Information Artificial Intelligence Research Articles

Legality of Credit Agreements with Fiduciary Guarantees made with Artificial Intelligence (AI)

Fiduciary Guarantee before being used as collateral in a credit agreement, it is mandatory to register the fiduciary first. Electronic fiduciary registration still has problems including network disruptions, system errors and is vulnerable to cyber attacks. Smart contract integration with electronic fiduciary guarantee registration can be a solution to these problems. Smart contract as a form of artificial intelligence / Artificial Intelligence (AI). The purpose of this study is to analyze the legality of credit agreements with fiduciary guarantees made with Artificial Intelligence (AI). The type of research used is empirical legal research, supported by primary and secondary data. The results of the research are that Smart contract is a computer program that can make agreements automatically with the blockchain system.AI is generally viewed as a legal object, not a legal subject. This is because AI is a tool or device used by humans to achieve certain goals. The requirements for the validity of an agreement (including a credit agreement with fiduciary guarantee) remain based on Article 1320 of the Civil Code.

The Complexity of Competitive Intelligence in the Age of data ambiguity and Artificial Intelligence

The aim of the study is to explore the development aspects of competitive intelligence (CI) in terms of the challenges posed by the increase in data volume, incl. unverified data, data reliability, and the integration of artificial intelligence (AI) into data analysis processes. The primary research question explores how the role of the human factor has become increasingly important in ensuring the accuracy and reliability of data used by AI, especially in an era dominated by AI and rapid information dissemination. The study highlights the imperative role of human judgment in the era of AI-driven data analysis, highlighting skills, competencies, and authority as critical factors in evaluating data processing outcomes. This points to the risks associated with the uncritical adoption of AI-generated solutions, which can lead to innovative but impractical outcomes that consume significant organizational resources. Furthermore, the study calls for a balanced approach to integrating AI into CI processes, supporting strategies that enhance the synergy between human analytical prowess and AI computational efficiency. This approach is critical to overcoming the challenges posed by data reliability and ensuring the effective implementation of CI strategies that are both innovative and grounded in reality.

Readiness to Embrace Artificial Intelligence Among Medical Students in Saudi Arabia: A National Survey.

Artificial intelligence (AI) is rapidly reshaping healthcare, offering transformative potential for diagnostics, treatment, and patient management. Despite its growing significance, there is limited integration of AI education in medical curricula, raising concerns about the readiness of future healthcare professionals to utilize AI technologies. This study aims to evaluate the readiness of medical students in Saudi Arabia to embrace AI and to assess the current state of AI education, AI Application use, and future perspectives for medical students. a cross-sectional design was employed. It involved medical students from various regions of Saudi Arabia. Data were collected using an anonymous, online, structured, and validated tool from previous studies. The survey included sociodemographic information, details on AI education, the usage of AI applications, intended specialties, and a Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). The data were extracted and revised in an Excel sheet. Statistical analysis was conducted using the IBM SPSS computer program with appropriate statistical tests. This study enrolled 572 medical students, with a mean age of 21.93 years. Most students were Saudi (99.0%), and 43.7% lived in the western region of Saudi Arabia. Most students attended a government medical college (97.41%), and 64.3% of students were in their clinical years. Only 14.5% of the students had received formal AI education, while 34.3% had participated in extracurricular AI training. The mean (SD) MAIRS-MS score was 68.39 (18.3), with higher scores associated with female students, those from the central region, and those with advanced English and computer technology skills (p < 0.001). there is limited AI education and moderate AI readiness among medical students in Saudi colleges, with significant variability in terms of gender, region, and educational background. These findings underscore the need to integrate AI education into medical curricula to better prepare future physicians for AI-enabled healthcare systems.

Analysing public goods games using reinforcement learning: effect of increasing group size on cooperation.

