Abstract Artificial Intelligence Research Articles

Abstract PO2-09-04: Changes in an artificial intelligence model score for breast cancer detection (Transpara) with tamoxifen and aromatase inhibitor treatment

Abstract Artificial intelligence (AI) models applied to mammography have been shown to improve breast cancer (BC) detection and risk estimation. The Transpara AI detection system has been associated with short and longer-term risk of invasive BC. Whether Transpara can identify changes in the breast associated with tamoxifen (TAM) and aromatase inhibitor (AIs) treatment is unknown. We compare changes in the Transpara malignancy score in the unaffected breast of BC cases using TAM or AIs to those of untreated women without cancer. Cases and controls were sampled from the Mayo Clinic Rochester Breast Screening Practice. Eligible cases were women with breast cancer treated with TAM and/or AIs for at least 8 months who had full field digital (FFDM) mammograms within two years prior to start of treatment (pretreatment mammogram). Controls did not have BC, were not using TAM, AIs, or postmenopausal hormones, and had at least two FFDM screening mammograms. Transpara scores (1-10) were calculated for the contralateral breast in cases and a randomly selected side for controls. Two controls were matched to each case on menopausal status at both mammograms, time between mammograms (< 6 months) and baseline Transpara score (±2 units). Conditional logistic regression was used to assess the association of treatment (case (TAM/AIs) vs. control) with change in Transpara score between the pretreatment mammogram and the mammogram taken closest to 13 months later. Change was classified as having a decrease in Transpara score between the two mammograms vs. no change or an increase. Models were adjusted for age and Transpara score at pretreatment mammogram, and time between the two mammograms. We performed analyses on all women combined and stratified by time on treatment, menopausal status, and treatment type (TAM vs. AIs). A total of 134 cases were identified, with 53 on TAM (21 premenopausal and 32 postmenopausal) and 81 on AIs (postmenopausal); 268 controls were matched to the cases. Characteristics of the study population are summarized (Table). Most case mammograms were obtained within six months prior to treatment start [median 2.63 months (interquartile range (IQR), 1.62, 5.72)] and median time on TAM/AIs was 10.9 months (IQR, 8.03, 16.6) at time of second mammogram. The median absolute change in Transpara score between the two mammograms was similar in cases [0 (IQR, -2, 1)] vs. controls [0 (IQR, -1.25, 1)]; 40.3% of cases and 39.2% of controls had any decrease. Conditional logistic models found no evidence for an association between TAM/AIs and decrease in Transpara score [odds ratio (OR) = 1.07; 95% confidence interval (CI), 0.65, 1.78]. When stratified by median time on treatment (10.9 months), there was suggestion of a stronger association for those on treatment for a longer duration (OR = 1.21; 95% CI, 0.61, 2.40 for >10.9 months vs. OR = 0.96; 95% CI, 0.43, 2.13 for < 10.9 months) but these results were not statistically significant. Similarly, odds ratios were stronger in postmenopausal (OR = 1.18; 95% CI, 0.67, 2.07) compared to premenopausal (OR = 0.91; 95% CI, 0.24, 3.45) women, and for AIs (OR = 1.21, 95% CI, 0.64, 2.29) vs. TAM (OR = 0.93; 95% CI, 0.39, 2.22) but none of these results reached statistical significance. In summary, we found no association between treatment with TAM/AIs and change in Transpara score. However, our study had limited power. Also, longer time on treatment as well as investigation of AI-based scores trained directly to assess BC risk may be necessary to identify relevant changes. Citation Format: Yvonne Wang, Christopher Scott, Matthew Jensen, Dan Hursh, Kathleen Brandt, Aaron Norman, Fang Fang Wu, Stacey Winham, Karla Kerlikowske, Celine Vachon. Changes in an artificial intelligence model score for breast cancer detection (Transpara) with tamoxifen and aromatase inhibitor treatment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-09-04.

