It seems that just as quickly as Artificial Intelligence systems.docx

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It seems that just as quickly as Artificial Intelligence systems show promise in transforming how we work, live, drive, and even get treated by law enforcement, scholars and others question the ethics that surround these autonomous decision-making systems. The ethics of AI focuses on whether or not decisions are being made that discriminate against people on the basis of race, religion, sex, or other criteria. AI's profound bias problems have become public in recent years, thanks to researchers like Joy Buolamwini and Timnit Gebru, authors of a 2018 study that showed that face-recognition algorithms nearly always identified white males but recognized black women only two-thirds of the time. Consequences of that flaw can be serious if the algorithms cause law enforcement to discriminate when identifying suspects, or doctors use the algorithms to decide who to treat. The challenge for developers is to remove bias from AI, which is complicated because the system depends upon the data that goes into the system. Training data must be vast, diverse, and reflective of the population so that the AI system has a strong sample. Use this forum to discuss two examples of situations where bias can skew the data causing an AI system to discriminate against certain groups of people. How can fairness be built into the AI systems? Are the advantages that AI bring to a system worth the bias, if uncorrected? .

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SHOULD ALGORITHMS DECIDE YOUR FUTURE? This publication was prepared by Kilian Vieth and Joanna Bronowicka from Centre for Internet and Human Rights at European University Viadrina. It was prepared based on a publication “The Ethics of Algorithms: from radical content to self-driving cars” with contributions from Zeynep Tufekci, Jillian C. York, Ben Wagner and Frederike Kaltheuner and an event on the Ethics of Algorithms, which took place on March 9-10, 2015 in Berlin. The research was supported by the Dutch Ministry of Foreign Affairs. Find out more: cihr.eu/ethics-of-algorithms/ Follow the discussion on Twitter: #EoA2015 Graphic design by Thiago Parizi cihr.eu @cihr_eu 1 | ETHICS OF ALGORITHMS ETHICS OF ALGORITHMS | 2 WHAT IS AN ALGORITHM? ALGORITHMS SHAPE OUR WORLD(S)! Our everyday life is shaped by computers and our computers are shaped by algorithms. Digital computation is constantly changing how we commu- nicate, work, move, and learn. In short, digitally connected computers are changing how we live our lives. This revolution is unlikely to stop any time soon. Digitalization produces increasing amounts of datasets known as ‘big data’. So far, research focused on how ‘big data is produced and stored. Now, we begin to scrutinize how algorithms make sense of this growing amount of data Algorithms are the brains of our computers, mobiles, Internet of Things. Algorithms are increasingly used to make decisions for us, about us, or with us – oftentimes without us realizing it. This raises many questions about the ethical dimension of algorithms. WHY DO ALGORITHMS RAISE ETHICAL CONCERNS? First, let's have a closer look at some of the critical features of algorithms. What are typical functions they perform? What are negative impacts for human rights? Here are some examples that probably affect you too. THEY KEEP INFORMATION AWAY FROM US Increasingly, algorithms decide what gets attention, and what is ignored; and even what gets published at all, and what is censored. This is true for all kinds of search rankings, for example the way your social media news- feed looks. In other words, algorithms perform a gate-keeping function. EXAMPLE Hiring algorithms decide if you are invited for an interview. • Algorithms, rather than managers, are more and more taking part in hiring (and firing) of employees. Deciding who gets a job and who does not, is among the most powerful gate-keeping function in society. • Research shows that human managers display many different biases in hiring decisions, for example based on social class, race and gender. Clearly, human hiring systems are far from perfect. • Nevertheless, we may not simply assume that algorithmic hiring can easily overcome human biases. Algorithms might work more accurate in some areas, but can also create new, sometimes unintended, problems depending on how they are programmed and what input data is used. Ethical.

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In the wake of mass automation, UBIs might be the answer low-income families and citizens might be looking towards. As automation across industries increases, the induced fear within citizens of its impact is severe. From privacy concerns through rogue AI to doomsday scenarios to more realistic concerns of misused AI and loss of jobs, pop-culture led paranoia has shaken up the world. These concerns have to be dealt with, and tech companies and businesses need to have a robust moral framework under which decisions are made, to ensure any negative externalities of implementing AI are mitigated to the maximum degree. Artificial Intelligence is a great tool to optimize businesses and make our world more efficient, but the moral imperative on all of us is to ensure it happens sides by side human sustainability, not at its expense.