Electricity competition, restrictions on carbon dioxide (CO2) emissions and arm races between nations are examples of social dilemmas within human society. In the presence of social dilemmas, rational choice in game theory leads to the avoidance of cooperative behaviour owing to its cost. However, in experiments using public goods games that simulate social dilemmas, humans have often exhibited cooperative behaviour that deviates from individual rationality. Despite extensive research, the alignment between human cooperative behaviour and game theory predictions remains inconsistent. This study proposes an alternative approach to solve this problem. We used Q-learning, a form of artificial intelligence that mimics decision-making processes of humans who do not possess the rationality assumed in game theory. This study explores the potential for cooperation by varying the number of participants in public goods games using deep Q-learning. The simulations demonstrate that agents with Q-learning can acquire cooperative behaviour similar to that of humans. Moreover, we found that cooperation is more likely to occur as the group size increases. These results support and reinforce existing experiments involving humans. In addition, they have potential applications for creating cooperation without sanctions.

Quality of Chatbot Information Related to Benign Prostatic Hyperplasia.

Large language model (LLM) chatbots, a form of artificial intelligence (AI) that excels at prompt-based interactions and mimics human conversation, have emerged as a tool for providing patients with information about urologic conditions. We aimed to examine the quality of information related to benign prostatic hyperplasia surgery from four chatbots and how they would respond to sample patient messages. We identified the top three queries in Google Trends related to "treatment for enlarged prostate." These were entered into ChatGPT (OpenAI), Bard (Google), Bing AI (Microsoft), and Doximity GPT (Doximity), both unprompted and prompted for specific criteria (optimized). The chatbot-provided answers to each query were evaluated for overall quality by three urologists using the DISCERN instrument. Readability was measured with the built-in Flesch-Kincaid reading level tool in Microsoft Word. To assess the ability of chatbots to answer patient questions, we prompted the chatbots with a clinical scenario related to holmium laser enucleation of the prostate, followed by 10 questions that the National Institutes of Health recommends patients ask before surgery. Accuracy and completeness of responses were graded with Likert scales. Without prompting, the quality of information was moderate across all chatbots but improved significantly with prompting (mean [SD], 3.3 [1.2] vs. 4.4 [0.7] out of 5; p < 0.001). When answering simulated patient messages, the chatbots were accurate (mean [SD], 5.6 [0.4] out of 6) and complete (mean [SD], 2.8 [0.3] out of 3). Additionally, 98% (39/40) had a median score of 5 or higher for accuracy, which corresponds to "nearly all correct." The readability was poor, with a mean (SD) Flesch-Kincaid reading level grade of 12.1 (1.3) (unprompted). LLM chatbots hold promise for patient education, but their effectiveness is limited by the need for careful prompting from the user and by responding at a reading level higher than that of most Americans (grade 8). Educating patients and physicians on optimal LLM interaction is crucial to unlock the full potential of chatbots.

Education in the AI era: a long-term classroom technology based on intelligent robotics

Artificial Intelligence (AI) will have a major social impact in the coming years, affecting today’s professions and our daily routines. In the short-term, education is one of the most impacted areas. The autonomous decision making that can be achieved with tools based on AI implies that some of the traditional methodologies associated with the fundamentals of the learning process in students, must be reviewed. Consequently, the role of teachers in the classroom may change, as they will have to deal with such AI tools performing parts of their work, and with students making a common use of them. In this scope, the AI in Education (AIEd) community agrees on the key relevance of developing AI literacies to train teachers and students of all educational levels in the fundamentals of this new technological discipline, so they can understand how these tools based on AI work and pilot the adaptation in an informed way. This implies teaching students about the fundamentals of topics like perception, representation, reasoning, learning, and the impact of AI, with the aim of delivering a solid formation in this area. To support them, formal teaching and learning resources must be developed and tested with students, properly adapted to different educational levels. The main contribution of this proposal lies in the presentation of the Robobo Project, a technological tool based on intelligent robotics that supports such formal AI literacy training for a wide range of ages, from secondary school to higher education. The core part of this paper is focused on showing the possibilities the Robobo Project offers to teachers in a simple way, and how it can be adapted to different levels and skills, leading to a long-term educational proposal. Validation results that support the feasibility of this technology in the education about AI, obtained with students and teachers in different educational levels during a period of six years, are presented and discussed.

