discoveries show a huge level of understanding between the perspectives and assessments of the two worker divisions, particularly as to AI execution and fulfilment levels with morals, straightforwardness, and conference. Discernments and mentalities with respect to AI innovation were demonstrated to be essentially emphatically affected by segment factors; the effect of calling type and key utilitarian areas of AI organization was viewed as less huge. Generally speaking, the review accentuates that it is so basic to consider socioeconomics while analyzing perspectives and conclusions in regards to AI innovation in corporate settings.

AI ethics and its pitfalls: not living up to its own standards?

AI ethics is deemed to be an essential ingredient in the quest for trustworthy AI. Hence, demands for implementing AI ethics and ethicists into AI organizations, especially corporations, are ubiquitous. However, the assumption that AI ethicists have particular epistemological advantages compared to non-ethicists as well as the idea that AI ethics automatically decreases the likelihood of unethical outcomes are both flawed. Therefore, this comment lists risks that either originate from AI ethicists themselves or from the consequences their embedding in AI organizations has. The compilation of risks comprises psychological considerations concerning the cognitive biases of AI ethicists themselves as well as biased reactions to their work, subject-specific and knowledge constraints AI ethicists often succumb to, negative side effects of ethics audits for AI applications, and many more. Ultimately, the aim of this comment is not to diminish or deny the importance of the discipline of AI ethics, but rather to increase its capacities for self-reflection and, ultimately, effectiveness.

The hope & wellness screening toolkit: developing a community based suicide and substance use screening program for American Indians and Alaska Natives

ABSTRACT Suicide is a leading cause of death in the U.S. In American Indian/Alaska Native (AI/AN) urban communities and rural reservation communities, youth experience high rates of suicidal ideation, attempts, and deaths. An urban AI organization partnered with a university to respond to this crisis with SAMHSA funds that supported the development and implementation of youth suicide prevention and intervention programming through screening, identification, and treatment of youth. They engaged the community in developing suicide prevention and intervention programming, and designed and tested procedures for implementing community-based suicide screenings, which led to the creation of the Hope & Wellness Screening Toolkit.

Evaluation of bull fertility in Italian Brown Swiss dairy cattle using cow field data

Dairy bull fertility is traditionally evaluated using semen production and quality traits; however, these attributes explain only part of the differences observed in fertility among bulls. Alternatively, bull fertility can be directly evaluated using cow field data. The main objective of this study was to investigate bull fertility in the Italian Brown Swiss dairy cattle population using confirmed pregnancy records. The data set included a total of 397,926 breeding records from 1,228 bulls and 129,858 lactating cows between first and fifth lactation from 2000 to 2019. We first evaluated cow pregnancy success, including factors related to the bull under evaluation, such as bull age, bull inbreeding, and AI organization, and factors associated with the cow that receives the dose of semen, including herd-year-season, cow age, parity, and milk yield. We then estimated sire conception rate using only factors related to the bull. Model predictive ability was evaluated using 10-fold cross-validation with 10 replicates. Interestingly, our analyses revealed that there is a substantial variation in conception rate among Brown Swiss bulls, with more than 20% conception rate difference between high-fertility and low-fertility bulls. We also showed that the prediction of bull fertility is feasible as our cross-validation analyses achieved predictive correlations equal to 0.30 for sire conception rate. Improving reproduction performance is one of the major challenges of the dairy industry worldwide, and for this, it is essential to have accurate predictions of service sire fertility. This study represents the foundation for the development of novel tools that will allow dairy producers, breeders, and artificial insemination companies to make enhanced management and selection decisions on Brown Swiss male fertility.

Influences on thermo-resistance of boar spermatozoa

The aim of this study was to investigate the influence of semen production management in 25 European boar studs on thermo-resistance (TRT) of boar spermatozoa. TRT is an important semen characteristic, easy to determine, and is used to explain variations in pig fertility. During a study period from 2013 to 2018, 905 ejaculates were analyzed for TRT on day 7 of semen storage. Statistical models showed that around 40% of the total variability in TRT could be explained by production management factors. Significant selected predictors were year, month, boar stud, boar age, dilution steps, mitochondrial activity and membrane integrity of spermatozoa, bacterial contamination, arrival temperature of ejaculates in the analysis laboratory, sperm concentration, ejaculate volume, sperm output, dose/ejaculate, and electrical conductivity of pure water for extender preparation. TRT increased during the study period. No effects were observed for breed, dilution rate, morphologically intact spermatozoa, and refractive index of extender on TRT. A holistic view of the requirements in the field of boar semen production is of great importance for future developments of individualized Hazard Analysis and Critical Control Point concepts, prognosis models for boar semen quality, and could help to improve the efficiency of AI organizations.

