Tax Exemptions for Charitable Single-Member Limited Liability Companies

This summer, the IRS issued long-awaited guidance on the deductibility of charitable contributions made to a single-member limited liability company (“SMLLC”) that is wholly-owned by a charitable organization exempt from federal income tax as a organization described in Section 501(c)(3). Previously, in a 2001 private letter ruling, the IRS confirmed that a SMLLC wholly-owned by a U.S. charity did not need to submit a separate application for recognition of federal income tax exemption, but declined to rule on whether contributions made to the SMLLC would be deductible under Section 170 as charitable contributions. An article in the IRS Continuing Professional Education Text for the fiscal year 2001 stated that “[g]uidance on this issue will be forthcoming in the near future.” Notice 2012-52 provides this guidance. In Notice 2012-52, the IRS ruled that a contribution to a domestic SMLLC that is wholly owned by a U.S. charity would be treated as a deductible charitable contribution, assuming all the requirements of Section 170 are met. This article discusses the requirements for federal income tax exemption of a SMLLC and the deductibility of contributions made to the SMLLC as well as the availability of Texas state tax exemptions for the SMLLC

Nonprofit Executive Compensation

Excessive compensation paid to nonprofit executives and board members is one of the key issues concerning charitable organizations that garner the attention of the general public and Congress. Charitable organizations may pay reasonable compensation to their directors, executive officers and employees for their services without violating applicable federal tax law or state law. The determination of reasonable compensation depends on several factors – the budget of the organization being the most significant factor. Other factors include the number of employees of the organization, the particular sector of the charitable community served by the organization, the geographic location of the organization, the focus of the organization as being national or local, the length of the employee’s service and external market forces. Even if executive compensation is considered reasonable in light of the foregoing factors, the perception that a charitable organization is paying excessive compensation can be damaging to the organization’s reputation. Some nonprofit executive salaries have reached seven figures, particularly in the larger health care systems and higher education.2 In some cases, the highest paid employee of a charitable organization is not its chief executive officer, but instead may be a senior administrator or key physician of a large urban medical center, a key athletic coach at a Division I university, or a chief investment officer of a university or foundation with a large endowment. Reports of high nonprofit executive compensation have lead the Internal Revenue Service to conduct an Executive Compensation Compliance Initiative in 2004 (with its findings published in March 2007, discussed below), and the Internal Revenue Service continues to scrutinize nonprofit executive compensation. In addition, because nonprofit executive compensation must be reported annually on the organization’s Form 990, the general public, the media, and charity watchdog organizations also scrutinize nonprofit executive compensation. Therefore, it is important for charitable organizations not only to understand the federal tax law governing the payment of reasonable compensation to their directors, officers and key employees, but also to understand their reporting obligations and best practices with respect to executive compensation to avoid undue scrutiny

Gender-based differences in letters of recommendation written for ophthalmology residency applicants.

BACKGROUND:To determine whether gender-based differences may be present in letters of recommendation written for ophthalmology residency applicants. METHODS:All applications submitted through SF Match to the UCLA Stein Eye Institute Residency Training Program from the 2017-2018 application cycle were analyzed using validated text analysis software (Linguistic Inquiry and Word Count (Austin, TX)). The main outcome measures were differences in language use in letters of recommendation by gender of applicant. RESULTS:Of 440 applicants, 254 (58%) were male and 186 (42%) were female. The two gender groups had similar United States Medical Licensing Exam (USMLE) Step 1 scores, undergraduate grade point averages (uGPA's), proportions of underrepresented minority (URM) applicants and Gold Humanism Honor Society members, numbers of academic and service activities listed, and gender distributions of their letter writers (all P values > 0.05). However, letters written for male applicants were determined to use more "authentic" words than those written for female applicants (mean difference, 0.800; 95% CI, 0.001-1.590; P = 0.047). Letters written for male applicants also contained more "leisure" words (mean difference, 0.056; 95% CI, 0.008-0.104; P = 0.023) and fewer "feel" words (mean difference, 0.033; 95% CI, 0.001-0.065; P = 0.041) and "biological processes" words (mean difference, 0.157; 95% CI, 0.017-0.297; P = 0.028). CONCLUSIONS:There were gender differences detected in recommendation letters in ophthalmology consistent with prior studies from other fields. Awareness of these differences may improve residency selection processes

Amplifying the Social Intelligence of Teams Through Human Swarming

Artificial Swarm Intelligence (ASI) is a method for amplifying the collective intelligence of human groups by connecting networked participants into real-time systems modeled after natural swarms and moderated by AI algorithms. ASI has been shown to amplify performance in a wide range of tasks, from forecasting financial markets to prioritizing conflicting objectives. This study explores the ability of ASI systems to amplify the social intelligence of small teams. A set of 61 teams, each of 3 to 6 members, was administered a standard social sensitivity test — Reading the Mind in the Eyes” or RME. Subjects took the test both as individuals and as ASI systems (i.e. “swarms”). The average individual scored 24 of 35 correct (32% error) on the RME test, while the average ASI swarm scored 30 of 35 correct (15% error). Statistical analysis found that the groups working as ASI swarms had significantly higher social sensitivity than individuals working alone or groups working together by plurality vote (p\u3c0.001). This suggests that when groups reach decisions as real-time ASI swarms, they make better use of their social intelligence than when working alone or by traditional group vote