Trends and future directions of artificial intelligence applications in Iranian livestock production systems

Abstract In recent years, the global quest for livestock intensification driven by ever-increasing demands for animal food products raised concerns about animal welfare, environmental sustainability, and public health. Leveraging artificial intelligence (AI) technologies such as remote sensing, Internet of Things (IoT), computer vision, and data-driven modeling has become a hotspot in livestock farming that could facilitate animal monitoring, disease detection, feed optimization, and health management. This review includes an assessment of these topics and research done in Iran so far, proposing future steps for the deployment of AI-powered technologies in farm applications. The Iranian livestock sector already seeing benefits from AI advancements and information technologies, however, most studies focused on model development without applications or deployment for the industry. Significant work is needed to address the limitations and challenges namely lack of data, economic feasibility, ethical concerns, infrastructure issues, and regulatory frameworks. Furthermore, reported AI-based methods and approaches have some inconsistencies in Iran that hinder validation. Looking forward, AI could create a new era in the livestock sector of Iran that not only copes with upcoming challenges but also boosts the circular economy making this country a pioneer in the region. However, tackling some potential limitations accompanying AI application in the Iranian livestock sector warrants the multi-disciplinary collaboration of veterinarians, computer scientists, animal nutritionists, agri-engineers, and governmental organizations.

The Issue of Technosubject through the Lens of “Together-with-Complexity” Thinking

The rapid development of artificial intelligence and digital technologies has inaugurated a transformative epoch, challenging traditional conceptions of subjectivity and cognition. The article examines the concept of technosubjectivity within the framework of complexity thinking and transformational anthropology. Focusing on potential ontological and epistemological shifts stimulated by the recent emergence of new forms of artificial intelligence, particularly generative neural networks and large language models, the authors investigate how these technologies transcend the boundaries between human and machine, generating novel forms of agency and subjectivity. Drawing upon the works of E. Morin, N. Luhmann, G. Simondon, and G. Spencer-Brown, the authors propose a mediative perspective that reveals the post-anthropological interconnectedness and coevolution of human and technological systems. The outlined approach illuminates the dynamic, processual nature of technosubjectivity as an individuating form that emerges through recursive interactions and communicative acts. The article argues that understanding technosubjectivity requires moving beyond simplifying reductionist paradigms and embracing a relational ontology that acknowledges the distributed and emergent properties of cognition and subjectivity in the digital age. The concept of “thinking-together-with” is employed as an alternative to the traditional cybernetic paradigm of control, enabling a reconceptualization of the relationships between humans and artificial intelligence in terms of horizontal collaboration and mutual enrichment. Special attention is paid to the role of distinction and communication in the process of technosubjectivity formation. The article concludes by examining the ethical and philosophical implications of this new form of subjectivity. The authors advocate for more integrated and collaborative relationships between humans and artificial intelligence, rooted in complexity-oriented thinking and mutual co-formation.

Prompt matters: evaluation of large language model chatbot responses related to Peyronie's disease.

