Allotment A (65 marks) Answer all questions about this fragment. Point to: You wish to answer ALL questions about this fragment. Answers must serene be supported with theoretical items and ideas from the suggested textbook, Lewicki, R. J., Saunders, D. M. & Barry, B. (2021). Necessities of Negotiation (Seventh ed). Unusual York: McGraw-Hill Worldwide Model and course materials. An skill to habits correct research, provide relevant referencing of sources and demonstrate crucial pondering are all necessary abilities that our students must develop. Subsequently, marks are awarded for appropriate research effort and correct referencing. Less marks would possibly be awarded to students who merely extract data from their reference sources without demonstration of crucial prognosis. It is a must-wish to enhance your solutions with particular examples in step with the pronounce of affairs given within the case gape below to illustrate your working out and utility of the related ideas. That you just might perchance well make life like assumptions, as segment of your prognosis. Please peep the wordcount restrict for every segment of the depend on and pronounce the wordcount at the end of your answer. Non-compliance with these wordcount requirements will consequence in stamp deductions. The case below is extracted from Harvard Trade Overview Case Perceive \u201cCirculation Instant, nonetheless without Bias: Ethical AI Pattern in a Commence-up Custom\u201d. \u201cPromoted?\u201d repeated Taylor, incredulous at the phrases that Darnell, Catalise\u2019s chief product officer, had appropriate stated. \u201cFully, Taylor,\u201d Darnell replied. \u201cYou\u2019ve undoubtedly proven your self as a product manager here at Catalise, and we know you\u2019ll cease mammoth work as a senior product manager.\u201d Darnell ended the resolution by briefing Taylor on her salary adjustment and developing a time to glue on the first venture in her new operate, which Darnell hinted might perchance perchance perchance be a huge one. Taylor closed the video chat, spun round in her chair, and let the info sink in. As a 29-year-frail girl, Taylor might perchance perchance perchance be the youngest senior product manager at Catalise, and the finest girl. Eighteen months before, when she had joined the originate-up, Catalise modified into once serene in its infancy, promoting customers and customers alike on the far-fetched thought that man made intelligence (AI) plan might perchance perchance perchance diagnose patients\u2019 mental health stipulations by analysing their speech patterns. Since then, Catalise had launched Catalisence, a narrate-based mostly fully mostly diagnostic plan for schizophrenia that had been adopted by an estimated 40% of mental health clinics within the US. Catalisence\u2019s success propelled over $300 million in venture capital funding for the corporate, attracted partnership alternatives from prestigious hospitals and universities, and fueled rumors of a Catalise IPO within the near future. Firm leaders had been serious about bringing more diagnostic merchandise to market in portray to retain the momentum going. The week after her promotion, Darnell briefed Taylor on her first venture as a senior product manager. Catalise modified into once gearing up to delivery Catalisten, an AI-based mostly fully mostly plan that might perchance perchance perchance diagnose americans with critical depressive disorder (MDD) by analysing their speech. As a result of the incidence of MDD \u2013 roughly 21 million American citizens experienced a critical depressive episode every year [1] \u2013 Catalise expected Catalisten to be its most successful product for the subsequent several years. As a consequence, management sequestered Catalisten builders from assorted product groups and research companions to avoid any competitive leaks. Catalisten modified into once 80% market ready, Darrell estimated, nonetheless the physique of workers wished Taylor to coordinate the closing steps before delivery. Darnell also shared that Catalise\u2019s CEO modified into once pushing to expedite Catalisten\u2019s delivery time line, in portray to ensure Catalisten did no longer plunge within the abet of any of its competitors, which management believed had the same merchandise in style. Write My Project Rent a Professional Essay & Project Writer for polishing off your Tutorial Assessments Native Singapore Writers Personnel 100% Plagiarism-Free Essay Best Pleasure Price Free Revision On-Time Transport Just a few days later, Taylor had a video name with Eduardo, the lead AI engineer on Catalisten\u2019s style physique of workers. The 2 reviewed Catalisten\u2019s manufacturing time line. At the CEO\u2019s quiz, Eduardo had moved Catalisten\u2019s delivery date to March 1, three weeks away. Eduardo outlined that the physique of workers modified into once in its making an are trying out and validation segment, making an are trying out the efficiency of the plan\u2019s AI mannequin; however, some tests had been being abbreviated to suit contained within the expedited delivery time line. To lend a hand gain in maintain a watch on, Taylor requested to envision the done test results, whereas Eduardo and the physique of workers