Category: Artificial intelligence

Introduction

Artificial intelligence is one of the innovations that promise to have a significant impact on healthcare delivery. AI refers to the simulation of human performed tasks by machines such as computer systems and robots. AI helps in data analysis through electronic health records, diagnosis, and disease management (Bresnick, 2018). Proper use of AI techniques in a healthcare setting can help to minimize errors in data analysis and enhance the quality of care received by patients.

Purpose

This paper aims to talk about AI as an innovative idea that can be integrated into healthcare. The paper will detail some of the strategies used in executing artificial intelligence in health care. It will also discuss the risks and benefits that are likely to arise from using AI in healthcare. This will help to inform on strategies that can be used to minimize possible risks. The paper will highlight the facilitators of healthcare institutions applying AI and the barriers that are likely to be faced in the process.

Problem

There exists a human resource crisis in healthcare institutions that have a significant impact on the quality of care delivered to patients. The human resource crisis in healthcare has resulted from a shortage of healthcare providers, burnout among professionals, and increasing demand for chronic care (ODonnell, 2019). This has led to challenges in healthcare delivery and the quality of care offered to patients, impacting their quality of life.

Problem Analysis

According to Miseda et al. (2017), there is a shortage of nurses and doctors, especially in rural areas and developing nations. Only thirty-eight percent of global nurses and twenty-five percent of doctors provide their services in rural areas. Burnout is also a contribution to the shortage of human resources experienced in healthcare. Burnout affects job turnover and the possibility of a healthcare provider quitting their work (Reith, 2018). A significant number of healthcare professionals experience burnout from their work duties, and the risk is likely to be great if the problem is not adequately addressed. The rise in the number of people with chronic diseases and conditions has enhanced the demand for chronic care, thus putting pressure on healthcare providers.

Results of Analysis

The analysis carried out above indicates that the quality of care delivered to patients is likely to be low, which might affect their quality of life. There is a shortage of healthcare providers, which is likely to increase within the coming years. This leaves a significant number of patients unattended, which can affect their quality of life. Healthcare professionals also experience burnout due to the pressure they face when handling their day-to-day activities. Burnout affects how they deliver healthcare which can impact patients quality of life. The increase in the number of patients suffering from chronic illnesses has also increased pressure on healthcare providers and has a likelihood of causing burnout.

Recommendations

To eliminate the human resource crisis in healthcare, it will be necessary for healthcare institutions to integrate AI into their operations. AI will help handle some of the tasks specified for healthcare providers, such as analysis of patient data, and detecting medical issues. According to Miseda et al. (2017), deep learning algorithms can be used to diagnose conditions related to cardiology, dermatology, and oncology. Using AI in healthcare will help to reduce the burden on healthcare providers and enhance the quality of care received by patients.

Strategies for Implementation

AI can be implemented in healthcare to manage patient data stored in electronic health records. Using AI in electronic health records helps the medical provider to retrieve and analyze patient data easier for them to make informed decisions. This will help to ensure that treatments specified for patients are accurate and appropriate for their medical conditions (Lin et al., 2020). AI in electronic health records will help handle complex data that cannot be analyzed manually and help to reduce the time used in the process.

Healthcare institutions can also implement AI in their operations to enhance the early detection of medical problems. AI can be used to detect cancer in its early stages by relying on mammograms. AI helps in the review and translation of mammograms, increasing the speed by thirty times compared to when the process is done manually. AI also enhances the accuracy of such processes compared to relying on humans who are prone to errors.

Risks of Implementation

One of the risks of implementing artificial intelligence in healthcare institutions is injuries and errors. According to Walter (2019), artificial intelligence systems can lead to errors that cause patient injuries or other medical problems, such as when a wrong drug is prescribed to a patient. A disadvantage with this is that patients are likely to react differently to errors that result from digital tools compared to human errors. Also, if a problem affects artificial intelligence in a healthcare institution, it is likely to affect a huge number of patients at once.

Privacy is also a risk associated with implementing artificial intelligence in a healthcare institution. Artificial intelligence systems face a risk of data breaches through hacking. In this case, patient data might end up in the wrong hands and be used for the wrong purposes. Data breaches can also affect the provision of healthcare by interfering with patient records. Healthcare institutions must lay out security strategies when implementing artificial intelligence.