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Artificial intelligence (AI) is becoming more and more prevalent in our daily lives, and its potential applications are practically limitless. However, as with any technology, there are concerns about how AI could be misused or abused. One of the most serious concerns is the potential for discrimination, particularly against women or minorities, when AI systems are used for tasks like job hiring. Additionally, there are concerns about privacy and security, as AI could be used to monitor people's movements or launch cyberattacks. To address these concerns, regulations must be developed to ensure that AI is developed and used ethically and responsibly. These regulations should address issues like safety, privacy, security, and discrimination. Finally, it is important to educate the public about AI and how to use it safely and responsibly. In this paper, I will examine the AI regulations and challenges that exist today, particularly in the United States. Two regulations I will focus on are the AI in Government Act of 2020 and the National Artificial Intelligence Initiative Act of 2020. Additionally, I will examine two Executive Orders that have addressed the issue of AI in the federal government. Finally, I will conclude with some policy considerations and recommendations for federal agencies.

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The ethics of AI go beyond just the technology itself. When we consider AI within the complex web of digital platforms and services (the digital ecosystem), new ethical concerns arise. A big focus is on how AI decisions can be biased, reflecting the data it's trained on and potentially leading to discrimination. We also need to be mindful of privacy issues and how AI might be used to manipulate users. To ensure ethical AI in digital ecosystems, we need to consider these potential pitfalls during development and use frameworks to make responsible choices. This includes reflecting on the decision-making process and how AI can be used for good.

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Preventing biases and digital literacy with the "no-code-movement" What is the impact of sex and gender differences in Artificial Intelligence (AI) used in biomedicine and healthcare? How can AI biases threaten our health and mitigate inequalities? According to Dr. Antonella Santuccione Chadha, founder of the Women’s Brain Project, ambitious goals set by precision medicine will be achieved using the latest advances in AI to identify the role of inter-individual differences. Yet, we need to consider more than ever that our decisions significantly impact the future treatment of patients and society. Despite scientific progress, most biomedical solutions do not account for (neither desirable nor desirable) bias detection. Furthermore, most algorithms neglect the sex and gender factor and their relevance to individual variances in health and illness. Failure to account for these variations could result in errors and discriminatory consequences (Cirillo et al., 2020). A solution to this issue would be “no-code-movement,” which stands for a type of web development that allows non-programmers or people with great creative rather than technical skills to build immersive online interactions, such as 2D (Web 2.0) and 3D (Web 3.0) applications, and create software using a user-friendly graphical interface, instead of writing boring code. The no-code movement rests upon the fundamental belief that technology should enable and facilitate the creation and understanding and not be a barrier to entry. In this regard, user experience (UX) approaches can be taken to make it inclusive, adaptable, and customized for each sex and gender variation representation. Historical sex and gender inequities and prejudices can infect health research and practice (Cirillo et al., 2020). Thus, developing AI precision systems in healthcare will enable the differentiation of vulnerabilities for disease and response to treatments among individuals while avoiding discriminatory biases. Furthermore, the no-code movement allows visualizations, logical statements, and dimensionality reduction techniques, which can be implemented to achieve interpretability (Cirillo et al., 2020). Therefore, developing and applying fair approaches is critical for implementing unbiased and interpretable models for everyone. The no-code approach also saves money since it requires less upkeep and upfront investment. Coding money could now be better utilized for all sex and gender well-designed research, implying that post-trials are essential to scientific advancements (Rich-Edwards et al., 2018). Furthermore, it allows for decentralized autonomy and innovation. When decentralizing development–by removing the barriers preventing healthcare users and professionals from experimenting and testing–no-code platforms, autonomy, and innovation are encouraged. It also enables to be known inside and out, thus avoiding the “black box” effect. (...)