Abstract PS10-06: Contribution of a breast cancer polygenic risk score to mammography artificial intelligence models and breast density for long term breast cancer risk prediction

Abstract Artificial intelligence (AI) models based on deep learning of mammogram images have been developed for lesion detection and diagnosis as well as breast cancer (BC) risk. We and others have shown that these AI models, coupled with breast density, predict long-term breast cancer risk. Common germline genetic variation, in particular a polygenic risk score (PRS) that captures the cumulative impact of hundreds of common risk variants, has been shown associated with 3-fold increased BC risk. The BC-PRS may complement imaging risk factors to improve identification of women at high BC risk and better target prevention strategies. We evaluated the contribution of a BC-PRS to two imaging-based AI models (Transpara AI cancer detection system and Mirai 5-year risk) and breast density measures to long-term BC risk. We hypothesized that the BC-PRS will improve long-term risk prediction above AI and density measures. We examined this hypothesis within a cohort of 10,271 women enrolled to the Mayo Clinic Biobank between 2009 and 2015. Eligible women had no prior cancer, were ages 35 to 85, had a full-field digital screening mammogram within two years of enrollment and genotyping by Regeneron. Incident invasive and DCIS BC was identified through the Mayo tumor registry and follow-up questionnaires, and person years were calculated from date of mammogram through date of BC, last mammogram at Mayo or last questionnaire. We estimated Transpara AI malignancy score (1-10), 5-year Mirai risk and Volpara volumetric density measures (volumetric percent density, dense volume) on screening mammograms at enrollment and obtained clinical BI-RADS density. We calculated the BC-PRS using 268 SNPs (of the 313-SNP score) and the weighted sum of the SNPs using odds ratios from the largest BC GWAS of European ancestry to date. We performed cox proportional hazards regression adjusting for age and BMI to estimate hazard ratios (HRs), 95% confidence intervals (CI), and C-statistics (AUC) to describe the contribution of BC-PRS (per standard deviation (SD)) to the association of AI scores and breast density with BC risk. Likelihood ratio tests (LRT) and bootstrapping were used to compare model performance with vs. without inclusion of BC-PRS, at 10-years follow-up. Comparisons to the Tyrer-Cuzick model are ongoing. Over a median of 9.1 years (Interquartile range, 6.8 to 10.6), 446 incident BC were identified of which 285 invasive and 103 DCIS were confirmed by pathology report (60 were self-reported); 399 (256 invasive) occurred within 10 years and were used in primary analyses. Transpara score [HR per 1 unit=1.18 (1.14, 1.23), AUC=0.655 (0.628, 0.682)] and Mirai risk [HR per SD log risk=1.54 (1.43, 1.65), AUC=0.681 (0.655, 0.706)] were both associated with BC. All three breast density measures were also significantly associated with BC, including in models with AI scores (Table). Addition of the BC-PRS to models with AI scores and breast density improved both model fit (All PLRT < 0.001; Table) and discrimination, with increases in AUC ranging from 0.033 to 0.034 for models with Transpara and density and 0.025 to 0.026 for models with Mirai and density (all P-values < 0.001; Table). Analyses restricted to invasive BC showed identical results. But, the BC-PRS contribution at 5-year follow-up was attenuated. In this cohort study, we found BC-PRS contributed to long-term risk prediction of BC above imaging AI models and breast density. Imaging and genetic risk factors are complementary and may outperform existing risk models and provide more accurate risk assessment to aid in precision prevention strategies. Citation Format: Celine Vachon, Christopher Scott, Imon Banerjee, Ramon Correa Moderno, Dan Hursh, Matthew Jensen, Yvonne Wang, Kathleen Brandt, Karla Kerlikowske, Sandhya Pruthi, Fergus Couch, Stacey Winham. Contribution of a breast cancer polygenic risk score to mammography artificial intelligence models and breast density for long term breast cancer risk prediction [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS10-06.

A narrative review on the current uses of artificial intelligence in endodontics

Abstract Artificial intelligence (AI) has been widely introduced to dentistry in the past decade. Its application in endodontics is limited to different areas such as working length determination, morphological assessment, detection of vertical root fracture, and the detection of periapical lesion. Therefore, this study aims to highlight the available evidence for the uses of AI in endodontics. It also presents the current status as well as the future perspectives on the uses of AI and its potential application in everyday practice. A literature search was conducted from January 2000 to January 2023 using PubMed and Google Scholar for the terms AI and endodontics. Thirty-one studies were evaluated and summarized, highlighting the potential use of different AI models in endodontics. The evaluation of the studies indicated that the use of AI is promising and could aid in tailored endodontics therapy. It would help the clinician in the detection of periapical radiolucency, root fractures, and determination of working length. However, well-designed, high-quality research is required to assess the possible implementation of AI into day-to-day practice in endodontics.