Benefits of Implementation

Artificial intelligence can perform better than human medical providers, thus helping to enhance care delivery and its quality. Existing artificial intelligence systems can even predict an injury even before it occurs, which is not possible among human medical providers (Van Eetvelde et al., 2021). Artificial intelligence can also automate activities in a healthcare institution, such as managing electronic health records (Price, 2019). This helps to save time and resources likely to be used when relying on human labor to manage healthcare records.

Facilitators for Implementation

Existing information technology systems in healthcare institutions act as facilitators for the implementation of artificial intelligence. Artificial intelligence scans are easily integrated into existing information technology systems without interfering with how workflow practices are conducted (Strohm et al., 2020). Another facilitator to the implementation of artificial intelligence is the expectations held by healthcare providers and stakeholders about its effectiveness (Klumpp et al., 2021). In this case, healthcare providers are likely to adopt artificial intelligence without problems.

Barriers to Implementation

The lack of adequate funding is one of the barriers to the implementation of artificial intelligence in healthcare institutions. According to Strohm et al. (2020), the benefits and costs of implementing artificial intelligence vary from one department to another, thus complicating funding decisions. Some artificial intelligence systems can also be very expensive to implement and maintain in a healthcare institution. Trust issues among patients, especially the elderly, also act as barriers to implementing artificial intelligence in medical institutions (Kuan, 2019). Patients who are not aware of the benefits of artificial intelligence are likely to refuse the use of such systems when receiving medical care.

References

Bresnick, J. (2018). Top 12 Ways Artificial Intelligence Will Impact Healthcare. HealthITAnalytics. Web.

Klumpp, M., Hintze, M., Immonen, M., Ródenas-Rigla, F., Pilati, F., Aparicio-Martínez, F., Çelebi, D., Liebig, T., Jirstrand, M., Urbann, O., Hedman, M., Lipponen, J. A., Bicciato, S., Radan, A., Valdivieso, B., Thronicke, W., Gunopulos, D., & Delgado-Gonzalo, R. (2021). Artificial intelligence for hospital health care: Application cases and answers to challenges in European hospitals. In Healthcare (Vol. 9, No. 8, p. 961). Multidisciplinary Digital Publishing Institute. Web.

Kuan, R. (2019). Adopting AI in Health Care Will Be Slow and Difficult. Harvard Business Review. Web.

Lin, W. C., Chen, J. S., Chiang, M. F., & Hribar, M. R. (2020). Applications of artificial intelligence to electronic health record data in ophthalmology. Translational vision science & technology, 9(2), 13-13. Web.

Miseda, M. H., Were, S. O., Murianki, C. A., Mutuku, M. P., & Mutwiwa, S. N. (2017). The implication of the shortage of health workforce specialists on universal health coverage in Kenya. Human resources for health, 15(1), 1-7. Web.

ODonnell, R. (2019). The HR Challenges Shaping the Healthcare Industry. Workest. Web.

Price, N. (2019). Risks and remedies for artificial intelligence in health care. Brookings. Web.

Strohm, L., Hehakaya, C., Ranschaert, E. R., Boon, W. P., & Moors, E. H. (2020). Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors. European radiology, 30, 5525-5532. Web.

Van Eetvelde, H., Mendonça, L. D., Ley, C., Seil, R., & Tischer, T. (2021). Machine learning methods in sports injury prediction and prevention: a systematic review. Journal of experimental orthopaedics, 8(1), 1-15. Web.

Walter, M. (2019). 6 serious risks associated with AI in healthcare. AI in Healthcare. Web.

Introduction

Rapidly advancing artificial intelligence technologies are gradually changing health care practices and bring a paradigm shift to the medical system. While increasing the availability of data and enhancing analytics techniques, they facilitate practices that were previously considered to be an area only for human experts. Although there are several challenges such as subjectivity of results, possible security, and privacy problems, and presumable unethicality of AI systems, further development of technologies will eliminate the concerns.