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Meeting or Beating Analyst Expectations in thePost-Scandals .docx

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Mental Status Examination: The patient is who is 70 years old who is forgetting things withing a short time which seem to be dementia and mood disorder. Since she has been working in the military for over 20 years, her condition is mainly associated with her worrying experiences and traumatic events in the warzone. She looks very presentable and smartly dressed thus her general appearance looks good She is communicating so well with a clear speech, report gets frustrated when she cannot remember things, alert to self and place disoriented to time, confused. She denies hallucination. despite being previously hospitalized for dementia and depression. However, she gets disrupted at some point during the conversation. Judgment is impaired, affect labile mood stress and anxious, can not care for self, behavior cooperative and disorganized, thought process tangential, thought content preoccupation, denies SI/HI, [removed] Case study CC (chief complaint)- Almost set the house on fire x2 times HPI: The patient is Alexis 70 years old woman who is complaining of dementia. She presents the signs and symptoms of dementia, and she claims having these condition for some time. She states that she has been forgetting things so easily and cannot recall anything Now she is 70 and has begun to have times when she does not know what day it is. Neighbor found her wandering around the neighborhood because she could not find her way home. Placed items on stove and forgot x2 times and forgot and almost burn the house.The patients states that the condition gets worse when she vividly recalls her services in the military from training to her retirement. The patient says that there is no history of dementia in her family and she had the same condition few years ago. Sister reported the family has concerns of living patient by herself her short-term memory has gotten worse. She was hospitalized for similar symptoms years ago and was placed on Aricept. Patient has mood swings, yell and scream for no reasons, delusional sometimes and behavior problem as reported by her sister, her husband death and son disability has made her condition worse, she has become aggressive and out of control. Tells stories of things that happens in the past over and over again and talks to herself. The patient was married to her late husband, and they had three children. She has been serving in the military for over 20 years and has been supporting the community through various activities. She started having a problem with memory loss and difficulty in solving issues. Husband death, daughter had an accident and died, son was shot in his arm and now has disable arm. Sister is her caregiver. Denies seeing things or hearing. Denies suicidal or homicidal ideation. Medical History: After being taken through various psychiatric test to determine her problems, it was proven that the patient has dementia.  Current Medications: Currently, Alexis is under medication where she takes and A.

Mental Status ExaminationThe patient is who is 70 years old who.docx

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MEMO Date: SEPTEMBER 29, 2022 To: CITY OF COLUMBUS MAYOR ANDREW GINTHER From: SUBJECT: MEMO & SWOT ANALYSIS OF THE COLUMBUS, OHIO POLICE DEPARTMENT EXECUTIVE SUMMARY The accompanying SWOT analysis was developed to identify levels of trust in the CPD, considering the high crime rate in Columbus, Ohio and the violence often connected to it. The youth and community interactions are highlighted as strengths and opportunities, whereas public distrust and crime are highlighted as weaknesses and threats, respectively. So, my suggestions for the CPD are to use crisis professionals in nonviolent situations (such mental health and homeless crises) and to give diversity in hiring top priority. BACKGROUND Crime in Columbus, Ohio, is so high that "more than 96% of the other municipalities [in Ohio] have a lower crime rate," making it one of the most dangerous cities in the country. Given the police's efforts to increase public safety, some may question the force's current standing. CPD's credibility has been damaged since the outbreak began as a result of the officers' fatally shooting of unarmed African Americans and the use of tear gas and rubber bullets on hundreds of protesters during the Black Lives Matter Movement (Wedd,2020). The U.S. Department of Justice has launched an investigation into the CPD because of these misdeeds, looking into their policies, procedures, and any racial prejudices. SWOT ANALYSIS After compiling the foregoing information, I developed the SWOT analysis of the CPD in the bellow attached. With the crime rate always on the rise and the public demanding action from law enforcement, one must wonder: do people still have faith in the Columbus Police Department? It is the goal of the SWOT analysis to shed light on the sources of distrust and the means by which trust can be reestablished in those sources. Youth engagement, fundraising prowess, and departmental effectiveness are all areas in which the CPD excels (Wedd,2020). There is a lot of mistrust in the public, and the reputation of executives and the workers are both falling. They have potential in attracting a more diverse workforce and gaining the trust of young people and communities. An increase in violent crime, public discontent, and reluctance on the part of the public to call the police will pose serious challenges for the Columbus Police Department. RECOMMENDATIONS First, in situations where police intervention is unnecessary (such as nonviolent mental health crises, suicide threats, and homeless crises), I advocate for the use of trained crisis experts rather than solely police personnel. specially at this time when public faith in law enforcement is low and crime rates are high, deploying police to areas where they are most needed and appreciated can make communities safer overall. In addition, many people think "police officers and police organizations are incapable of repairing themselves" because of policing's racist origins. Additionally, 73% of.