Application of Artificial Intelligence in Pediatric Dentistry: A Literature Review

ABSTRACT Artificial intelligence (AI) is the development of computer systems that can do tasks that normally require human intelligence. A number of dental specialties, including pediatric dentistry, now use AI and its subsets, machine learning, and deep learning. The evolution of AI in healthcare has been linked to the creation of AI applications meant to support medical professionals in diagnosing patients and choosing the best course of treatment. AI is the capability of robots to learn and use that information to carry out a range of cognitive tasks, including language processing, learning, reasoning, and making decisions—basically imitating human behavior. This review gives an overview of the numerous applications of AI that are beneficial to pediatric dentistry.

When Trust in AI Mediates: AI News Use, Public Discussion, and Civic Participation

Abstract Artificial intelligence (AI), since its marked proliferation in the mid-2010s, has significantly penetrated various sectors, becoming a transformative force that necessitates ongoing refinement of communication strategies and political dynamics by stakeholders. Drawing on the Orientation-Stimulus-Reasoning-Orientation-Response (O-S-R-O-R) model, this study delves into the role of AI news consumption in participatory democracy. Using data from a nationwide survey (N = 1,252), the study assesses a mediation model that proposes a multilayered process through which AI news consumption facilitates civic participation. The findings reveal that AI news consumption across different media platforms is positively linked with civic participation, facilitated by both online and offline AI discussions. This relationship is further mediated by trust in AI news and trust in AI actors. Further theoretical and practical implications are also discussed.

The application of artificial intelligence in EUS

ABSTRACT Artificial intelligence (AI) is an epoch-making technology, among which the 2 most advanced parts are machine learning and deep learning algorithms that have been further developed by machine learning, and it has been partially applied to assist EUS diagnosis. AI-assisted EUS diagnosis has been reported to have great value in the diagnosis of pancreatic tumors and chronic pancreatitis, gastrointestinal stromal tumors, esophageal early cancer, biliary tract, and liver lesions. The application of AI in EUS diagnosis still has some urgent problems to be solved. First, the development of sensitive AI diagnostic tools requires a large amount of high-quality training data. Second, there is overfitting and bias in the current AI algorithms, leading to poor diagnostic reliability. Third, the value of AI still needs to be determined in prospective studies. Fourth, the ethical risks of AI need to be considered and avoided.

Artificial Intelligence and Its Role in Medical Research

Abstract Artificial intelligence (AI) has emerged as a revolutionary mechanism in the field of science and technology. The role of AI in scientific research is becoming broader day by day. While AI is making processes easier and smoother, it is also raising concerns among researchers regarding its ethical utility. Besides, the decision-making process of AI remains a black box for research scholars. This review seeks to provide a comprehensive overview of the utilization of AI-based tools in medical research, along with an exploration of the associated challenges. The search strategy involved querying PubMed using keywords such as “Artificial intelligence,” “machine learning,” and “medical research” to identify relevant literature. The significance of AI in research is inevitable. Researchers need to accept the fact that AI will soon be an integral part of research, at the same time, the current limitations of AI need to be alleviated so that it will be embraced by the scientific community.

Revolutionizing Dentistry: The Applications of Artificial Intelligence in Dental Health Care

ABSTRACT Artificial intelligence (AI) is transforming the landscape of health care, and dentistry is no exception. This article explores the various applications of AI in dentistry, showcasing how this advanced technology is revolutionizing diagnosis, treatment, and patient care. From enabling early detection of oral diseases to enhancing the precision of dental procedures, AI is driving the industry toward more efficient and effective dental healthcare services. This article delves into the specific ways in which AI is being integrated into dental practices, highlighting its potential to improve patient outcomes and advance the field of dentistry.

Smart science: How artificial intelligence is revolutionizing pharmaceutical medicine

Abstract Artificial intelligence (AI) is a discipline within the field of computer science that encompasses the development and utilization of machines capable of emulating human behavior, particularly regarding the astute examination and interpretation of data. AI operates through the utilization of specialized algorithms, and it includes techniques such as deep (DL), and machine learning (ML), and natural language processing (NLP). As a result, AI has found its application in the study of pharmaceutical chemistry and healthcare. The AI models employed encompass a spectrum of methodologies, including unsupervised clustering techniques applied to drugs or patients to discern potential drug compounds or appropriate patient cohorts. Additionally, supervised ML methodologies are utilized to enhance the efficacy of therapeutic drug monitoring. Further, AI-aided prediction of the clinical outcomes of clinical trials can improve efficiency by prioritizing therapeutic intervention that are likely to succeed, hence benefiting the patient. AI may also help create personalized treatments by locating potential intervention targets and assessing their efficacy. Hence, this review provides insights into recent advances in the application of AI and different tools used in the field of pharmaceutical medicine.