Despite direct access to clinicians through the electronic health record, patients are increasingly turning to the internet for information related to their health, especially with sensitive urologic conditions such as Peyronie's disease (PD). Large language model (LLM) chatbots are a form of artificial intelligence that rely on user prompts to mimic conversation, and they have shown remarkable capabilities. The conversational nature of these chatbots has the potential to answer patient questions related to PD; however, the accuracy, comprehensiveness, and readability of these LLMs related to PD remain unknown. To assess the quality and readability of information generated from 4 LLMs with searches related to PD; to see if users could improve responses; and to assess the accuracy, completeness, and readability of responses to artificial preoperative patient questions sent through the electronic health record prior to undergoing PD surgery. The National Institutes of Health's frequently asked questions related to PD were entered into 4 LLMs, unprompted and prompted. The responses were evaluated for overall quality by the previously validated DISCERN questionnaire. Accuracy and completeness of LLM responses to 11 presurgical patient messages were evaluated with previously accepted Likert scales. All evaluations were performed by 3 independent reviewers in October 2023, and all reviews were repeated in April 2024. Descriptive statistics and analysis were performed. Without prompting, the quality of information was moderate across all LLMs but improved to high quality with prompting. LLMs were accurate and complete, with an average score of 5.5 of 6.0 (SD, 0.8) and 2.8 of 3.0 (SD, 0.4), respectively. The average Flesch-Kincaid reading level was grade 12.9 (SD, 2.1). Chatbots were unable to communicate at a grade 8 reading level when prompted, and their citations were appropriate only 42.5% of the time. LLMs may become a valuable tool for patient education for PD, but they currently rely on clinical context and appropriate prompting by humans to be useful. Unfortunately, their prerequisite reading level remains higher than that of the average patient, and their citations cannot be trusted. However, given their increasing uptake and accessibility, patients and physicians should be educated on how to interact with these LLMs to elicit the most appropriate responses. In the future, LLMs may reduce burnout by helping physicians respond to patient messages.

Shaping future practices: German-speaking medical and dental students’ perceptions of artificial intelligence in healthcare

BackgroundThe growing use of artificial intelligence (AI) in healthcare necessitates understanding the perspectives of future practitioners. This study investigated the perceptions of German-speaking medical and dental students regarding the role of artificial intelligence (AI) in their future practices.MethodsA 28-item survey adapted from the AI in Healthcare Education Questionnaire (AIHEQ) and the Medical Student’s Attitude Toward AI in Medicine (MSATAIM) scale was administered to students in Austria, Germany, and Switzerland from April to July 2023. Participants were recruited through targeted advertisements on Facebook and Instagram and were required to be proficient in German and enrolled in medical or dental programs. The data analysis included descriptive statistics, correlations, t tests, and thematic analysis of the open-ended responses.ResultsOf the 409 valid responses (mean age = 23.13 years), only 18.2% of the participants reported receiving formal training in AI. Significant positive correlations were found between self-reported tech-savviness and AI familiarity (r = 0.67) and between confidence in finding reliable AI information and positive attitudes toward AI (r = 0.72). While no significant difference in AI familiarity was found between medical and dental students, dental students exhibited slightly more positive attitudes toward the integration of AI into their future practices.ConclusionThis study underscores the need for comprehensive AI education in medical and dental curricula to address knowledge gaps and prepare future healthcare professionals for the ethical and effective integration of AI in practice.

Is AI my co-author? The ethics of using artificial intelligence in scientific publishing

ABSTRACT The recent emergence of Large Language Models (LLMs) and other forms of Artificial Intelligence (AI) has led people to wonder whether they could act as an author on a scientific paper. This paper argues that AI systems should not be included on the author by-line. We agree with current commentators that LLMs are incapable of taking responsibility for their work and thus do not meet current authorship guidelines. We identify other problems with responsibility and authorship. In addition, the problems go deeper as AI tools also do not write in a meaningful sense nor do they have persistent identities. From a broader publication ethics perspective, adopting AI authorship would have detrimental effects on an already overly competitive and stressed publishing ecosystem. Deterrence is possible as backward-looking tools will likely be able to identify past AI usage. Finally, we question the value of using AI to produce more research simply for publication’s sake.

