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This article provides an overview of the regulation of artificial intelligence (AI) in Brazil, with a focus on its impact on labor and employment. While AI adoption in Brazilian industry is still limited, a significant portion of jobs in the country are considered highly vulnerable to AI. The article highlights the challenges and lack of regulation surrounding platform work and the potential impact of AI on agriculture. The Brazilian government has established the Brazilian National Strategy for Artificial Intelligence (EBIA) to promote responsible AI development and the training of qualified professionals. Additionally, there are bills being drafted to regulate AI and establish principles, rights, and duties for its use, as well as to provide rights for individuals affected by AI systems. The article emphasizes the importance of social dialogue and workers' organizations in protecting workers' rights in the face of AI advancements. [Extracted from the article]

AI-powered misinformation detectors—artificial intelligence tools that identify false or inaccurate online content—have emerged as a potential intervention for helping internet users understand the veracity of the content they view. However, the algorithms used to create these detectors are experimental and largely untested at the scale necessary to be effective on a social media platform. Furthermore, the assumptions programmed into these algorithms frequently conflict with everyday, non-threatening online communication, potentially leading to unwarranted censorship. Consequently, understanding the current limitations of AI interventions for misinformation detection on the internet is important for developing better iterations of these tools and forming effective policies around them. [ABSTRACT FROM AUTHOR]

This editorial introduces the special issue "The Interplay Between Artificial Intelligence, Production Systems, and Operations Management Resilience.' We selected twelve papers, encompassing many angles that illuminate the advances and challenges dealing with artificial intelligence tools and approaches in the production systems and operations management resilience domains. This editorial presents the papers with a smart view, highlighting the essentials of each article, such as full paper title, background, theory/literature scope, methodology design/analysis approach, and the main findings/contributions. Finally, the conclusions, future pathways, and research directions are presented. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is an important source of competitive advantage as it enables task augmentation and automation. However, while AI can create significant value, it is important to note that AI investments are fraught with risks and uncertainties. Thus, managers are likely to carefully evaluate potential AI investments before committing to investing. However, we know little about how managers' appraisal of AI influences their investment choices. Drawing upon theorization in the areas of business value of AI, agentic information systems (IS) appraisal, and time-situated agency, we extend existing theory in two ways: (1) development of an AI classification (foundational typology) that proposes two dimensions (action autonomy and learning autonomy) for classifying AI by type and level of autonomy; and (2) development of propositions that leverage time-situated agency and the AI classification to explicate how managers' delegation preferences influence their AI investment appraisal. This paper contributes a foundational theoretical platform for furthering AI investment appraisal research. In addition, the paper sets an agenda for future research in this area. [ABSTRACT FROM AUTHOR]

Persistent racial disparities in health outcomes have catalyzed legislative reforms and heightened scientific focus recently. However, despite the well-documented properties of skin pigments in binding drug compounds, their impact on therapeutic efficacy and adverse drug responses remains insufficiently explored. This perspective examines the intricate relationships between variation in melanin-based skin pigmentation and pharmacokinetics and -dynamics, highlighting the need for considering diversity in skin pigmentation as a variable to advance the equitability of pharmacological interventions. The article provides guidelines on the selection of New Approach Methods (NAMs) to foster inclusive study designs in preclinical drug development pipelines, leading to an improved level of translatability to the clinic.

Looking back at 50 years of Journal of Consumer Research methods and interviewing some of the field's most respected methodologists, this article seeks to craft a core set of best practices for scholars in consumer research. From perennial issues like conceptual validity to emerging issues like data integrity and replicability, the advice offered by our experts can help scholars improve the way they approach their research questions, provide empirical evidence that instills confidence, use new tools to make research more inclusive or descriptive of the "real world," and seek to become thought leaders. [ABSTRACT FROM AUTHOR]

Extending Thompson's discussion of the extended self, I contrast his view if the I Self (in William James' terms) with my focus on James' Me Self. I go on to discuss the Aggregate Self, the Digital Self, and various concepts of plural selves and cultural differences. Thompson's introduction of distributed self fits nicely here and further expands the discussion of self. I introduce the concept of the Extended Object and briefly examine thanobots and AI and the way they may affect our concepts of self in the future. [ABSTRACT FROM AUTHOR]