1^st Rebuttal

It is often thought that using AI systems in health care leads to the subjectivity of results, as while learning, AI tends to copy human prejudices regarding decision-making. Although doctors should always be cautious and check whether there are any glaring mistakes due to misuse of AI, the technology is quickly advancing as it receives larger amounts of input data to learn on. What is essential is that the more data the AI gets, the fewer percent of prejudiced information it receives. The main aim of AI technologies in the health care system is to assist clinical decision-making through uncovering relevant information from data (He, Baxter, Xu, Xu, Zhou & Zhang, 2019, p. 30). As He et al. (2019) argue, with AI, it is easier to generate a diagnosis, select therapy, reduce medical errors, manage risk predictions, and improve productivity. The health system will reap enormous benefits of using AI, so there is a tendency of developing countries sharing more and more medical data for the development and learning of AI, so the chance of it being biased decreases.

2^nd Rebuttal

Some people are afraid that the application of AI in health care will pose a threat to the privacy and security of patients data. However, data leaks are not so often in the medical sphere, and scientists continue to develop new strategies to preserve and secure data. As Yu, Beam & Kohane (2018) state, privacy-preserving methods can permit secure data sharing through cloud services (such as third-party-hosted computing environments), and advancing blockchain may be a solution (p. 727). To break into a blockchain system, more than 51% of its nodes have to be hacked at the same time, and the new block has to be inserted into each of them. Consequently, blockchain systems are secure as binary computers do not have enough computing power to hack them, and quantum computers are far from reaching the level of being able to do so.

3^rd Rebuttal

Finally, the third counter-argument to implementing the AI in health care systems is that it is unethical and presents an ethical issue associated with patients choices. It is considered that some medical workers may have a false sense of security while using AI systems in research or analysis. However, AI does provide incredibly precise results while conducting a study, and with time passing and technology developing, it becomes an equally reliable source of information as a human is. Jiang et al. (2017) state that in a cancer research, 99% of the treatment recommendations from Watson (AI system) are coherent with the physician decisions (p. 241). Another concern is that there is a lack of legal liability regarding possible medical errors, but it is easily fixed by appointing people responsible for specific areas of AI performance. Troubles of reaching consent between machines and humans will also be eliminated in the future since technology is developing to be able to address issues conclusively.

Conclusion

AI-based technologies benefit the health care system today, and their influence will continue to grow in the future, as they facilitate research and analysis, spur productivity, and assist clinical decision-making. Although there are some challenges regarding their implementation, such as subjectivity of results, the possible threat of data hacks, and the unethicality of AI systems, all of them can be overcome by advancing technologies. Vast amounts of data and advanced algorithms may help reduce subjectivity; blockchain may allow secure data, and developing technologies will boost the credibility of AI research and analysis.

References

He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X. & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 3036. Web.

Jiang F., Jiang Y., Zhi, H., Dong, Y., Li, H., Ma, S., & Wang, Y. (2017) Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2: e000101, 230-243. Web.

Yu, K.-H., Beam, A. L. & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719731. Web.

The medical field constantly innovates and develops new technologies to improve patient care. Societies, in general, are significantly impacted by technological innovations (Giddens et al., 2021). One such recent innovation is the use of artificial intelligence (AI) in various applications, such as medical diagnosis and treatment. AI is quicker than humans in processing data accurately. This can be helpful in the diagnosis and treatment of medical conditions.

AI can be used to generate hypotheses about a patients symptoms. It can then use this information to search for patterns in data that may indicate a diagnosis. AI can also be used to identify potential treatments for patients. This information can then be compared to the results of tests that were done on the patient and improve doctors decisions on their patients health.

As AI becomes more widely used in the medical field, it has the potential to change our way of life in both small and large ways. One potential impact of AI on society is its ability to reduce the cost of healthcare (Mintz & Brodie, 2019). AI can automate routine tasks, such as reviewing X-rays and scans, which can save healthcare providers time and money. Additionally, AI has the potential to improve personalized care by predicting patient needs, which can help shorten patient care time. This technology also has the potential to improve medical care by automating processes and reducing the need for human expertise.

Another potential impact of AI on society is its ability to reduce the number of medical errors. By automating processes, AI could help healthcare providers ensure that all patient data is correctly entered and analyzed (Mintz & Brodie, 2019). This could help to reduce the number of medical errors, which can have serious consequences for patients. As AI continues to evolve, it has the potential to further reduce the cost of healthcare and improve patient care.