Identifying and Categorizing Bias in AI/ML for Earth Sciences

Abstract Artificial intelligence (AI) can be used to improve performance across a wide range of Earth system prediction tasks. As with any application of AI, it is important for AI to be developed in an ethical and responsible manner to minimize bias and other effects. In this work, we extend our previous work demonstrating how AI can go wrong with weather and climate applications by presenting a categorization of bias for AI in the Earth sciences. This categorization can assist AI developers to identify potential biases that can affect their model throughout the AI development life cycle. We highlight examples from a variety of Earth system prediction tasks of each category of bias.

Geostationary Satellite–Derived Positioning of a Tropical Cyclone Center Using Artificial Intelligence Algorithms over the Western North Pacific

Abstract Artificial intelligence (AI) models were developed to determine the center of tropical cyclones (TCs) in the western North Pacific. These models integrated information from six channels of geostationary satellite imagery: the brightness temperature of four infrared (IR) and one shortwave IR channels, as well as the reflectivity of one visible channel. The first model is a convolutional neural network designed for spatial data processing, and the second is a convolutional long short-term memory model that effectively captures spatiotemporal information. For training, verification, and testing purposes, spatial images from six channels were obtained from the Japanese Himawari-8 satellite from 2016 to 2021. The position of the European Centre for Medium-Range Weather Forecasts 6- or 12-h prediction was assigned as an initial value to the AI models. Errors in the initial value were 20–50 km compared to the Joint Typhoon Warning Center best track, depending on TC intensity. Weak (strong) TCs exhibited large (small) errors. This error dependency was found in Automated Rotational Center Hurricane Eye Retrieval (ARCHER) product, which is currently used by several operational organizations. ARCHER errors were typically small when observations from both geostationary and polar-orbiting satellites were included. Significant errors remained in the absence of microwave channel information from polar-orbiting satellites. This study successfully developed two AI models that consistently determined the location of the TC center using only six-channel images from geostationary satellites. These models exhibited comparable or better performance than the ARCHER products. The newly developed AI models can potentially be implemented for operational use.

AI-enabled industrial equipment monitoring, diagnosis and health management

Abstract Artificial intelligence (AI) has achieved significant progress in recent years and its applications cover a wide range of fields such as computer vision, natural language processing, autonomous driving and medical diagnosis. In industry, the rapid development of real-time-sensor measurement techniques promotes equipment surveillance and maintenance into the era of big data. It is still challenging to manually analyze this big data and establish general physical modelling by using the information hidden in these data. To this end, AI technology, such as machine learning and neural networks, has emerged as a promising tool to extract useful knowledge from measured data, and to tackle the real-time monitoring, diagnosis problems as well as enhancing the health management and reliability of modern industrial equipment. With the vision of establishing a strong link between AI and industrial equipment surveillance and maintenance, this special feature is designated to select, organize and exhibit the latest research progress on the cutting-edge research topics relevant to AI-Enabled Industrial Equipment Monitoring, Diagnosis and Health Management. Submissions for this topic in Measurement Science and Technology are open from 23 March 2022 to 30 September 2022 and contains 42 outstanding papers in above research fields.

Exploring the bioethical implications of using artificial intelligence in writing research proposals

Abstract Artificial intelligence (AI) has great potential to assist researchers in writing research proposals, by generating hypotheses, identifying literature, and suggesting methods for data collection and analysis. However, the use of AI in research proposal writing raises important bioethical implications, including the unintentional propagation of bias and questions about the role of human expertise and judgment in the research process. This paper explores the ethical implications of using AI in research proposal writing and proposes guidelines for the responsible and ethical use of AI in this context. The paper will review the potential benefits and challenges associated with using AI in research proposal writing, discuss the role of human expertise and judgment, and propose guidelines for promoting transparency and accountability in developing and using AI systems. Ultimately, addressing the bioethical issues related to AI in research proposal writing will require ongoing dialogue and collaboration between stakeholders, as well as a commitment to transparency, accountability, and ethical principles.

The performance of artificial intelligence chatbot large language models to address skeletal biology and bone health queries.