A mating method accounting for inbreeding and multi-trait selection in dairy cattle populations

Selection in dairy cattle populations usually takes into account both the breed profiles for many traits and their overall estimated breeding values (EBV). This can result in effective contributions of breeding animals departing substantially from contributions optimised for saving future genetic variability. In this work, we propose a mating method that considers not only inbreeding but also the detailed EBV of progeny or the EBV of sires in reference to acceptance thresholds. Penalties were defined for inbreeding and for inadequate EBV profiles. Relative reductions of penalties yielded by any mating design were expressed on a scale ranging from 0 to 1. A value of 0 represented the average performance of random matings and a value of 1 represented the maximal reduction allowed by a specialized, single-penalty, mating design. The core of the method was an adaptative simulated annealing, where the maximized function was the average of both ratios, under the constraints that both relative penalty reductions should be equal and that the within-herd concentration criterion should be equal to a predefined reasonable value. The method was tested on two French dairy cattle populations originating from the same AI organization. The optimised mating design allowed substantial reductions of penalty: 70% and 64% for the Holstein and the Normandy populations, respectively. Thus, this mating method decreased inbreeding and met various demands from breeders.

SIGART on AAAI's Founding: The Chairman's Message, 1980

This article reprints a section of the January 1980 Chairman's Message of the SIGART Newsletter (No. 69). SIGART is the Special Interest Group on Artificial Intelligence, of the Association for Computing Machinery. At the time of AAAI's formation, SIGART, with its 3,800 members, was the principal AI organization in the United States, and its primary activity was publishing the Newsletter.

Genetic Parameters and Trends of Milk, Fat, Days Open, and Body Weight After Calving in North Carolina Experimental Herds

The main objective of this study was to estimate genetic trends for 3.7% FCM, fat yield, days open, and predicted body weight after calving in six experimental dairy herds owned by the State Farm Division of the North Carolina Department of Agriculture. Body weights were predicted from heart girths measured at or before the first test day after calving. Data analyzed were 23,052 records from 8575 cows, daughters of 681 bulls. Heritabilities and breeding values were estimated using the multiple-trait, derivative-free REML programs and a single-trait repeatability model. Breeding values of cows were averaged by and regressed on birth year to estimate genetic trends. Genetic correlations between traits were estimated by correlating breeding values. Estimates of heritability were 0.25 for 3.7% FCM, 0.28 for fat yield, 0.03 for days open, and 0.17 for predicted body weight. Unfavorable genetic relationships were found between yields and days open and between yields and body weight. Genetically, cows that were heavier after calving produced less milk and fat but conceived earlier than smaller cows. Genetic changes in yields and days open were greater for cows born after 1970, but the greatest genetic changes were after 1980 (FCM, 94.7kg/yr; fat yield, 3.46kg/yr; days open, 1.1 d/yr). Breeding values for body weight increased for cows born from 1950 to 1970, decreased until 1980, and increased for later parities. The results of our study suggest that AI organizations may need to include fertility traits in progeny testing and relax the emphasis on increased body weight.

Influences of Progeny Test Programs on Genetic Evaluations of Young Sires

Our purpose was to examine differences among AI progeny test programs in their effectiveness in identifying elite young sires for milk and protein. Data were from 6238 Holstein sires in five animal model evaluations of the USDA from January 1995 to February 1997. Bulls were required to be ≤8 yr of age and to have ≥10 daughters in production at the time of the evaluation. Bulls in AI programs were placed in nine groups based on affiliation with major AI organizations; from these nine groups, bulls with daughters averaging <150 DIM and bulls sampled in >100 herds were placed in two separate groups. Bulls sampled in organizations with controller numbers ≥30 and bulls that had no connection to a sampling organization formed two additional groups. We also classified bulls by sampling codes of the National Association of Animal Breeders. A model predicting daughter yield deviation included effects of organization or sampling code, parent average (free of progeny information), and the interaction of organization or sampling method and parent average. When all data were used, a common intercept for milk was appropriate for all sampling methods, but the slopes differed (R2 = 0.44). Neither a common intercept nor a common slope was appropriate for protein. When data were restricted to the nine major organizations, a common intercept and slope were appropriate for milk, and R2 decreased to 0.14. A common intercept and slope were found for protein, and R2 decreased to 0.15. We detected no important differences in response to pedigree selection among progeny-testing methods used by major organizations that provide semen, but a difference was detected among the sampling codes of the National Association of Animal Breeders.

