Data-Centric Distrust Quantification for Responsible AI: When Data-driven Outcomes Are Not Reliable

At the same time that AI and machine learning are becoming central to human life, their potential harms become more vivid. In the presence of such drawbacks, a critical question one needs to address before using these data-driven technologies to make a decision is whether to trust their outcomes. Aligned with recent efforts on data-centric AI, this paper proposes a novel approach to address the trust question through the lens of data, by associating data sets with distrust quantification that specify their scope of use for predicting future query points. The distrust values raise warning signals when a prediction based on a dataset is questionable and are valuable alongside other techniques for trustworthy AI. We propose novel algorithms for computing the distrust values in the neighborhood of a query point efficiently and effectively. Learning the necessary components of the measures from the data itself, our sub-linear algorithms scale to very large and multi-dimensional settings. Besides demonstrating the efficiency of our algorithms, our extensive experiments reflect a consistent correlation between distrust values and model performance. This underscores the message that when the distrust value of a query point is high, the prediction outcome should be discarded or at least not considered for critical decisions

A Fair and Memory/Time-efficient Hashmap

There is a large amount of work constructing hashmaps to minimize the number of collisions. However, to the best of our knowledge no known hashing technique guarantees group fairness among different groups of items. We are given a set $P$ of $n$ tuples in $\mathbb{R}^d$, for a constant dimension $d$ and a set of groups $\mathcal{G}=\{\mathbf{g}_1,\ldots, \mathbf{g}_k\}$ such that every tuple belongs to a unique group. We formally define the fair hashing problem introducing the notions of single fairness ($Pr[h(p)=h(x)\mid p\in \mathbf{g}_i, x\in P]$ for every $i=1,\ldots, k$), pairwise fairness ($Pr[h(p)=h(q)\mid p,q\in \mathbf{g}_i]$ for every $i=1,\ldots, k$), and the well-known collision probability ($Pr[h(p)=h(q)\mid p,q\in P]$). The goal is to construct a hashmap such that the collision probability, the single fairness, and the pairwise fairness are close to $1/m$, where $m$ is the number of buckets in the hashmap. We propose two families of algorithms to design fair hashmaps. First, we focus on hashmaps with optimum memory consumption minimizing the unfairness. We model the input tuples as points in $\mathbb{R}^d$ and the goal is to find the vector $w$ such that the projection of $P$ onto $w$ creates an ordering that is convenient to split to create a fair hashmap. For each projection we design efficient algorithms that find near optimum partitions of exactly (or at most) $m$ buckets. Second, we focus on hashmaps with optimum fairness ($0$-unfairness), minimizing the memory consumption. We make the important observation that the fair hashmap problem is reduced to the necklace splitting problem. By carefully implementing algorithms for solving the necklace splitting problem, we propose faster algorithms constructing hashmaps with $0$-unfairness using $2(m-1)$ boundary points when $k=2$ and $k(m-1)(4+\log_2 (3mn))$ boundary points for $k>2$

A Study of Relationship between Library Anxiety and Emotional Intelligence among Students of University of Tabriz and Azarbaijan Shahid Madani University

The main objective of the present research was the possibility of predicting library anxiety (LA) concerning students’ emotional intelligence (EI). This study's objective, practical research, data gathering, and research method are descriptive correlational studies. Graduate students of Azarbaijan Shahid Madani University and Tabriz University make up the statistical population of this research. Based on Morgan’s formulae for sample size determination, 298 and 350 students were selected as the study's sample size. Then, the random sampling method was used to prepare the questionnaires, which were distributed and collected. The required information in preparing the questionnaires was taken from Siberia Schering's Emotional Intelligence and the “library anxiety questionnaire.” For examining the research hypotheses, Pearson Correlation Test and stepwise regression were used. Based on the present study's findings, the average emotional intelligence scores of students of Azarbaijan Shahid Madani University and Tabriz University were above-average, i.e. 3.003 and 3.169, respectively. The correlation of the coefficients of emotional intelligence and library anxiety turned out to be -0.38. Also, the multiple regression analysis results showed that 17% of the changes in library anxiety can be predicted or revealed based on students’ emotional intelligence, and that 83% percent of them depend on other reasons. The findings of this research can raise the awareness about the status of students’ emotional intelligence and library anxiety among officials of Azarbaijan Shahid Madani University and Tabriz University and help with future decision-making and planning. https://dorl.net/dor/20.1001.1.20088302.2022.20.1.1.