MEMODate SEPTEMBER 29, 2022 To CITY OF COLUMBUS MA.docx

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Memo To: Sally Jones From: James Student Date: January 15, 202x Subject: Travelers for the <insert your city here> trip Below is the current status of the <city> trip: Create your table below this line and then delete this line. Create your chart below this line and then delete this line. 1. Saint Leo Portal login User ID:[email protected] Saintleo\martha.ramsey Password: Demonte5!!! 2. New Login for email through Okta User ID: Martha.ramsey Password: Demonte5!!! 3. What did you earn your first medal or award for? Art class 4. Lion Share Courses 5. Research Method II .

Memo ToSally JonesFromJames StudentDate Ja.docx

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Metabolic acidosis: A decrease in serum HCO3 of less than 24 mEq/L and an increase in the hydrogen ion concentration in the systemic circulation (Burger & Schaller, 2022). Pathophysiology: This occurs when non-carbonic acid concentrations rise, bicarbonate (base) is lost from extracellular fluid, or the kidneys are unable to replenish it. This can happen suddenly, as in the case of lactic acidosis brought on by inadequate circulation or hypoxemia, or more gradually, as in the case of renal failure (failure to excrete acid) or diabetic ketoacidosis (excess production of keto acids from lack of insulin) (Huether & McCance, 2014). Clinical manifestations: Alteration in the neurologic, respiratory, gastrointestinal, and cardiovascular systems are signs of metabolic acidosis. Initial symptoms of severe acidosis include lethargy and a headache, which can escalate to a coma. Respiratory compensation is indicated by deep, fast breathing (Kussmaul breaths). It's typical to have anorexia, nausea, vomiting, diarrhea, and abdominal pain. Extreme acidosis may make it harder for the heart to contract normally and result in potentially fatal dysrhythmias (Huether & McCance, 2014). Evaluation and treatment: Lab results, symptoms, and medical history are used to make the definitive diagnosis of metabolic acidosis. The results of the laboratory tests will reveal arterial blood pH below 7.35 and bicarbonate concentration below 24 mEq/L. A movement to the right can be seen in the oxyhemoglobin curve. For a buffering solution to be used effectively, the underlying issue must be identified. Administration is necessary to raise the pH to a safe level during severe acidosis (pH 7.1), especially if there is renal failure. Deficits in water and sodium must also be made up (Huether & McCance, 2014). Metabolic alkalosis: An initial rise in serum bicarbonate, which causes a blood pH increase to >7.45 (Tinawi, 2021). Pathophysiology: Increased bicarbonate concentration, which is often brought on by an excessive loss of metabolic acids. Prolonged vomiting, stomach suctioning, a high intake of bicarbonate, hyperaldosteronism with hypokalemia, and diuretic medication are all conditions that might lead to metabolic alkalosis (Huether & McCance, 2014). Clinical Manifestations: Volume loss and electrolyte deficits are linked to several common symptoms including weakness, cramping, and overactive reflexes. Some people may have paresthesias, tetany, and seizures. To maximize carbon dioxide retention, respirations are shallow and sluggish. With severe alkalosis, disorientation and seizures happen. A potential issue is atrial tachycardia. As oxyhemoglobin's dissociation decreases and the likelihood of dysrhythmias rises, the oxyhemoglobin curve shifts to the left (Huether & McCance, 2014). Evaluation and treatment:A sodium chloride solution is necessary for the treatment of contraction alkalosis or hypochloremic alkalosis. As a result, HCO3 may be expelled as NaHCO3 in the urine .