Usability for Genetic Evaluations of Records from Herds Participating in Progeny Test Programs of Artificial Insemination Organizations

Thirteen AI organizations provided identification of herds that participated in their progeny test programs in 1989 and 1990; 15% of those herds participated in programs of more than one AI organization, but only 2.6% participated in programs of more than two AI organizations. Of the 19,589 participating herds, 82 and 76% were enrolled in DHI test plans that were considered to be usable for genetic evaluations during 1991 and 1992. For herds that had participated in AI progeny test programs, mean percentages of usable records were 77% in 1991 and 78% in 1992; the mean percentages of usable records for nonparticipating herds were 62% in 1991 and 60% in 1992. Participating herds had larger mean herd sizes, higher means and standard deviations of milk yields, younger cows, and a lower percentage of registered cows than did nonparticipating herds. Analysis of variance was used to explain the variation in the percentage of records that were usable for genetic evaluations. Herds that participated in AI progeny test programs or that had smaller herd sizes, higher mean milk yields, younger cows, or larger percentages of registered cows had higher percentages of records that were usable for genetic evaluations. Improved usability of records for genetic evaluations would increase the efficiency of AI progeny testing, and consideration of herd characteristics associated with higher percentages of usable records should aid AI organizations in evaluating prospective herds for progeny test programs.

Review: Dairy Cattle Reproductive Physiology Research and Management—Past Progress and Future Prospects

Artificial insemination developed as the solution for two important problems in the dairy cattle industry during the past 50 yr: 1) the need for genetic improvement and 2) the elimination of costly venereal diseases. Cooperation among researchers, extension workers, veterinarians, dairy producers, and emerging AI organizations in pooling their expertise, was instrumental to the remarkably rapid development of AI. The cooperation of universities, government, and producers to fund teams of reproductive specialists to collaborate and transfer findings quickly to potential users was a major component of this successful venture. Money invested in these experiments was estimated to have returned about $100 for each $1 invested. Successful freezing of sperm led to the development of the field of cryobiology, and AI paved the way for embryo transfer. The development of ultrasound equipment; various types of rapid hormone assays; prostaglandins, progestogens, and GnRH; and computerization made possible various alternative management plans for controlling reproduction. Multidisciplinary, multigeographical teams that gather basic needed information have the potential for making excellent progress. As herd size increases, new programs for efficient reproductive management and for identifying needed research through computer modeling are a must. Sexed embryos from elite cows and bulls will be used selectively. When embryonic stem cell technology becomes practical, it will revolutionize cattle breeding.

A Model for Reproductive Efficiency of Dairy Bulls

Reproductive efficiency of bulls is usually measured by nonreturn rate, which is commonly defined as the proportion of cows that were inseminated and did not return for another service within a specified number of days. The AI organizations use nonreturn rate to evaluate fertility of a bull or performance of a technician. Measures derived from nonreturn rate, such as conception rate and calving rate, might be more reliable for evaluation than nonreturn rate itself. Estimated conception rate is a better early measure of efficiency than nonreturn rate, because conception rate depends on the population of spermatozoa at insemination and not on developmental potential of the conceptus after insemination. A mathematical function is presented to model reproductive efficiency of bulls by estimation of the probability of conception at time of insemination (conception rate) and the probability of completing gestation after insemination (calving rate) through the relationship of nonreturn rate to the concentration of spermatozoa at insemination and the time after insemination. The model is illustrated with three bulls, using nonreturn rates by 28, 56, and 84 d after insemination.

Genetic Prediction for Calving Ease in the United States: Data, Models, and Use by the Dairy Industry

Dystocia of Holsteins has a major economic impact on the dairy cattle industry. Genetic evaluation of AI sires for calving ease is sponsored by the National Association of Animal Breeders. The AI organizations and dairy records processing centers collect data from herds that have progeny of AI bulls. Genetic predictions of merit for calving ease have been available for Holstein AI bulls in the US since 1978. A threshold model was implemented during 1988. The threshold model adjusts for the effects of herd-year, season, sex of calf, parity, and birth-year group of sire to predict the genetic merit of each sire for calving ease. Pedigree information from sires, sons, and grandsons is also included in the analysis to improve the reliability of genetic predictions for bulls that have pedigree data available. Each sire's genetic prediction for calving ease is reported as the expected percentage of difficult births of primiparous cows. National in scope, genetic predictions for calving ease are used internationally by breeders of dairy cattle to minimize the risk of difficult births of primiparous cows and to increase the use of AI for young cows.