References

Giddens, A., Duneier, M., Appelbaum, R. P., & Carr, D. (2021). Essentials of sociology (8^th ed.). W.W. Norton & Company.

Mintz, Y., & Brodie, R. (2019). Introduction to artificial intelligence in medicine. Minimally Invasive Therapy & Allied Technologies, 28(2), 73-81. Web.

Artificial intelligence makes life for humans easier by doing things more precise and more efficient than humans normally do. Businesses can use AI to do work that is dangerous and can be harmful to humans. Some think that AI is bad due to the fact that it can take people’s jobs and that if it is put in the wrong hands it could end up being destructive and cause massive problems. With the world revolving around technology, advanced artificial intelligence benefits society by making everyday life easier and more precise in the workplace and the home.

Artificial intelligence or AI started with Allen Newell, Cliff Shaw and Herbert Simon’s and their ‘Logic Theorist’. ‘Logic Theorist’ was a program, that did calculations and could mimic the things that humans normally do and it was funded by Research and Development (RAND) Corporation.

Advanced artificial intelligence is anything that uses basic human programming to do human jobs while learning and developing methods of doing things on its own. Easier life means things that were once hard can be accomplished by artificial intelligence so that it takes less human effort. A more precise life means that everything that is done in everyday life is accurate and work is done and completed near perfect.

Easier in the Workplace

AI makes life in the workplace much easier. Artificial intelligence can do jobs that were once dangerous and harmful for human beings to do. AI is used also for jobs that aren’t necessarily difficult, but more than that, they are just time-consuming like, for example, Amazon uses Kiva robots to bring and package things people ordered for the people who take them to be shipped. AI makes it easier for people to do their work from home with programs that can be used from anywhere and shared with anyone, anywhere. AI takes over jobs making humans work less.

Easier in the Home

AI makes life at home simpler. AI can help make your life easier by doing things in your everyday routine for you. For example, monitoring home security and automatically calling 911 if needed, waking you up with a message, and making morning coffee. It is also used in smart fridges to tell you when food is going to go bad. Also, to give you recipes for food you can make based on what you have. Siri and Alexa are examples of AI used in personal everyday devices to help match certain things to your specific taste like movies, shows, and products. Advanced AI is becoming more readily available to cook your food for you and to be your own butler bringing you things.

More Precise in the Workplace

The use of AI in the workplace creates precision that is unlike that of humans, which benefits everyone. AI can be used to take jobs that humans would normally have. They are much more accurate and precise when doing these jobs and never have any type of small errors that only occur when humans do the job. AI robots also don’t have to eat or sleep meaning they are more available to work than humans are. The use of robots instead of humans can save companies lots of money and in the long run can help them boost their revenue by up to 20%.

More Precise in the Home

Artificial intelligence does everything to perfection and helps life become more precise in the home. Instruct your robot-chef and he will cook up your favorite dish to perfection every time, unlike the chef at the local restaurant. Robots make things more precise than humans every time. Autonomous vehicles use AI to help drive cars and can sense the environment to make the driving near perfect and better than some humans. Autonomous vehicles have been worked on and the closest we are to fully self-driving capability is with Tesla´s ‘autopilot’.

Controversies About Artificial Intelligence

Some people think that artificial intelligence is bad because put in the wrong hands, it can do serious damage, although the goods of AI outweigh the odds. AI that put into the wrong hands could lead to destruction through programming it to do bad things, like killing or harming human beings, without a way to stop it. AI could also try and learn on its own for the best method or way to do something only to end up destroying things in the process. Although this could be prevented if we keep it safe and in good hands with only the intentions of benefiting humankind.

Conclusion

AI is very beneficial to society and has a huge potential to do great things in the world. AI is shown to make life easier and more precise in both the workplace and the home, which is the whole main goal. AI will continue to only benefit society unless someone uses it in a way that is destructive and harmful to humans.

Machine learning and artificial intelligence are related, often present in the same context and sometimes used interchangeably. From just being a figment of someone’s imagination in sci-fi movies and novels, they have come a long way to augmenting human potential in doing tasks faster, more accurate and with greater precision each time, driven by technology, automation and innovation.