Exploring NWS Forecasters’ Assessment of AI Guidance Trustworthiness

Abstract As artificial intelligence (AI) methods are increasingly used to develop new guidance intended for operational use by forecasters, it is critical to evaluate whether forecasters deem the guidance trustworthy. Past trust-related AI research suggests that certain attributes (e.g., understanding how the AI was trained, interactivity, and performance) contribute to users perceiving the AI as trustworthy. However, little research has been done to examine the role of these and other attributes for weather forecasters. In this study, we conducted 16 online interviews with National Weather Service (NWS) forecasters to examine (i) how they make guidance use decisions and (ii) how the AI model technique used, training, input variables, performance, and developers as well as interacting with the model output influenced their assessments of trustworthiness of new guidance. The interviews pertained to either a random forest model predicting the probability of severe hail or a 2D convolutional neural network model predicting the probability of storm mode. When taken as a whole, our findings illustrate how forecasters’ assessment of AI guidance trustworthiness is a process that occurs over time rather than automatically or at first introduction. We recommend developers center end users when creating new AI guidance tools, making end users integral to their thinking and efforts. This approach is essential for the development of useful and used tools. The details of these findings can help AI developers understand how forecasters perceive AI guidance and inform AI development and refinement efforts. Significance Statement We used a mixed-methods quantitative and qualitative approach to understand how National Weather Service (NWS) forecasters 1) make guidance use decisions within their operational forecasting process and 2) assess the trustworthiness of prototype guidance developed using artificial intelligence (AI). When taken as a whole, our findings illustrate that forecasters’ assessment of AI guidance trustworthiness is a process that occurs over time rather than automatically and suggest that developers must center the end user when creating new AI guidance tools to ensure that the developed tools are useful and used.

ROBOTIC REVOLUTION: DOES ARTIFICIAL INTELLIGENCE TRIGGER INTERNATIONAL ARMED CONFLICT OR NON- INTERNATIONAL ARMED CONFLICT

The introduction of new forms of artificial intelligence (AI) military weaponry specifically autonomous weapon systems (AWS) can select and engage targets without human intervention therefore the application of lethal AWS incorporation with AI has revolutionized armed conflicts.The main concern regarding the military application of AI is the use of force should be maintained by only human soldiers. There is an urgent need to reinterpret the threshold for triggering an international armed conflict because AI technology unintentionally causes war during border control or surveillance operation. This article predominantly focuses on fully autonomous weapon systems which refer to human agents being removed from certain force applications. The main research questions in this article are first, is it possible that an AWS might, alone, spark an international armed conflict, thus bringing international humanitarian law into force? Second, can the criteria of organisation and intensity that give rise to non-international armed conflicts be met when AWS are controlled by non-state armed actors? This study will examine the research questions by focusing on the main areas of debate in the field of international law on AWS, specifically the compatibility of AI with the principles of humanitarian law, the determination of international responsibility, and ethical problems.

Using artificial intelligence to generate medical literature for urology patients: a comparison of three different large language models

PurposeLarge language models (LLMs) are a form of artificial intelligence (AI) that uses deep learning techniques to understand, summarize and generate content. The potential benefits of LLMs in healthcare is predicted to be immense. The objective of this study was to examine the quality of patient information leaflets (PILs) produced by 3 LLMs on urological topics.MethodsPrompts were created to generate PILs from 3 LLMs: ChatGPT-4, PaLM 2 (Google Bard) and Llama 2 (Meta) across four urology topics (circumcision, nephrectomy, overactive bladder syndrome, and transurethral resection of the prostate). PILs were evaluated using a quality assessment checklist. PIL readability was assessed by the Average Reading Level Consensus Calculator.ResultsPILs generated by PaLM 2 had the highest overall average quality score (3.58), followed by Llama 2 (3.34) and ChatGPT-4 (3.08). PaLM 2 generated PILs were of the highest quality in all topics except TURP and was the only LLM to include images. Medical inaccuracies were present in all generated content including instances of significant error. Readability analysis identified PaLM 2 generated PILs as the simplest (age 14–15 average reading level). Llama 2 PILs were the most difficult (age 16–17 average).ConclusionWhile LLMs can generate PILs that may help reduce healthcare professional workload, generated content requires clinician input for accuracy and inclusion of health literacy aids, such as images. LLM-generated PILs were above the average reading level for adults, necessitating improvement in LLM algorithms and/or prompt design. How satisfied patients are to LLM-generated PILs remains to be evaluated.