Effect of Sampling Status and Adjustment for Heterogeneous Variance on Bias in Bull Evaluations

Holstein bulls were assigned to sampling categories (AI stud, AI nonstud, or non-AI) based on bull code, controller number, and age at semen distribution. The AI stud bulls were sampled through traditional progeny-testing programs of 13 AI organizations; AI nonstud bulls had AI semen collection reported by another organization or by multiple organizations. The non-AI bulls had no reported AI semen collection. Actual daughter yield deviations for these three groups of bulls were compared with expected performance (parent average) to provide an indication of whether evaluations were free from bias for daughter yield deviations. Mean difference of daughter yield deviation from parent average was close to 0kg for animal model evaluations of all 22,930 bulls but was positively biased by 46kg of milk for AI nonstud bulls. Mean PTA and reliabilities for parents were highest for AI stud bulls and lowest for non-AI bulls. The AI stud bulls varied least and were intermediate for mean management, approximated as mean daughter yield minus bull ETA. Management was highest for AI nonstud bulls, which suggested that adjustment for heterogeneous variance might reduce bias. However, the effect of this adjustment on mean difference of daughter yield deviation from parent average was small.

Correlation of the α-Lactalbumin (+15) Polymorphism to Milk Production and Milk Composition of Holsteins

The α-lactalbumin (+15) polymorphism (a single base variation 15 basepairs 3′ of the α-lactalbumin transcription start point) was examined for its usefulness as a genetic marker for Holsteins. The +15 polymorphism is located in a region of the gene that is potentially involved in the regulation of α-lactalbumin gene expression. Animals from two dairy herds and young sires from progeny-testing programs of four AI organizations were used in the analysis. A group of sons from a heterozygous sire were also evaluated. Each individual animal was genotyped at the α-lactalbumin (+15) locus, and differences of genotypes were investigated. Estimated differences among alleles were calculated for PTA for milk, kilograms of protein, protein percentage, protein dollars, kilograms of fat, fat percentage, and fat dollars. Animals having the α-lactalbumin (+15) AA (an adenine on both alleles at position +15) genotype had statistically higher PTA for milk, kilograms of protein, protein dollars, kilograms of fat, and fat dollars than did the α-lactalbumin (+15) BB (a cytosine, guanine, or thymine on both alleles at position +15) animals. The α-lactalbumin (+15) BB animals had higher protein and fat percentages than the α-lactalbumin (+15) AA animals. Animals that were heterozygous at this locus, α-lactalbumin (+15) AB, had intermediate values for all traits analyzed. These results indicate a potential marker or actual locus effect of the α-lactalbumin (+15) polymorphism in Holstein cattle.

Evaluation of Bulls for Nonreturn Rates Within Artificial Insemination Organizations

Programs have been written for use on microcomputers to utilize breeding receipt data collected by AI organizations to evaluate dairy bulls for nonreturn rates. The statistical model of analysis allows the user to have up to four fixed factors, as well as herd or herd-year effects, technician effects, and service sire effects. As an example, 137,874 AI in 1991 from one organization were analyzed. Data included information from 3609 herds, 80 technicians, and 464 sires. Although AI organizations traditionally compute 60- to 90-d nonreturn rates, these programs have caused organizations to consider using shorter period nonreturn rates in order to evaluate bulls sooner. Evidence from other work indicated that the evaluations of bulls from this analysis were more highly correlated with physiological characteristics of ejaculates than simple nonreturn rates.

National genetic improvement programs for dairy cattle in the United States2

Rate of genetic improvement for milk yield has been increasing in recent years. Cows born in 1986 were about 135 kg superior in breeding value for milk yield to those born in 1985. Over 2.2 million cows contribute new data to genetic evaluations for production traits annually. These evaluations are computed with an animal model that provides best linear unbiased predictions of transmitting abilities for milk, fat, and protein yields and fat and protein percentages. The model includes effects of management group, permanent environment, herd-sire interaction, and animal genetic merit. Unknown-parent groups represent the genetic merit of base populations defined by birth year and sex. Type appraisal data are collected by breed associations and are evaluated with a sire model. Holstein cow evaluations are computed using scores from all appraisals and a multitrait model; evaluations for other breeds are computed using all appraisal scores, a repeatability model, and a single-trait system. Dystocia data are collected by individual AI organizations and dairy records processing centers; they are analyzed by a categorical-trait sire model at Iowa State University with support from the National Association of Animal Breeders. The AI organizations have been extremely important in increasing rate of genetic progress by increasing numbers of young bulls sampled, increasing selection intensity of bull dams through multiple ovulation and embryo transfer, and shortening generation interval through the use of younger cows and some virgin heifers as bull dams. (ABSTRACT TRUNCATED AT 250 WORDS)