We have entered an era where, in addition to us humans, systems can also think. It is imperative to decide how decision rights and authorities are allocated and distributed across interconnected co-cognitors: things that think. To address this question vital to the redesign of organizations, we reconceptualize cognitive reapportionment as the dynamic reallocation of decision rights and authorities across human and system co-cognitors capable of independent decision making. We articulate the Helix Model of Decision Journeys, wherein decision journeys, comprised of decision elements allocable to human or system co-cognitors, interweave and integrate business processes. The dynamic reapportionment of cognitive responsibilities across co-cognitors is dependent upon the type of scientific reasoning—deduction, induction, or abduction—at each decision element. We further propose two intertwined mechanisms for the art of letting go that facilitate the adoption of the Helix model. First, the nexus of omnipresent system co-cognitors collectively needs to evoke trust. Only when Technology Trust Thresholds are exceeded can decision making be reorganized and control relinquished to system co-cognitors. Second, the world of omnipresent cognitors will compel us to redesign, reprogram, and remix our workflow and business processes. Only through a purposeful effort to Redesign in the Remix Regime can the deduction-induction-abduction nature of decision tasks serve as the basis for their reapportionment. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has become an important driver of economic growth and innovation. With rapid advances in AI, firms have a strategic opportunity to re-envision the cognitive reapportionment of tasks between humans, AI, and non-AI technologies. In doing so, firms can transform and dramatically elevate value creation through their business models, processes, and market offerings. We focus on a key risk, patent infringement litigation (PIL), that can adversely impact a firm's value creation with AI. We posit and demonstrate that firms facing AI-PILs would have more negative short term abnormal returns compared to firms facing non-AI PILs. We further suggest that the abnormal returns are moderated by the types of plaintiffs and AI patents in which the abnormal returns are more negative when the plaintiffs are non-practicing entities (versus practicing entities) and when the AI patents at suit concern expertise-driven (versus data-driven) AI. Exploring the moderators jointly, we find evidence that for data-driven AI patents, the negative abnormal returns are stronger when the plaintiffs are practicing entities, but for expertise-driven AI patents, the negative abnormal returns are stronger when the plaintiffs are non-practicing entities. [ABSTRACT FROM AUTHOR]

BACKGROUND: About one-third of older adults aged 65 years and older often have mild cognitive impairment or dementia. Acoustic and psycho-linguistic features derived from conversation may be of great diagnostic value because speech involves verbal memory and cognitive and neuromuscular processes. The relative decline in these processes, however, may not be linear and remains understudied. OBJECTIVE: This study aims to establish associations between cognitive abilities and various attributes of speech and natural language production. To date, the majority of research has been cross-sectional, relying mostly on data from structured interactions and restricted to textual versus acoustic analyses. METHODS: In a sample of 71 older (mean age 83.3, SD 7.0 years) community-dwelling adults who completed qualitative interviews and cognitive testing, we investigated the performance of both acoustic and psycholinguistic features associated with cognitive deficits contemporaneously and at a 1-2 years follow up (mean follow-up time 512.3, SD 84.5 days). RESULTS: Combined acoustic and psycholinguistic features achieved high performance (F1-scores 0.73-0.86) and sensitivity (up to 0.90) in estimating cognitive deficits across multiple domains. Performance remained high when acoustic and psycholinguistic features were used to predict follow-up cognitive performance. The psycholinguistic features that were most successful at classifying high cognitive impairment reflected vocabulary richness, the quantity of speech produced, and the fragmentation of speech, whereas the analogous top-ranked acoustic features reflected breathing and nonverbal vocalizations such as giggles or laughter. CONCLUSIONS: These results suggest that both acoustic and psycholinguistic features extracted from qualitative interviews may be reliable markers of cognitive deficits in late life.