pushed ahead with their final work. As Taylor reviewed the test results, she illustrious that Catalisten\u2019s AI plan misclassified female narrate samples as having MDD in 19% of test cases, nonetheless misclassified male narrate samples as having MDD in simply 2.1% of test cases. Taylor modified into once fervent; groundless-certain diagnoses might perchance perchance perchance consequence in pointless clinical therapies and expenses for patients. This chance modified into once even more pronounced for women folks; early research suggested that girls folks had been more likely to be overdiagnosed with MDD and overprescribed psychotropic medication. The next day to come, Taylor called Eduardo and outlined her findings. Eduardo sighed. If the physique of workers had been to address the groundless-certain charge for women folks, he outlined, it might perchance possibly perchance possibly add a minimal of six weeks to Catalisten\u2019s delivery time line, upsetting the CEO and possibly endangering the corporate\u2019s competitive advantage. Eduardo informed Taylor to let the scenario sail. \u201cYou\u2019ve given me a lot to mediate of, Eduardo. Let me gain abet to you,\u201d Taylor stated. She exited the meeting and went abet to envision her notes. Background: AI, Natural Language Processing, and Medical Diagnosis First developed within the Fifties, AI modified into once a computer science subject serious about designing computer programs and equipment that might perchance perchance perchance impress duties as effectively as, or greater than, humans. By 2022, the subject had developed to the point where everyday applied sciences feeble AI to total duties equivalent to language processing (e.g., translating movies), image recognition (tagging photos), and situational planning (mapping out using routes). There had been many technical subfields inner AI, one of which modified into once pure language processing (NLP). NLP serious about growing plan that might perchance perchance perchance interpret human language. NLP plan algorithms took enter data from speech or textual command material and analysed it by comparing it to used patterns in grammar, semantics, and tone. From there, the analysed data would possibly be output as closed captioning, translated in precise time into one other language, or feeble to trigger a tool portray\u2014equivalent to a narrate assistant interpreting human speech to construct a timer or allotment the weather forecast. Often, NLP algorithms had been feeble alongside with assorted AI algorithms to total a fancy string of duties in step with a single narrate or textual command material enter. By analysing speech and textual command material patterns, NLP algorithms is also feeble to command diagnoses of mental health and neurological stipulations. Day to day conversations included key cues about an particular particular person\u2019s mental and neurological states, together with speech complexity, semantic coherence (i.e., the spend of phrases within the ethical context), and mood valence (emotional tone of narrate). When an particular particular person\u2019s free speech modified into once jog thru a particularly professional NLP algorithm, the plan might perchance perchance perchance calculate a statistical chance of that particular person experiencing a certain mental or neurological situation, in step with a weighted combination of those cues. In 2022, NLP plan modified into once feeble within the prognosis and management of stipulations together with MDD, postpartum despair, post-annoying stress disorder, schizophrenia, psychosis, suicidal ideation, soft cognitive impairment, and dementia. On epic of NLP algorithms subtle their calculations after every new exposure to data, these diagnostic instruments might perchance perchance perchance, over time, outperform clinical doctors\u2019 classifications. NLP instruments would possibly be display in hospitals and clinical doctors\u2019 locations of work, where they interpreted speech and textual command material from doctor\u2019s appointments, electronic health document notes, or the affected person\u2019s bedside. They had been also feeble in outpatient settings, analysing language from affected person portals, clinical apps, social media boards, and narrate assistants. AI Product Pattern Taylor and the Catalise physique of workers had been the spend of a frail AI style course of to construct up out Catalisten\u2019s NLP plan. A frail AI product style course of adopted the workflow in Figure 1 and modified into once overseen by a senior product manager. Figure 1. AI product style workflow The major step on this style float modified into once to clearly elaborate a subject for the AI product to resolve. At this stage, the product manager would oversee a physique of workers of particular person expertise (UX) researchers, who would habits interviews with prospective customers to learn more about their on each day foundation lives and the challenges they confronted. In step with these interviews and various research exercises, UX researchers would put together a particular subject statement for the AI product to resolve, which would then be feeble to manual the physique of workers within the next