Making AI more transparent

The Sarlem project aims to create a new form of artificial intelligence that brings together reinforcement learning and model predictive control. This new form of Artificial Intelligence will combine the ability to learn from experience with a greater degree of transparency over how decisions are reached, as Dr Sebastien Gros explains.

Predicting Reduction Mammaplasty Total Resection Weight With Machine Learning.

Machine learning (ML) is a form of artificial intelligence that has been used to create better predictive models in medicine. Using ML algorithms, we sought to create a predictive model for breast resection weight based on anthropometric measurements. We analyzed 237 patients (474 individual breasts) who underwent reduction mammoplasty at our institution. Anthropometric variables included body surface area (BSA), body mass index, sternal notch-to-nipple (SN-N), and nipple-to-inframammary fold values. Four different ML algorithms (linear regression, ridge regression, support vector regression, and random forest regression) either including or excluding the Schnur Scale prediction for the same data were trained and tested on their ability to recognize the relationship between the anthropometric variables and total resection weights. Resection weight prediction accuracy for each model and the Schnur scale alone were evaluated based on using mean absolute error (MAE). In our cohort, mean age was 40.36 years. Most patients (71.61%) were African American. Mean BSA was 2.0 m 2 , mean body mass index was 33.045 kg/m 2 , mean SN-N was 35.0 cm, and mean nipple-to-inframammary fold was 16.0 cm. Mean SN-N was found to have the greatest variable importance. All 4 models made resection weight predictions with MAE lower than that of the Schnur Scale alone in both the training and testing datasets. Overall, the random forest regression model without Schnur scale weight had the lowest MAE at 186.20. Our ML resection weight prediction model represents an accurate and promising alternative to the Schnur Scale in the setting of reduction mammaplasty consultations.

AI frontiers in oncology: Bridging the gap between humanity and machine.

e13657 Background: Artificial intelligence (AI) is rapidly transforming medical education and how healthcare services are delivered. Oncology healthcare professionals (HCPs) can significantly benefit from the advances of AI including optimized diagnostic capabilities, treatment plans, resource allocation, patient outcomes and much more. The synergy between technology and healthcare is strengthening, heralding opportunities for a revolution in medical education and the broader healthcare industry. We developed an introductory AI workshop and conducted pre- and post-surveys, aiming to inform the design of successive workshops and an AI curriculum, to access knowledge, attitudes and perceived challenges for AI integration into Medical Oncology. Methods: An interactive one-hour workshop was conducted for physicians and faculty of the Hematology and Oncology department. 18 participants, encompassing 6 physicians and 12 other HCPs completed a pre- and post-workshop survey. We evaluated participants' knowledge, opinions, and perceived limitations regarding AI in oncology research, practitioner education, and clinical applications using both open and closed-ended questions, such as multiple-choice and 1-5 Likert scale formats. Results: Survey results included 18 pre-workshop and 17 post-workshop participants, 83% had no formal computer science training: AI knowledge in medicine and awareness of clinical AI applications increased by 100%. Interestingly 24% felt the workshop influenced their view on AI's role in their medical career and better prepared for AI-related challenges. 58% believed AI would assist clinicians within 5 years. However, the belief that AI will play a significant role in their own career did not change after the intervention. Data privacy concerns increased by 25%, while concerns about physician over-reliance on AI decreased by 15%. Notably, 76% were motivated to engage with AI during their future practice after the workshop. Conclusions: The AI initiative significantly enhanced oncology healthcare professionals' understanding and readiness for AI integration, despite most lacking prior formal AI education. A marked increase in AI knowledge and motivation to use AI in practice was noted, although perceptions of AI's impact in their day-to-day work remained unchanged with one workshop, contrasting expert views and predictions. Data privacy concerns rose, but decreased fears of over-reliance on AI indicate a more balanced perspective. These findings stress the need for ongoing AI education in healthcare and underscore the importance of baseline evaluations and feedback for shaping future workshops. Since, AI's integration into society is poised to transform healthcare, an AI curriculum is essential.