The father of artificial intelligence, John McCarthy, in the 1990s, defined the term as “artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs”. Generally, the term “AI” is used when a machine simulates functions that humans associate with other human minds, such as learning and problem solving (Krawczyk, B., 2016).

On a very broad account, the areas of artificial intelligence are classified into 16 categories. These are: reasoning, programming, artificial life, belief revision, data mining, distributed AI, expert systems, genetic algorithms, systems, knowledge representation, machine learning, natural language understanding, neural networks, theorem proving, constraint satisfaction, and theory of computation (Meiring, G.A.M.; [image: ]Myburgh, H.C, 2015)

A lot of us today already have AI enabled systems in their homes such as Siri, Alexa and Google Assistant to name a few. This technology is becoming an integral part of our daily lives which will go on to influence in much wider terms around business activities as well. PwC research suggests global GDP could be up to 14% higher by 2030 as a result of AI, making it the biggest commercial opportunity in today’s fast-changing economy.

So, what does an AI powered world mean for accountants? Will robots replace us? Certainly, the transactional aspect of accounting and the need to collect and process data are particularly susceptible to automation. Accounting software such as QuickBooks Online, Sage Business Cloud Accounting, and Xero are all leveraging the power of AI to classify transactions from bank and credit card feeds automatically. A Harvard Business Review study noted that only 3% of organizations’ data meet basic data quality standards; on average 47% of newly created data records have at least one critical error.6. By leveraging technology to streamline accounting tasks instead of spending time on manual and repetitive tasks, accountants will be able to focus on building and analysing reports that drive business insights and decisions, securing our role in corporate reporting and as strategic advisors. The long-term benefits involve making routine tasks automated, proper accounting classification of charges, processing large amounts of data at speed of the light, predicting outcomes using trends and patterns processing large amount of data. accountants can add value in terms of bringing their professional scepticism and ability to interrogate, and having oversight of what the algorithm is doing,’ says Vaidyanathan, head of technology insight at ACCA.

However, even the best of AI Machines need to be controlled by humans, as a computer would mean nothing unless intelligence is loaded onto it – through programs and software written by humans. Excessive automation can do more harm than good and humans are underrated in that context; the true potential lies in not replacing humans but to augment and amplify them. Foremost we need to take into consideration our customer privacy, the potential lack of transparency, technological complexity and cost structure of the company (i.e. the hardware and software need to get updated with time to meet the latest requirements and machines need repairing and maintenance which need plenty of cost.) There is no doubt that machines are much better when it comes to working efficiently but they cannot replace the human connection that makes the team.

One of the best approaches to implement AI is based on the four pillars below: Create an environment of learning, implement the change, monitor the input/output, manage the risks and training the teams to operate the system.

• Manage/sustain an AI plan with a comprehensive roadmap to identify priorities and modify them on ongoing basis.
• Select the right software, right platform (cloud/on premise) , the relevant costs, associated with the implementation.
• Assess, develop a strategy around data, our needs and skill assessment, timelines, training management.

MACHINE LEARNING

Machine learning (ML) is a sub-set of artificial intelligence (AI) and is generally understood as the ability of the system to make predictions or draw conclusions, based on the analysis of a large historical data set. At its most basic level, machine learning refers to any type of computer program that can “learn” by itself without having to be explicitly programmed by a human. The underlying idea has its origins decades ago – all the way to Alan Turing’s seminal 1950 paper “Computing Machinery and Intelligence”.

Essentially, ML ‘learns’ in the sense that the outcomes are not explicitly programmed in advance. An ML system is fed more data, it can improve its recognition of the patterns therein, and apply this improved recognition to new data sets that it may not have seen previously. Data scientists can program machine learning algorithms using a range of technologies and languages, including Java, Python, Scala, other others. They can also use pre-built machine learning frameworks to accelerate the process;

[image: ]The capabilities that machine learning offers could assist the work of professional accountants in various ways over time. One of the key drivers of this is the proliferation of data. Applications for adoption range across diverse areas, including for example, invoice coding, fraud detection, corporate reporting, taxation and working capital management. Online accounting software provider Xero (Kevin Fitzgerald, Asia Pacific Director for Xero) announced in May 2018 that its ML software had already made more than 1bn recommendations to customers since it became available, with areas of invoice coding and bank reconciliations being prominent.