stages of style. Buy Custom Answer of This Overview & Raise Your Grades Win A Free Quote Next, the physique of workers\u2019s data scientists would originate gathering data to coach the product\u2019s AI algorithm(s), collectively identified as the AI mannequin. Files scientists would gape data items that the majority clearly matched instances specified contained within the subject statement. Files scientists also had to note out to make a different data items that had been no longer skewed by classes luxuriate in accelerate (e.g., together with more data from white americans than Murky americans) and gender (e.g., together with more data from men than women folks). After a suite modified into once chosen, data scientists would \u201ceasy\u201d the info, meaning they’d remove any incomplete or mis-enter parts. Then they’d \u201cannotate\u201d it, or review data parts and selectively remove some data in portray to balance class distribution contained within the info build. While data scientists ready the info build, AI engineers would form and write the code for the AI mannequin to operate. As soon as a mannequin modified into once coded and its data build ready, AI engineers would \u201ceducate\u201d the mannequin. Practicing fervent letting the AI mannequin explore the cleaned and annotated data build to title patterns. In step with the patterns it observed, the AI mannequin would originate to predict which data aspects predicted certain outcomes. As the mannequin grew to was more assured in its predictions, it might perchance possibly perchance possibly originate to imply choices for assorted data parts in step with the aspects it recognised. As soon as an AI mannequin modified into once sufficiently professional on the cleaned and annotated data, the physique of workers\u2019s AI engineers would originate to \u201ctest\u201d the mannequin by introducing it to data items that more carefully resembled the messy and complex data display within the precise world. Every time the mannequin stumbled (i.e., its predictions dropped in accuracy), the physique of workers would regroup and put together new practising data for the mannequin to learn from. This making an are trying out and validation cycle would continue till the physique of workers certain that the mannequin modified into once ready for deployment into precise-world settings. Evaluating Catalisten: Files Bias and Mannequin Accuracy When Taylor joined the Catalisten physique of workers, Eduardo and his engineers had been actively making an are trying out and validating the product\u2019s AI mannequin. And, as Eduardo talked about, the physique of workers modified into once abbreviating this step as segment of Catalisten\u2019s expedited delivery. Taylor wondered which parts within the near course of had allowed for the discrepancy between female and male groundless-certain charges. She pored over the physique of workers notes from all phases of Catalisten\u2019s style course of and found out two necessary inflection parts. First, within the info preparation segment, Catalisten\u2019s data scientists reported recount compiling a practising data build that presented a balanced number of female and male patients. The total affected person data sources to which the Catalisten physique of workers had access presented enormously greater percentages of fellows than women folks. And, because Catalisten\u2019s physique of workers modified into once separated from colleagues and research companions to avoid competitive leaks, its data scientists might perchance perchance perchance no longer reach out to health programs or research companions for greater data items. Buried within the physique of workers\u2019s workflow chat logs, Taylor found out a reward from the physique of workers\u2019s head data scientist confirming a gender bias within the info build, whereas also indicating that the women folks represented had the next-than-life like charge of MDD. The head data scientist warned that, if professional on these data, Catalisten\u2019s AI mannequin might perchance perchance perchance be more likely to overdiagnose women folks with MDD. Nonetheless, given timing constraints, Eduardo\u2014who labored as the intervening time Catalisten product lead before Taylor\u2019s arrival\u2014suggested the info science physique of workers to continue with the info build despite its bias. If Catalisten\u2019s mannequin did \u201clearn\u201d this bias all the intention thru its practising, Eduardo promised that it might perchance possibly perchance perchance be addressed within the making an are trying out and validation stages. Taylor scrolled additional down the chat logs to head making an are trying what took location subsequent. When Catalisten\u2019s mannequin entered its making an are trying out and validation segment, engineers confirmed that it presented the next charge of groundless positives for women folks than for men. This finding sparked a heated dialog amongst engineering physique of workers individuals within the chat log in terms of precision and recall, two assorted evaluative metrics for AI mannequin accuracy. Precision answered the depend on, \u201cWhen the mannequin makes a prediction, how likely is that prediction to be ethical?