BACKGROUND: Although uncertainties exist regarding implementation, artificial intelligence-driven generative language models (GLMs) have enormous potential in medicine. Deployment of GLMs could improve patient comprehension of clinical texts and improve low health literacy. OBJECTIVE: The goal of this study is to evaluate the potential of ChatGPT-3.5 and GPT-4 to tailor the complexity of medical information to patient-specific input education level, which is crucial if it is to serve as a tool in addressing low health literacy. METHODS: Input templates related to 2 prevalent chronic diseases-type II diabetes and hypertension-were designed. Each clinical vignette was adjusted for hypothetical patient education levels to evaluate output personalization. To assess the success of a GLM (GPT-3.5 and GPT-4) in tailoring output writing, the readability of pre- and posttransformation outputs were quantified using the Flesch reading ease score (FKRE) and the Flesch-Kincaid grade level (FKGL). RESULTS: Responses (n=80) were generated using GPT-3.5 and GPT-4 across 2 clinical vignettes. For GPT-3.5, FKRE means were 57.75 (SD 4.75), 51.28 (SD 5.14), 32.28 (SD 4.52), and 28.31 (SD 5.22) for 6th grade, 8th grade, high school, and bachelors, respectively; FKGL mean scores were 9.08 (SD 0.90), 10.27 (SD 1.06), 13.4 (SD 0.80), and 13.74 (SD 1.18). GPT-3.5 only aligned with the prespecified education levels at the bachelors degree. Conversely, GPT-4s FKRE mean scores were 74.54 (SD 2.6), 71.25 (SD 4.96), 47.61 (SD 6.13), and 13.71 (SD 5.77), with FKGL mean scores of 6.3 (SD 0.73), 6.7 (SD 1.11), 11.09 (SD 1.26), and 17.03 (SD 1.11) for the same respective education levels. GPT-4 met the target readability for all groups except the 6th-grade FKRE average. Both GLMs produced outputs with statistically significant differences (P

BackgroundPatients find technology tools to be more approachable for seeking sensitive health-related information, such as reproductive health information. The inventive conversational ability of artificial intelligence (AI) chatbots, such as ChatGPT (OpenAI Inc), offers a potential means for patients to effectively locate answers to their health-related questions digitally.ObjectiveA pilot study was conducted to compare the novel ChatGPT with the existing Google Search technology for their ability to offer accurate, effective, and current information regarding proceeding action after missing a dose of oral contraceptive pill.MethodsA sequence of 11 questions, mimicking a patient inquiring about the action to take after missing a dose of an oral contraceptive pill, were input into ChatGPT as a cascade, given the conversational ability of ChatGPT. The questions were input into 4 different ChatGPT accounts, with the account holders being of various demographics, to evaluate potential differences and biases in the responses given to different account holders. The leading question, "what should I do if I missed a day of my oral contraception birth control?" alone was then input into Google Search, given its nonconversational nature. The results from the ChatGPT questions and the Google Search results for the leading question were evaluated on their readability, accuracy, and effective delivery of information.ResultsThe ChatGPT results were determined to be at an overall higher-grade reading level, with a longer reading duration, less accurate, less current, and with a less effective delivery of information. In contrast, the Google Search resulting answer box and snippets were at a lower-grade reading level, shorter reading duration, more current, able to reference the origin of the information (transparent), and provided the information in various formats in addition to text.ConclusionsChatGPT has room for improvement in accuracy, transparency, recency, and reliability before it can equitably be implemented into health care information delivery and provide the potential benefits it poses. However, AI may be used as a tool for providers to educate their patients in preferred, creative, and efficient ways, such as using AI to generate accessible short educational videos from health care provider-vetted information. Larger studies representing a diverse group of users are needed.

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Sneha Revanur, a college sophomore, founded the organization Encode Justice at the age of 15 to address the harmful implications of artificial intelligence (AI). The organization now has 900 young members in 30 countries and focuses on issues such as biased AI algorithms in the justice system, facial recognition technology, surveillance, privacy, and democratic erosion. Revanur and her peers have been involved in legislative efforts, written op-eds, and successfully defeated a state ballot initiative. Encode Justice aims to reimagine technology and build justice into AI systems from the beginning, while also preparing for potential catastrophic harms from AI. [Extracted from the article]