Risk assessment, being the advantage of ML is the ability to assess the likelihood of fraud, inaccuracy, misstatement, based on a mix of empirical data and professional judgement. In this risk assessment, supervised learning algorithms can be used to help identify specific types or characteristics that warrant greater scrutiny; and improve targeting of the areas of focus for the audit.

ML is also being seen to have applications in relation to tax. It has a role to play in making tax query systems more effective. Using the ML technique of reinforced learning, AI chatbots and speech engines can train themselves to become more effective over time.

However, ML needs enough time to let the algorithms learn and develop enough to fulfill their purpose with a considerable amount of accuracy and relevancy. It also needs massive resources to function. Also machine learning is autonomous but highly susceptible to errors. Suppose we train an algorithm with data sets small enough to not be inclusive. We then end up with biased predictions coming from a biased training set to thus leading to irrelevant information being generated.

An example is Uber’s pricing system. Where 10 years ago this would have been hard-coded logic, a trained model now makes these decisions. It looks nothing like artificial general intelligence, but it performs a specific task to great accuracy. Viewed from the outside, the embedding of this AI software creates an increase in the operating effectiveness of the whole – a cost-saving development even if not a radical change.

LAWS AND REGULATION – ETHICS OF USE OF TECHNOLOGY IN FINANCE

In the wake of the financial crisis of 2008-2009, regulators worldwide, but particularly in the United States and Europe, have sought to increase accountability and foster ethical behaviour in their financial services industries. Environmental, social and corporate governance (ESG) issues have now become an essential part of non-financial reporting and of managing risk in today’s uncertain world.

Among the regulatory changes aimed at improving ethics are enhanced disclosure and reporting requirements. AI is the new subject of a wide-ranging debate in which there is a growing concern about its ethical and legal aspects. Ethics is specially needed when regulation is lacking. Law, however, is essential. Law implies a binding legal commitment, including for instance those ethical contents that are common and/or shared and therefore reach the statute of obligatory norms.

These include curbing speculative investment in commodity markets, addressing conflicts of interest in the provision of financial advice, and bringing greater transparency to algorithmic trading (known as high-frequency trading). Firms engaged in algorithmic trading, now must divulge their trading strategies among other elements.

The UNI Global Union based in Switzerland represents more than 20 million workers from over 150 countries in the fastest growing sectors in the world. This organisation adopts 10 principles for ethical AI:

• AI Systems are transparent
• Equip AI system with an Ethical Black Box
• Make AI serve People and Planet.
• Adopt a human-in-command approach
• Ensure a Genderless, unbiased AI
• Share the benefits of AI system
• Secure a Just Transition and ensuring support for fundamental freedom and rights.
• Establish global governance mechanisms
• Ban the attribution of responsibility to robots
• Ban AI arms race

On the other hand, the IEEE defends these principles:

• Human Rights
• Well-being
• Data Agency
• Effectiveness
• Transparency
• Accountability
• Awareness of misuse
• Competence

Other EU regulations relate more directly to customers, such as Anti-Money Laundering/ Know Your Customer requirements. The European Union’s General Data Protection Regulation, which aims to protect EU citizens’ data privacy wherever their data is stored, has considerable implications for how multinational financial services capture, store and transfer customer data.

Earlier this year, a high-level expert group on artificial intelligence (AI HLEG), set up by the European Commission, published guidelines for trustworthy AI, while similar principles were also adopted by the OECD’s 36 member countries, along with Argentina, Brazil, Colombia, Costa Rica, Peru and Romania last month. Ethical issues are also being explored by the UK’s newly created Centre for Data Ethics and Innovation and Office for AI. Organisations exploring the use of AI in financial services need the help of these global standards bodies to shape more detailed requirements on the use of AI that address the issue of ethics, as well as legal and regulatory compliance. Professional accountants need to consider, and appropriately manage, potential ethical compromises that may result from decision making by an algorithm.