Metabolic acidosis A decrease in serum HCO3 of less than 24 mEqL.docx

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McDonald’s—The Coffee Spill Heard ’Round the World The McDonald’s coffee spill is the most famous consumer lawsuit in the world. Everyone knows about this case, and the details involved in it continue to be debated in many different venues—classrooms, Web sites, blogs, law schools, and business schools. Regardless, it serves as one of the best platforms in the world for discussing what companies owe their consumer stakeholders and what responsibilities consumers have for their own well-being. Consumers, lawyers, and analysts are still debating the world famous coffee spill case. Keeping the topic hot was the 2011 documentary film, Hot Coffee , which analyzed the famous coffee spill, set the facts straight, and highlighted the ongoing debate about the impact of tort reform on the U.S. judicial system. The film premiered at the 2011 Sundance Film Festival and aired on HBO during June 2011. The film won many awards. Stella Liebeck Stella Liebeck and her grandson, Chris Tiano, drove her son, Jim, to the airport 60 miles away in Albuquerque, New Mexico, on the morning of February 27, 1992. Because she had to leave home early, she and Chris missed having breakfast. Upon dropping Jim off at the airport, they proceeded to a McDonald’s drive-through for breakfast. Stella, an active, 79-year-old, retired department-store clerk, ordered a McBreakfast, and Chris parked the car so she could add cream and sugar to her coffee. What occurred next was the coffee spill that has been heard ‘round the world. A coffee spill, serious burns, a lawsuit, and an eventual settlement made Stella Liebeck (pronounced Lee-beck) the “poster lady” for the bitter tort reform discussions that have dominated the news for more than 20 years. To this day, the issue is still debated, with cases similar to Stella’s continuing to be filed. Third-Degree Burns According to Liebeck’s testimony, she tried to get the coffee lid off. She could not find any flat surface in the car, so she put the cup between her knees and tried to get it off that way. As she tugged at the lid, scalding coffee spilled into her lap. Chris jumped from the car and tried to help her. She pulled at her sweatsuit, but the pants absorbed the coffee and held it close to her skin. She was squirming as the 170-degree coffee burned her groin, inner thigh, and buttocks. Third-degree burns were evident as she reached an emergency room. A vascular surgeon determined she had third-degree (full thickness) burns over 6 percent of her body. Hospitalization Following the spill, Liebeck spent eight days in the hospital and about three weeks at home recuperating under the care of her daughter, Nancy Tiano. She was then hospitalized again for skin grafts. Liebeck lost 20 pounds during the ordeal and at times was practically immobilized. Another daughter, Judy Allen, recalled that her mother was in tremendous pain both after the accident and during the skin grafts. According to a Newsweek magazine report, Liebeck wro.

McDonald’s—The Coffee Spill Heard ’Round the WorldThe McDonald’s.docx

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may use One of the following formats for reflection.; all concepts from the reflection model MUST be utilized : Johns' Model of Reflection Carper's Ways of Knowing Sister Roach's Six C's of Caring Boud's Model of Reflection Q: Although many believe (and behave!) as if the COVID-19 pandemic has resolved, it has not. What are your thoughts as a new nurse entering the profession during a COVID-19 pandemic crisis and critical shortage of nurses in acute care facilities? What are some skills/knowledge acquired through your nursing education that can assist you into your transition to professional practice? .

may use One of the following formats for reflection.; all conc.docx

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master budget problem. only part B in attached file Part B: Master Budget You have just been hired as a new management trainee by Earrings Unlimited, a distributor of earrings to various retail outlets located in shopping malls across the country. In the past, the company has done very little in the way of budgeting and at certain times of the year has experienced a shortage of cash. Since you are well trained in budgeting, you have decided to prepare a master budget for the upcoming second quarter. To this end, you have worked with accounting and other areas to gather the information assembled below. The company sells many styles of earrings, but all are sold for the same price—$10 per pair. Actual sales of earrings for the last three months and budgeted sales for the next six months follow (in pairs of earrings): January (actual) 30,000 June (budget) 45,000 February (actual) 20,000 July (budget) 40,000 March (actual) 50,000 August (budget) 30,000 April (budget) 70,000 September (budget) 20,000 May (budget) 95,000 Sufficient inventory should be on hand at the end of each month to supply 40% of the earrings sold in the following month. Suppliers are paid $3 for a pair of earrings. 40% of a month’s purchases is paid for in the month of purchase; the other 60% is paid for in the following month. All sales are on credit. Only 30% of a month’s sales are collected in the month of sale. An additional 60% is collected in the following month, and the remaining 10% is collected in the second month following sale. Monthly operating expenses for the company are given below: Variable: Sales commissions 5% of sales Fixed: Advertising $ 190,000 Rent $ 20,000 Salaries $ 100,000 Utilities $ 8,000 Insurance $ 3,000 Depreciation $ 14,000 Insurance is paid on an annual basis, in November of each year. At the end of June, the company received $4,000 deposit for July sales. Sales in advance is a liability. The company plans to purchase $20,000 in new equipment during May and $60,000 in new equipment during June; both purchases will be for cash. The company declares dividends of $15,000 each quarter, payable in the first month of the following quarter. The company’s balance sheet as of March 31 is given below: Assets Cash $ 74,000 Accounts receivable ($20,000 February sales; $350,000 March sales) 370,000 Inventory 80,000 Prepaid insurance 21,000 Property and equipment (net) 950,000 Total assets $ 1,495,000 Liabilities and Stockholders’ Equity Accounts payable $ 100,000 Dividends payable 15,000 Common stock 800,000 Retained earnings 580,000 Total liabilities and stockholders’ equity $ 1,495,000 The company maintains a minimum cash balance of $50,000. All borrowing is done at the beginning of a month; any repayments are made at the end of a mont.