Artificial intelligence (AI) chatbots utilizing large language models (LLMs) have recently garnered significant interest due to their ability to generate humanlike responses to user inquiries in an interactive dialog format. While these models are being increasingly utilized to obtain medical information by patients, scientific and medical providers, and trainees to address biomedical questions, their performance may vary from field to field. The opportunities and risks these chatbots pose to the widespread understanding of skeletal health and science are unknown. Here we assess the performance of 3 high-profile LLM chatbots, Chat Generative Pre-Trained Transformer (ChatGPT) 4.0, BingAI, and Bard, to address 30 questions in 3 categories: basic and translational skeletal biology, clinical practitioner management of skeletal disorders, and patient queries to assess the accuracy and quality of the responses. Thirty questions in each of these categories were posed, and responses were independently graded for their degree of accuracy by four reviewers. While each of the chatbots was often able to provide relevant information about skeletal disorders, the quality and relevance of these responses varied widely, and ChatGPT 4.0 had the highest overall median score in each of the categories. Each of these chatbots displayed distinct limitations that included inconsistent, incomplete, or irrelevant responses, inappropriate utilization of lay sources in a professional context, a failure to take patient demographics or clinical context into account when providing recommendations, and an inability to consistently identify areas of uncertainty in the relevant literature. Careful consideration of both the opportunities and risks of current AI chatbots is needed to formulate guidelines for best practices for their use as source of information about skeletal health and biology.

A Brief Review of the Efficacy in Artificial Intelligence and Chatbot-Generated Personalized Fitness Regimens

ABSTRACT Artificial intelligence (AI) and chatbot technologies have rapidly emerged with promising applications in public health, including developing personal lifestyles and exercise routines. This article examines the effectiveness of AI-based chatbot platforms to generate personalized exercise regimens. Chatbots are freely accessible to users with Internet access and may be more cost-effective than a personal trainer. This review highlights multiple examples of AI being used to offer fitness advice, supplement athletic coaching, and create everyday exercise routines. The results of these studies demonstrate a positive outlook on using AI for strength and conditioning applications, for personal and professional use. Despite having the potential to replace personal fitness trainers for everyday users and athletes alike, more extensive research should be performed using AI combined with physicians, personal trainers, and nutritionists to determine areas where AI can be further optimized. More research focusing on the effectiveness of AI-generated routines for the everyday user may further validate the potential for chatbot-mediated exercise. This brief review aims to evaluate the efficacy of AI-generated personalized fitness regimens, emphasizing physical fitness, athletic performance, and overall physical and mental well-being. The video abstract for this article can be found in the video SDC 1.

ENGAGING OLDER ADULTS AND CAREGIVERS AS STAKEHOLDERS IN THE ARTIFICIAL INTELLIGENCE AND TECHNOLOGY COLLABORATORY

Abstract Artificial Intelligence (AI) holds the unique potential to capture older adult functional and behavioral diversity and improve geriatric care. Yet a gap exists between new technologies and the real-world needs of their target populations. Currently in the United States, three Collaboratories support novel AI projects in Aging Research. As the Stakeholder Engagement Core of the Johns Hopkins AI Collaboratory, in this poster, we share early lessons of forming a ‘Council’ of stakeholders – a group with the charge of informing pilot AI projects longitudinally. The Council was envisioned to include 10-15 persons: older adults, caregivers, and clinicians. We adopted different outreach techniques for clinicians versus non-clinicians; leveraged existing relationships with affiliated Memory and Social Clinics; individually approached community dwelling older adults and caregivers including homebound older adults and older adults with disabilities; and reached out to clinicians and advocates known to be committed to geriatric care. We developed plain language materials and explained the meaning of ’AI’ and ’Stakeholder Engagement’. Five out of 5 outreached clinicians, and 10 out of 20 older adults/caregivers agreed to serve on the Council. Many older adults on our Council had difficulty using their email and computer and required support. Several caregivers declined due to high caregiving demands and many older adults with multiple chronic conditions declined due to high personal needs; all reiterated their interest in the topic area despite declining participation. Future novel-technology projects should ensure inclusion of diverse stakeholders; incorporating a flexible plan of engaging high-needs older adults and caregivers is required.