Enhancing Group Social Perceptiveness through a Swarm-based Decision-Making Platform

Swarm Intelligence is natural phenomenon that enables social animals to make group decisions in real-time systems. This process has been deeply studied in fish schools, bird flocks, and bee swarms, where collective intelligence has been observed to emerge. The present paper describes swarm.ai—a collaborative technology that enables swarms of humans to collectively converge upon a decision as a real-time system. Then we present the results of a study investigating if groups working as “human swarms” can amplify their social perceptiveness, a key predictor of collective intelligence. Results showed that groups reduced their social perceptiveness errors by more than half when operating as a swarm. A statistical analysis revealed with 99.9% confidence that groups working as swarms had significantly higher social perceptiveness than either individuals working alone or through plurality vote

Keeping Humans in the Loop: Pooling Knowledge through Artificial Swarm Intelligence to Improve Business Decision Making

This article explores how a collaboration technology called Artificial Swarm Intelligence (ASI) addresses the limitations associated with group decision making, amplifies the intelligence of human groups, and facilitates better business decisions. It demonstrates of how ASI has been used by businesses to harness the diverse perspectives that individual participants bring to groups and to facilitate convergence upon decisions. It advances the understanding of how artificial intelligence (AI) can be used to enhance, rather than replace, teams as they collaborate to make business decisions

Determinants of Sexual Activity and Its Relation to Cervical Cancer Risk among South African Women

BACKGROUND. Invasive cervical cancer is the commonest cause of cancer morbidity and mortality in South African women. This study provides information on adult women's sexual activity and cervical cancer risk in South Africa. METHODS. The data were derived from a case-control study of hormonal contraceptives and cervical cancer risk. Information on age of sexual debut and number of lifetime sexual partners was collected from 524 incident cases and 1541 hospital controls. Prevalence ratios and adjusted prevalence ratios were utilised to estimate risk in exposures considered common. Crude and adjusted relative risks were estimated where the outcome was uncommon, using multiple logistic regression analysis. RESULTS. The median age of sexual debut and number of sexual partners was 17 years and 2 respectively. Early sexual debut was associated with lower education, increased number of life time partners and alcohol use. Having a greater number of sexual partners was associated with younger sexual debut, being black, single, higher educational levels and alcohol use. The adjusted odds ratio for sexual debut < 16 years and ≥ 4 life-time sexual partners and cervical cancer risk were 1.6 (95% CI 1.2 – 2.2) and 1.7 (95% CI 1.2 – 2.2), respectively. CONCLUSION. Lower socio-economic status, alcohol intake, and being single or black, appear to be determinants of increased sexual activity in South African women. Education had an ambiguous effect. As expected, cervical cancer risk is associated with increased sexual activity. Initiatives to encourage later commencement of sex, and limiting the number of sexual partners would have a favourable impact on risk of cancer of the cervix and other sexually transmitted infections.National Cancer Institute (R01 CA 73985

Measuring Group Personality with Swarm AI

The aggregation of individual personality tests to predict team performance is widely accepted in management theory but has significant limitations: the isolated nature of individual personality surveys fails to capture much of the team dynamics that drive real-world team performance. Artificial Swarm Intelligence (ASI), a technology that enables networked teams to think together in real-time and answer questions as a unified system, promises a solution to these limitations by enabling teams to take personality tests together and converge upon answers that best represent the group’s disposition. In the present study, the group personality of 94 small teams was assessed by having teams take a standard Big Five Inventory (BFI) test both as individuals, and as a real-time system enabled by an ASI technology known as Swarm AI. The predictive accuracy of each personality assessment method was assessed by correlating the BFI personality traits to a range of real-world performance metrics. The results showed that assessments of personality generated using Swarm AI were far more predictive of team performance than the traditional survey-based method, showing a significant improvement in correlation with at least 25% of performance metrics, and in no case showing a significant decrease in predictive performance. This suggests that Swarm AI technology may be used as a highly effective team personality assessment tool that more accurately predicts future team performance than traditional survey approaches

Amplifying the Collective Intelligence of Teams with Swarm AI

Group decision-making is strengthened by the varied knowledge and perspectives that each member brings, yet teams often fail to capitalize on their diversity. This paper describes how Swarm AI, a novel collaborative intelligence technology modeled on the decision-making process of honey bee swarms, enables networked human groups to more effectively leverage their combined insights. Through an empirical study conducted on 60 small teams, each of 3 to 6 members, we demonstrate the capacity of Swarm AI to significantly amplify the collective intelligence of human groups. A well-known testing instrument—the Reading the Mind in the Eyes (RME) test —was used to measure the social intelligence of each team—a key indicator of collective intelligence. The study compares the RME performance of (i) individuals, (ii) teams working by majority vote, and (iii) teams using an interactive software platform that employs Swarm AI technology