REFERENCES

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4. Liu, S. (2019). Global Big Data market size 2011-2027 | Statista. [online] Statista. Available at: https://www.statista.com/statistics/254266/global-big-data-market-forecast/ [Accessed 1st Mar. 2020].
5. Kühl, N., Goutier, M., Hirt, R. and Satzger, G. (2019) Machine Learning In Artificial Intelligence: Towards A Common Understanding. [online] Research Gate. Available at: https://www.researchgate.net/publication/327802544_Machine_Learning_in_Artificial_Intelligence_Towards_a_Common_Understanding.
6. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, (2005) “The elements of statistical learning: data mining, inference and prediction,” Math. Intell., vol. 27, no. 2, pp. 83–85.
7. Robles Carrillo, M. (2020). Artificial intelligence: From ethics to law. Telecommunications Policy, p.101937.

Artificial intelligence is complex in its nature. It uses a very complex mixture of computer science, mathematics and other complex sciences. Complex programming helps these machines replicate human cognitive abilities.

1. Reduction of error

Artificial intelligence helps us reduce the error and the chance to achieve accuracy with a higher degree of precision. It is used in a variety of studies, such as space exploration.

Intelligent robots are fed with information and sent to explore space. Since they are machines with metal bodies, they are more resistant and have a greater ability to withstand space and a hostile atmosphere. They are created and acclimatized in such a way that they cannot be modified or disfigured or broken down in a hostile environment.

2. Difficult Exploration

Artificial intelligence and robotics science can be used for mining and other fuel exploration processes. Not only that these complex machines can be used to explore the ocean floor and thus overcome human limitations. Thanks to the programming of robots, they can do more laborious and hard work with greater responsibility. In addition, they don’t wear out easily.

3. Daily Application

Computed methods for automatic thinking, learning and interpretation have become a widespread phenomenon in our daily lives. We’ve got our lady Siri or Cortana to get us out of here. We’re also taking the road for long journeys and trips with the aid of GPS. The smartphone is an appropriate daily example of how we use artificial intelligence. In utilities, we find they can predict what we’re going to type and correct human spelling errors. It’s machine intelligence at work. When we take a snapshot, the artificial intelligence algorithm identifies and recognizes the person’s face and marks individuals when we share our images on social media sites. Artificial Intelligence is commonly used in the organization and management of data by financial firms and banking institutions. Fraud detection uses artificial intelligence in a smart card based system.

4. Digital Assistants

Highly advanced companies use ‘avatars’ that are replicas or digital assistants that can actually communicate with users, saving the need for human resources. For artificial thinkers, feelings come in the way of critical thought, and they are no diversion at all. The utter absence of the emotional side, lets robots think critically and make the right program decisions. Emotions are correlated with moods that can cloud judgment and influence human effectiveness. This is entirely removed from machine intelligence.

5. Repetitive Jobs

Repetitive activities that are monotonous in nature can be carried out with the aid of artificial intelligence. Machines think faster than humans and can be used for multi-tasking. Machine intelligence can be used to perform risky tasks. Unlike humans, their parameters can be changed. Their speed and time are just parameters dependent on measurement. In reality, when humans play a video game or operate a computer-controlled robot, we communicate with artificial intelligence. The machine is our foe in the game we’re playing. Machine intelligence is preparing the movement of the game in response to our movements. Gaming can be considered to be the most common application of the advantages of artificial intelligence.

6. Medical Applications

Lots of people can also notice the broad application of AI in the medical field. Doctors evaluate patients and their health risks with the help of artificial machine intelligence. It teaches them about the side effects of different drugs. Health professionals are also trained by simulators of artificial surgery. It finds an enormous application in the diagnosis and control of neurological disorders as it can model brain activity. Robotics is also used to help mental health patients resolve depression and stay involved. Radiosurgery is the most common application of artificial intelligence. Radiosurgery is used in operating tumors, and can potentially aid in the procedure without affecting the underlying tissues.

Machines, unlike humans, do not require regular breaks and refreshes. They are programmed for long hours and can work constantly without getting bored or distracted or tired.