ARTIFICIAL INTELLIGENCE IN MENTAL HEALTH FOR THE AGING POPULATION: A SCOPING REVIEW

Abstract Artificial intelligence (AI) has significant potential in mental health research and service delivery for the aging population. This poster provides an overview of its evolving utilization, including research methods, data sources, and predictors of mental health. The review of 26 articles from 2015 to 2023 found that surveys/questionnaires, clinical data, non-clinical data, and electronic health records are the most commonly used data sources, with clinical data being the most common, accounting for more than 50% of the articles. The frequently utilized AI techniques in mental health research for older adults were supervised learning (65.4%), deep learning (42.3%), unsupervised learning (7.7%), NLP (7.7%), and semi-supervised learning (3.8%). Mental health factors that received the most attention in the research were anxiety/depression (65.4%), dementia (Alzheimer’s) (38.5%), mild cognitive impairment (23.1%), suicidal ideation (15.4%), and trajectory of cognitive decline (7.7%). AI is currently employed in various aspects of mental health research for older adults, including identifying and diagnosing mental health conditions at an early stage, monitoring mental well-being, assessing the severity of mental health conditions, predicting cognitive trajectories, and providing assistance in treatment. The recent application of generative language models, such as ChatGPT, has further advanced the field of AI in mental health research. However, ethical concerns such as bias, privacy, and transparency must be addressed before widespread implementation. In conclusion, AI has great potential as well as risks and ethical concerns for mental health research and service delivery to aging populations, but further research is needed.

HEALTHCARE WORKERS’ PERSPECTIVES ON AI-ENABLED ROBOTS USE IN LONG-TERM CARE: A SCOPING REVIEW

Abstract Artificial intelligence (AI) enabled robots are increasingly implemented in long-term care homes (LTC). However, the views of LTC staff on these robots remain largely unexplored. Our scoping review delves into the staff’s perceptions, outlining the advantages and challenges of using AI robots in LTC settings. Using the Joanna Briggs Institute’s methodology, we screened 86 articles from 2013 to 2023, with 35 fitting our criteria. Our analysis was informed by McCormack’s Person-centred Care Practice (PCP) Framework and the Consolidated Framework for Implementation Research (CFIR). We identified five key barriers: 1) the complexity of the robot, 2) potential job losses and increased workload, 3) concerns about safety and efficacy, 4) risk of depersonalized care, and 5) a lack of supportive regulations and resources in LTC facilities. To address these challenges, we recommend strategies: a) staff training, b) clarifying robot benefits to staff, c) demonstrating how robots can fulfill resident needs, d) implementing ethical guidelines, and e) aligning robot use with LTC policies while ensuring resource availability. In conclusion, partnership is required among healthcare workers, organizational leaders, robot developers, and researchers; they should not work in silos. More research is needed to explore how to facilitate effective partnerships.

AI Regulation and the Protection of Source Code

Abstract Artificial intelligence (AI) has been rapidly developing since the mid-20th century. Today, AI is deployed in many aspects of our daily lives and is increasingly ubiquitous. At the same time, concerns about AI, including discrimination, privacy, and security, have prompted calls for greater regulation. To this end, regulators may seek access to AI’s source code (the human-readable program for the software). However, many trade agreements contain provisions prohibiting source code disclosure as a condition for market access in the relevant countries. This article considers the tension between AI regulation and these provisions. In situations where such provisions apply, for AI regulation to remain compliant, we argue that regulators can only require access to source code in exceptional cases where the general exceptions in the General Agreement on Trade in Services apply.

Regulating Gatekeeper Artificial Intelligence and Data: Transparency, Access and Fairness under the Digital Markets Act, the General Data Protection Regulation and Beyond

AbstractArtificial intelligence (AI) is not only increasingly being used in business and administration contexts, but a race for its regulation is also underway, with the European Union (EU) spearheading the efforts. Contrary to existing literature, this article suggests that the most far-reaching and effective EU rules for AI applications in the digital economy will not be contained in the proposed AI Act, but in the Digital Markets Act (DMA). We analyse the impact of the DMA and related EU acts on AI models and underlying data across four key areas: disclosure requirements; the regulation of AI training data; access rules; and the regime for fair rankings. We demonstrate that fairness, under the DMA, goes beyond traditionally protected categories of non-discrimination law on which scholarship at the intersection of AI and law has focused on. Rather, we draw on competition law and the FRAND criteria known from intellectual property law to interpret and refine the DMA provisions on fair rankings. Moreover, we show how, based on Court of Justice of the European Union jurisprudence, a coherent interpretation of the concept of non-discrimination in both traditional non-discrimination and competition law may be found. The final section sketches out proposals for a comprehensive framework of transparency, access and fairness under the DMA and beyond.