master budget problem. only part B in attached filePa.docx

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MAT 133 Milestone One Guidelines and Rubric Overview: The final project for this course is the creation of a research study report. For the first milestone, you need to select an appropriate study from the Final Project Research Study Options document found in the Module One Reading and Resources. Then you will draft the first of three sections that will make up your report. Prompt: Draft the “Introduction” section of your research study report, which includes the following critical elements: I. Identify the specific focus of the research. In other words, what was this study about? II. Explain the purpose of the study. What was the study trying to achieve? III. Describe the specific characteristics of the group being studied. What was the population? What was the sample size? What were its demographics? Submit your Milestone One submission to the assignment page in Module Two. You will be graded based on the rubric information below.  You will also be sharing your ideas for your introduction to the discussion board for feedback. Make sure to respond to your peers with thoughts and information to help them improve their work for the final project submission (later in the course). Rubric Guidelines for Submission: Your draft of the “Introduction” section of your research study report should be 1 page in length (plus a cover page and references) and must be written in APA format. Use double spacing, 12-point Times New Roman font, and one-inch margins. Cite all references in APA format. Note: This rubric is tailored to this assignment and awards full points for “Proficient.” For the final project, you will need to demonstrate “Exemplary” achievement to earn full points. To see how you will be graded on your final project, review the Final Project Guidelines and Rubric document (in the Assignment Guidelines and Rubrics section of the course). Critical Elements Proficient (100%) Needs Improvement (75%) Not Evident (0%) Value Introduction: Focus Accurately identifies the specific focus of the research Identifies the focus of the research, but is not fully accurate or lacks specificity Does not identify the focus of the research 25 Introduction: Purpose Accurately explains the purpose of the study Explains the purpose of the study, but is not fully accurate or lacks specificity Does not explain the purpose of the study 25 Introduction: Group Accurately describes the specific characteristics of the group being studied Describes the characteristics of the group being studied, but is not fully accurate or lacks specificity Does not describe the characteristics of the group being studied 25 Articulation of Response Submission has no major errors related to citations, grammar, spelling, syntax, or organization Submission has major errors related to citations, grammar, spelling, syntax, or organization that negatively impact readability and ar.