\u201d It modified into once calculated by dividing the mannequin\u2019s number of correct positives by all positives, and it modified into once greater when the number of groundless positives modified into once low. Recall, on the various hand, answered the depend on, \u201cHow appropriate is a mannequin at identifying proper occurrences of objects within the info?\u201d It modified into once calculated by dividing the mannequin\u2019s correct positives by its correct positives plus groundless negatives, and it modified into once greater when the number of groundless negatives modified into once low. Precision and recall metrics had been most often in tension, and AI product groups had to think the influence of every metric on mannequin efficiency. If the Catalisten physique of workers had been to prioritise precision, the AI mannequin might perchance perchance perchance be more conservative in its MDD diagnoses; it might perchance possibly perchance possibly comprise an extraordinarily low charge of groundless positives nonetheless would also omit some MDD cases (i.e., present the next number of groundless negatives). If the Catalisten physique of workers had been to prioritise recall instead, the AI mannequin would diagnose a increased number of MDD cases nonetheless would also falsely diagnose some americans (i.e., present the next number of groundless positives). Catalisten\u2019s chat logs captured the abet-and-forth between engineers as they discussed whether or no longer to prioritise the product\u2019s precision or recall. The product had a high recall, precisely diagnosing all MDD cases nonetheless also diagnosing some americans with MDD who did no longer comprise the situation. Just a few engineers proposed that prime recall modified into once a appropriate thing, guaranteeing that all americans who wished MDD medication bought it. Excessive recall also presented a stable industry case for Catalisten, since mental health sanatorium customers would completely watch an uptick in diagnoses made \u2013 using more income from affected person procedures and prescriptions. Other engineers pointed out that Catalisten\u2019s MDD overdiagnoses, notably for women folks, would consequence in patients being prescribed pointless medications, which might perchance perchance perchance negatively have an effect on their emotional steadiness, social interactions, and even ethical rights. The chat logs confirmed that Eduardo had made the closing physique of workers resolution. Excessive recall modified into once a certain operate for Catalisten\u2019s customers, Eduardo illustrious, and reducing Catalisten\u2019s groundless-certain charge for women folks would require more making an are trying out and validation \u2013 pushing abet the initiating time line. \u201cLet\u2019s no longer plan to any extent additional consideration to the 19% groundless-certain charge for women folks,\u201d he wrote. \u201cAny fallout originate from the groundless-certain discrepancy isn\u2019t our subject. It\u2019s within the fingers of our customers who write the prescriptions \u2013 they know that every applied sciences lift risks. Our job is to gain this product to market as posthaste as we are capable of.\u201d Choices Transferring Forward Taylor took a lag to course of everything she had realized since joining the Catalisten physique of workers. She mirrored on her dialog with Eduardo and sympathised alongside with his expertise as Catalisten\u2019s intervening time lead. Commence-usawere notorious for promoting a \u201ccirculation posthaste\u201d mentality, and Catalise modified into once no exception in prioritising jog to market. Taylor understood that Eduardo had sacrificed necessary steps to address the gender bias in practising data and chosen no longer to ethical the mannequin\u2019s gender bias all the intention thru the making an are trying out and validation phases because he confronted outsized pressure from management to expedite the Catalisten delivery. Yet Taylor also knew that Catalise\u2019s company culture prioritised going above and past for patients. If Catalisten grew to was the groundbreaking success that management hoped it might perchance possibly perchance perchance be, it modified into once only a subject of time before the public uncovered its high groundless-certain prognosis charge for women folks. So, if Catalisten launched without addressing the scenario, no longer only would it no longer chance female patients\u2019 health, nonetheless it completely would also jeopardise the corporate\u2019s standing and that of its staff. Taylor didn\u2019t opt her product management occupation or her teammates\u2019 careers to be tarnished by a rushed product delivery. She wished to counteract Eduardo\u2019s choices and convince Darnell to extend delivery. Caught with lots of homework assignments and feeling careworn ? Grasp professional tutorial assistance & Win 100% Plagiarism free papers Win A Free Quote [1] \u201cMain Despair,\u201d Nationwide Institute of Psychological Correctly being (NIMH), January 2022, https:\/\/www.nimh.nih.gov\/health\/statistics\/critical-despair (accessed Jul. 13, 2022). Question 1a The corporate proposes launching Catalisten, an AI-based mostly