Firms are beginning to use chatbots to collect information from users (e.g., online reviews), a task traditionally done through forms. We used two experiments to investigate how collecting reviews using chatbots in place of forms might impact user experience and, in turn, alter the nature of the reviews submitted by users. Study 1 compares chatbots to web forms, finding that chatbots may improve perceived efficiency but otherwise have no discernible effect on satisfaction and usage intention. At the same time, chatbot usage results in shorter, lower-quality reviews. While structured templates enhance form-based reviews, introducing structure to chatbot interactions does not positively impact satisfaction and usage intent, potentially making the process less efficient. Nonetheless, a structured chatbot approach yields longer reviews and mitigates declines in quality. Adding structure to a chatbot takes the chat out of the chatbot, turning the interaction from casual conversation to a formal process, as demonstrated in Study 2. Hence, while this structured approach for chatbots improves review quality without harming satisfaction and usage intent, it may not be the most effective method for enhancing the reviewer experience. Our research shows that the chatbot and structure pulled users towards contradicting genre rules (the social structures that guide technology use) and triggered users to subconsciously enact distinctly different thought patterns as they composed reviews. [ABSTRACT FROM AUTHOR]

BACKGROUND: The elastic scattering spectroscopy (ESS) device (DermaSensor Inc., Miami, FL) is a noninvasive, painless, adjunctive tool for skin cancer detection. OBJECTIVES: To investigate the performance of the ESS device in the detection of melanoma. METHODS: A prospective, investigator-blinded, multicenter study was conducted at 8 United States (US) and 2 Australian sites. All eligible skin lesions were clinically concerning for melanoma, examined with the ESS device, subsequently biopsied according to dermatologists standard of care, and evaluated with histopathology. A total of 311 participants with 440 lesions were enrolled, including 44 melanomas (63.6% in situ and 36.4% invasive) and 44 severely dysplastic nevi. RESULTS: The observed sensitivity of the ESS device for melanoma detection was 95.5% (95% CI, 84.5% to 98.8%, 42 of 44 melanomas), and the observed specificity was 32.5% (95% CI, 27.2% to 38.3%). The positive and negative predictive values were 16.0% and 98.1%, respectively. LIMITATIONS: The device was tested in a high-risk population with lesions selected for biopsy based on clinical and dermoscopic assessments of board-certified dermatologists. Most enrolled lesions were pigmented. CONCLUSION: The ESS devices high sensitivity and NPV for the detection of melanoma suggest the device may be a useful adjunctive, point-of-care tool for melanoma detection.

The movement of organic anionic drugs across cell membranes is partly governed by interactions with SLC and ABC transporters in the intestine, liver, kidney, blood-brain barrier, placenta, breast, and other tissues. Major transporters involved include organic anion transporters (OATs, SLC22 family), organic anion transporting polypeptides (OATPs, SLCO family), and multidrug resistance proteins (MRPs, ABCC family). However, the sets of molecular properties of drugs that are necessary for interactions with OATs (OAT1, OAT3) vs. OATPs (OATP1B1, OATP1B3) vs. MRPs (MRP2, MRP4) are not well-understood. Defining these molecular properties is necessary for a better understanding of drug and metabolite handling across the gut-liver-kidney axis, gut-brain axis, and other multi-organ axes. It is also useful for tissue targeting of small molecule drugs and predicting drug-drug interactions and drug-metabolite interactions. Here, we curated a database of drugs shown to interact with these transporters in vitro and used chemoinformatic approaches to describe their molecular properties. We then sought to define sets of molecular properties that distinguish drugs interacting with OATs, OATPs, and MRPs in binary classifications using machine learning and artificial intelligence approaches. We identified sets of key molecular properties (e.g., rotatable bond count, lipophilicity, number of ringed structures) for classifying OATs vs. MRPs and OATs vs. OATPs. However, sets of molecular properties differentiating OATP vs. MRP substrates were less evident, as drugs interacting with MRP2 and MRP4 do not form a tight group owing to differing hydrophobicity and molecular complexity for interactions with the two transporters. If the results also hold for endogenous metabolites, they may deepen our knowledge of organ crosstalk, as described in the Remote Sensing and Signaling Theory. The results also provide a molecular basis for understanding how small organic molecules differentially interact with OATs, OATPs, and MRPs.