MAT 133 Milestone One Guidelines and Rubric Overview .docx

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Master of Business Analytics BUS5AP La Trobe Business School 1 BUS5AP – Business Application of Analytics Assignment 03: “Analyse This!” – Bring your user stories to life with Power BI Assignment Type: Individual Marks: 40% Release Date: Wednesday, 05-Oct-2022 Due Date: Saturday, 12 November 2022 23:55 (AEDT) – submission link on LMS “Analyse This!” – Bring your user stories to life with PowerBI Having successfully completed your analysis of user stories, you have been requested by HOLISTICO to deliver a prototype dashboard. You will use your experience gained from Week 05 (Think like a designer) and the Power BI content from Week 8 to Week 12 to develop a visually compelling dashboard that encompasses user stories completed in Assignment 02. You will then pitch this dashboard to HOLISTICO’s team to show them the value of the analytics exercise they have undertaken with you as you uncover insights about their operation and direction. A sample dataset for this assessment is provided in the spreadsheet (BUS5AP-HOLISTICO-AS03- Dataset.xlsx) on the LMS under Assignment 03. Deliverables: 1. Complete the spreadsheet template, with the first column showing a list of user stories from Assignment 02 and the second column noting if this user story is prototyped in your dashboard. 2. A 15-minute (exactly 15 minutes) video recording of you presenting your dashboard and how this dashboard delivers on the user stories presented in Assignment 02. o You may use additional material such as a slide deck to present your dashboard (especially when linking the dashboard to the user stories), and you must be visible in the recording to deliver the pitch. o The presentation must cover the following: ▪ How your dashboard delivers on the value drops specific in your business case and the user stories from assignment 02 (both the Summary and Operational level dashboard) – it is acceptable to make changes to your value drops and user stories in the event that it doesn’t align with the dashboards. ▪ A walkthrough of each of the elements of the dashboards (and how it delivers on a selected user story) o You may use Zoom screen recording feature to capture you dashboard pitch. For a step-by-step guide to Zoom screen recording, follow this link. 3. A PowerBI file in PBIX format covering the content above. The PowerBI dashboard must consist at minimum of the following: o A strategic level dashboard highlighting organisation trends (ideally aligned to the Clinic Owner). o An Operational level dashboard that allows personas such as the Clinic Owner Practitioner and the Practice Managers’ to gain insights into specific patient trends. o Each of the dashboards will be located on the same PowerBI PBIX file under multiple tabs. You can have more than 2 tabs but less than 4 tabs. https://www.guidingtech.com/record-your-screen-with-zoom-windows-10/ Master of Busine.

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Program code examples (known also as worked examples) play a crucial role in learning how to program. Instructors use examples extensively to demonstrate the semantics of the programming language being taught and to highlight the fundamental coding patterns. Programming textbooks allocate considerable space to present and explain code examples. To make the process of studying code examples more interactive, CS education researchers developed a range of tools to engage students in the study of code examples. These tools include codecasts (codemotion,codecast,elicasts), interactive example explorers (WebEx, PCEX), and tutoring systems (DeepTutor). An important component in all types of worked examples is code explanations associated with specific code lines or code chunks of an example. The explanations connect examples with general programming knowledge explaining the role and function of code fragments or their behavior. In textbooks, these explanations are usually presented as comments in the code or as explanations on the margins. The example explorer tools allow students to examine these explanations interactively. Tutoring systems, which engage students in explaining the code, use these model explanations to check student responses and provide scaffolding. In all these cases, to make a worked example re-usable beyond its presentation in a lecture, the explanations have to be authored by instructors or domain experts i.e., produced and integrated into a specific system. As the experience of the last 10 years demonstrated, these explanations are hard to obtain. Those already collected are usually “locked” in a specific example-focused system and can’t be reused. The purpose of this working group is to support broader re-used of worked examples augmented with explanations. Our current plan is to develop а standard approach to represent explained examples. This approach will enable an example created for any of the existing systems to be explored in a standard format and imported into any other example-focused system. We plan to follow a successful experience of the PEML working group focused on re-using programming exercises.

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1. It seems that just as quickly as Artificial Intelligence systems show promise in transforming how we work, live, drive, and even get treated by law enforcement, scholars and others question the ethics that surround these autonomous decision- making systems. The ethics of AI focuses on whether or not decisions are being made that discriminate against people on the basis of race, religion, sex, or other criteria. AI's profound bias problems have become public in recent years, thanks to researchers like Joy Buolamwini and Timnit Gebru, authors of a 2018 study that showed that face-recognition algorithms nearly always identified white males but recognized black women only two- thirds of the time. Consequences of that flaw can be serious if the algorithms cause law enforcement to discriminate when identifying suspects, or doctors use the algorithms to decide who to treat. The challenge for developers is to remove bias from AI, which is complicated because the system depends upon the data that goes into the system. Training data must be vast, diverse, and reflective of the population so that the AI system has a strong sample. Use this forum to discuss two examples of situations where bias can skew the data causing an AI system to discriminate against certain groups of people. How can fairness be built into the AI systems? Are the advantages that AI bring to a system worth the bias, if uncorrected?

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