fully mostly plan that might perchance perchance perchance diagnose americans with critical depressive disorder (MDD) by analysing their speech. Nonetheless, Catalisten has a high groundless-certain charge for women folks that might perchance perchance perchance lead such patients to be prescribed pointless medication and be negatively affected in terms of emotions, social interactions and ethical rights. Taylor needs to convince Darnell, her chief product officer, to extend the product delivery date which is suitable three weeks away. Appraise the four (4) approaches to ethical reasoning (Desk 5.1 of the textbook) that Taylor can spend to elaborate the proposed extend on the postulate that it might perchance possibly perchance possibly no longer be ethical to delivery without first addressing the gender bias. Please restrict your answer to no greater than 600 phrases and reward the notice depend at the end of your answer. (30 marks) Question 1b Sooner than contacting Darnell, Taylor would luxuriate in to gain Eduardo\u2019s enhance to extend the corporate\u2019s delivery of its AI-enabled product, Catalisten. Though Eduardo earlier informed Taylor to let the scenario (in terms of high groundless certain charges for female patients) sail, she needs to persuade him so that they’ll present a united entrance as the near physique of workers. The Harvard Program on Negotiation summarised several research research on ways to construct up belief (watch Field 9.2 of the Textbook). Formulate three (3) ways thru which Taylor can accumulate and assemble Eduardo\u2019s belief. Please restrict your answer to no greater than 400 phrases and reward the notice depend at the end of your answer. (20 marks) Question 1c No longer only is Taylor the youngest senior product manager at Catalise, she is also the finest girl to abet the map. Ladies are most often viewed as less effective negotiators than men because women folks have a tendency to be perceived as being less assertive or rational and, more emotional. Judge that you just might perchance well perchance possibly very effectively be Taylor, discuss the spend of three (3) out of the ten most attention-grabbing practices of negotiation which that you just might perchance undertake, to habits a successful negotiation with Darnell. Please restrict your answer to no greater than 300 phrases and reward the notice depend at the end of your answer. (15 marks) Allotment B (25 marks) Answer all questions about this fragment. Question 2 Prepare a video recording of you presenting any part or segment of your answer to any one depend on in Allotment A above. Decide and present the principle parts out of your answer. You are no longer required to illustrate any PowerPoint slides within the video recording. Please read the Overview Criteria. The presentation must serene be a minimal of three minutes nonetheless must no longer exceed 6 minutes. Refer to Canvas T\/TG\/RESIT course residing > Assignments > ECA_VIDEO for the step-by-step manual on strategies to submit the video. (25 marks) Write My Project Rent a Professional Essay & Project Writer for polishing off your Tutorial Assessments Native Singapore Writers Personnel 100% Plagiarism-Free Essay Best Pleasure Price Free Revision On-Time Transport Allotment C (10 marks) Answer all questions about this fragment. Question 3 Prepare a suite of PowerPoint presentation slides in line alongside with your video presentation in Allotment B. Decide and present the principle parts in no greater than 15 slides. Please read the Overview Criteria and reward that the PowerPoint slides must serene be transformed to PDF before submission to Canvas. 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Allotment A (65 marks) Answer all questions about this fragment. Point to: You wish to answer ALL questions about this fragment. Answers must serene be supported with theoretical items and ideas from the suggested textbook, Lewicki, R. J., Saunders, D. M. & Barry, B. (2021). Necessities of Negotiation (Seventh ed). Unusual York: McGraw-Hill Worldwide Model […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-21504","post","type-post","status-publish","format-standard","hentry","category-solutions"],"_links":{"self":[{"href":"https:\/\/academicwritersbay.com\/solutions\/wp-json\/wp\/v2\/posts\/21504","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/academicwritersbay.com\/solutions\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/academicwritersbay.com\/solutions\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/academicwritersbay.com\/solutions\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/academicwritersbay.com\/solutions\/wp-json\/wp\/v2\/comments?post=21504"}],"version-history":[{"count":0,"href":"https:\/\/academicwritersbay.com\/solutions\/wp-json\/wp\/v2\/posts\/21504\/revisions"}],"wp:attachment":[{"href":"https:\/\/academicwritersbay.com\/solutions\/wp-json\/wp\/v2\/media?parent=21504"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/academicwritersbay.com\/solutions\/wp-json\/wp\/v2\/categories?post=21504"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/academicwritersbay.com\/solutions\/wp-json\/wp\/v2\/tags?post=21504"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}