Artificial intelligence and machine learning for early cancer prediction and response

Worldwide, cancer claims more lives than any other disease. Although cancer detection, prognosis, and treatment have all advanced, one major obstacle is the lack of personalized, data-driven care. Artificial intelligence (AI), used to forecast and automate numerous malignancies, has emerged as a viable solution for increasing healthcare accuracy and patient outcomes. Artificial intelligence applications in cancer include risk evaluation, early detection, patient prognosis prediction, and treatment selection based on comprehensive data. Machine learning (ML), a form of artificial intelligence that allows computers to learn from training data, is very successful in predicting breast, brain, lung, liver, and prostate cancers. Indeed, AI and machine learning have outperformed physicians in predicting cancer. These technologies can potentially enhance the diagnosis, prediction, and quality of life of patients with a wide range of disorders, not just cancer. As a result, it is critical to enhance existing AI and ML technologies and create new programs to help patients. This article discusses the use of AI and machine learning algorithms in cancer prediction, including its present uses, future possibilities, and limitations.

Accommodating Machine Learning Algorithms in Professional Service Firms

Machine learning algorithms, as one form of artificial intelligence, are significant for professional work because they create the possibility for some predictions, interpretations and judgements that inform decision-making to be made by algorithms. However, little is known about whether it is possible to transform professional work to incorporate machine learning while also addressing negative responses from professionals whose work is changed by inscrutable algorithms. Through original empirical analysis of the effects of machine learning algorithms on the work of accountants and lawyers, this paper identifies the role of accommodating machine learning algorithms in professional service firms. Accommodating machine learning algorithms involves strategic responses that both justify adoption in the context of the possibilities and new contributions of machine learning algorithms and respond to the algorithms’ limitations and opaque and inscrutable nature. The analysis advances understanding of the processes that enable or inhibit the cooperative adoption of artificial intelligence in professional service firms and develops insights relevant when examining the long-term impacts of machine learning algorithms as they become ever more sophisticated.

Status Quo of Artificial Intelligence’s Role in the HRM Operations

Coexistence of technology and business dates back to the late 18th century when the first ever use of a computer was for recording the census by the US government in 1890. The use of technology in business can be ascribed to different organisations in the different nations on the parallel lines of time. It includes invention of cash machines and their use by Barclays in England in the early 1960s, the induction of telephone-based modems for order management by Baxter Pharmaceuticals and use of small desktop computing device called Minitel for processing customer orders in France were the other notable developments in the history of coexistence of technology and business. Increasing operations in the business functions have created an urge for the technological innovation in the industry to handle the operations electronically. Figueiredo and Cohen (2019) say that technology has become an indispensable component of every business function by delivering ease in operations and productivity. The end of the 20th century had witnessed the leaping progress in computing in the form of artificial intelligence (AI) performing the tasks that were unimaginable to comprehend a decade back in time. Developments in the technological research and development prove that organisations have started inducting AI into as many fields as possible at a considerable pace. As a part of the shifting technological dynamics in the industry HR function has also transformed digitally. Tools like enterprise applications have forayed intensely into the operations of human resources management (HRM). These enterprise resource planning (ERP) tools remain to primarily serve the integration of HRM to the other functions. However, enterprise tools could not serve the purpose of supporting decisiveness in the areas of HR planning, workforce design and performance management at large. However, Tuck (2019) argues that AI is assuming increased responsibilities in the different sections of the society and business including the HRM function. At present, the amount of knowledge on the status quo of the role of AI in the HRM functions is scarcely available. Literature related to this disruptive technology in the HR function is still at the nascent stage. This study will examine the role of AI as a key component in the HRM function, which is regarded to be highly human-driven.