The study of the contamination extent in jewelry used by nursing staff and its relation to some factors in Shahrekord Medical University hospitals

Background and aims: In clinics, nurses spend most of their working shifts « hours with different if they do not follow the general principals of prevention, they will have greater share of transmission of infection to patients and others. Therefore, the aim of this study is determining the extent of contamination in nursing staff's jewelry and their level of awareness of using these objects in Shahrekord medical university hospitals. Methods: This study is a analytical discriptive study which was done on 220 nurses, who were selected by cluster sampling, in Shahrekord hospitals. For data collection, a questionnaire included demographic information and assessment of the level of awareness about the terms of using jewelries in clinical environments was used. The reliability of this questionnaire was obtained 0.86 based on Cronbach's alpha, and confirmed. After that, the nurses' palms and the jewelry used by them were sampled and cultured in vitro. After 48-72 hours, the samples were examined and the data were analyzed by the use of SPSS Software and descriptive and inferential statistical techniques such as Cross tab. Results: The results of this study revealed that only 7 (3.2%) of the participants did not use jewelry in the workplace. In the in vitro cultures from the jewelry used by participants in the study, Staphylococcus Epidermidis had the highest frequency and candida albikans had the lowest frequency. Also, the materials used in watches had a significant role in the level of microbial contamination but this relationship is not seen in other jewelry. (r=259.84, P=0.000). There is a relationship between the gender of the participants and the level of microbial contamination in the watches. (r=24.913, P=0.000) This relationship is more in women comparing with men. Although more than 75% of nurses have a good awareness of the abandonment of using the jewelry in clinical areas, yet the use of jewelry is at a considerable extent. Conclusion: The results of this study show that the hands and various types of jewelry used by nurses are contamined with various pathogens, while applying the defined professional principles for controlling infection is the most important principles of clinical programs. Therefore, the results of this study can be used by the managers of the hospitals to emphasize more desirably on the compliance of the rules and regulations for controlling of infection in clinical environments

The study of the contamination extent in jewelry used by nursing staff and its relation to some factors in Shahrekord Medical University hospitals

Background and aims: In clinics, nurses spend most of their working shifts « hours with different if they do not follow the general principals of prevention, they will have greater share of transmission of infection to patients and others. Therefore, the aim of this study is determining the extent of contamination in nursing staff's jewelry and their level of awareness of using these objects in Shahrekord medical university hospitals. Methods: This study is a analytical discriptive study which was done on 220 nurses, who were selected by cluster sampling, in Shahrekord hospitals. For data collection, a questionnaire included demographic information and assessment of the level of awareness about the terms of using jewelries in clinical environments was used. The reliability of this questionnaire was obtained 0.86 based on Cronbach's alpha, and confirmed. After that, the nurses' palms and the jewelry used by them were sampled and cultured in vitro. After 48-72 hours, the samples were examined and the data were analyzed by the use of SPSS Software and descriptive and inferential statistical techniques such as Cross tab. Results: The results of this study revealed that only 7 (3.2%) of the participants did not use jewelry in the workplace. In the in vitro cultures from the jewelry used by participants in the study, Staphylococcus Epidermidis had the highest frequency and candida albikans had the lowest frequency. Also, the materials used in watches had a significant role in the level of microbial contamination but this relationship is not seen in other jewelry. (r=259.84, P=0.000). There is a relationship between the gender of the participants and the level of microbial contamination in the watches. (r=24.913, P=0.000) This relationship is more in women comparing with men. Although more than 75% of nurses have a good awareness of the abandonment of using the jewelry in clinical areas, yet the use of jewelry is at a considerable extent. Conclusion: The results of this study show that the hands and various types of jewelry used by nurses are contamined with various pathogens, while applying the defined professional principles for controlling infection is the most important principles of clinical programs. Therefore, the results of this study can be used by the managers of the hospitals to emphasize more desirably on the compliance of the rules and regulations for controlling of infection in clinical environments. Keywords: Infection, nurse, Jewelry, Awareness, Transmission of infection

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Self-Movement of Water Droplet at the Gradient Nanostructure of Cu Fabricated Using Bipolar Electrochemistry

This Article reports on gradient electrodeposition of copper on the surface of a bipolar electrode (BPE). The formation mechanism of the as-fabricated gradient nanostructure is discussed, and the effects of time, potential, and concentration of CuSO₄ solution on the morphology of the deposited structures are investigated. Scanning electron microscopy (SEM) is used to visualize the morphology of the deposited Cu at different positions of the BPE. By scanning from the cathodic pole to the midpoint of the BPE, three distinct structures are observed; (i) nanodendrites, (ii) nanodendrites in the vicinity of nanoparticles, and (iii) nanoparticles. The BPE surface was characterized by X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) measurements. The contact angle measurement of a water droplet reveals a surface with gradient wettability. Modification of the as-electrodeposited Cu surface with 1-dodecanethiol provides self-movement of the water droplet