ABSTRACT

The complexity and height of data in healthcare means that artificial intelligence (AI) is increasingly being used within the field. Artificial intelligence is an exciting robotics industry emerging in the current healthcare. The purpose of AI is to make computer-related health-related challenges more efficient. AIS simplifies the lives of patients, doctors, and hospital administrators by performing tasks that are normally performed by humans, but in short and at a fraction of the cost. There are countless applications in AI health care and it is an honor to have healthcare.

INTRODUCTION

Artificial intelligence is rapidly expanding into healthcare. We have highlighted eight ways in which this information is passing.

KEEPING WELL

One of the biggest potential benefits of AI is to help people stay healthy so they don’t need a doctor. Using AI and IOMT in consumer health applications is helping people. Technology applications and apps encourage healthy behavior in individuals and assist in the active management of healthy lifestyles. It gives consumers control of health and fitness.

EARLY DETECTION

One of the biggest potential benefits of AI is to help people stay healthy so they don’t need a doctor. Using AI and IOMT in consumer health applications is helping people. Technology applications and apps encourage healthy behavior in individuals and assist in the active management of healthy lifestyles. It gives consumers control of health and fitness.

DECISION MAKING

Improving care requires aligning major health data with appropriate and timely decisions, and predictive analytics can support clinicians’ decisions and actions, as well as prioritize management tasks.

DIAGNOSIS

The health care organization uses knowledge technology to open large volumes of health data and power diagnostics. Every medical journal, study of symptoms and treatments and reactions worldwide is faster than any human. This technology combines machine learning and system neuroscience to create powerful general purpose learning algorithms in the neural network that mimic the human brain.

TREATMENT

In addition to scanning health records, the provider can identify chronically ill individuals who are at risk of a negative event. Robots have been used in medicine for over 30 years. These range from simple laboratory robots to highly sophisticated surgical robots that can either assist a human surgeon or perform the operation themselves.

END OF LIFE CARE

We are living longer than previous generations, and as we get closer to the end of life, we are dying differently and slowly from conditions like dementia, heart failure and osteoporosis. It is also a phase of life that is often lacking. Robots have the potential to revolutionize end-of-life care, help people be more independent, and reduce the need for hospitalization and care homes.

Artificial intelligence is the fastest growing branch of technology. AI has the potential to help diagnose the disease and is currently being sued in some UK hospitals for this purpose.

Uses of AI in clinical care includes:-

• ED Medical Imaging – can reduce the cost and time involved in the analysis of AI scans, potentially allowing the use of more scans for better target treatment. AI has shown results associated with the detection of conditions such as pneumonia, breast and skin cancer and eye diseases.
• CH echocardiography – AE echocardiography helps analyze scans that detect heartbeat patterns and diagnose coronary heart disease.
• UR surgeries are used in AI-controlled robotic tool research such as bandaging to close wounds in cataract surgery.

AI IS CHANGING HEALTHCARE SYSTEM:

• Medical records and other data management – As a first step in compiling and analyzing health care information, commonly used data management is artificial intelligence and digital automation.
• PK is doing the right thing – faster, faster accurate analysis of robots by tests, x-rays, CT scans, data entry and more. Cardiology and radiology are two subjects where data analysis takes time.
• IG Digital Consulting – Apps like Babylon in the UK use AI for medical advice based on personal medical history and general medical information.
• Users report their symptoms in the app, which uses speech recognition to compare illness databases.
• DATA Security – Medical information is incredibly useful for criminals who steal identity or sell information, and can help preserve AI medical records.
• Drug Research – Pharmaceutical companies are using the AI platform to accelerate drug discovery. Platforms can be helpful in drug detection
• MEDICAL DIAGNOSES AND IMAGING – AI is a poor platform that supports the diagnosis of certain diseases, supports rapid diagnosis, reduces the cost of diagnosis and is capable of diagnosing distances. And also save AI’s ability to process photos faster. They reduce the time needed to perform medical imaging procedures, provide better 2D and 3D imaging.
• VIRTUAL :- In 2016, Boston Children’s Hospital developed an app for Amazon Alexa that provides basic health information and advice to parents of sick children. The app answers questions about medications and symptoms that require a doctor’s visit.

SOME INCREDIBLE INNOVATIONS

· PROSTHETIC HAND

Most hypnotherapists may not allow their wearers to regain their senses of contact, but this latest version of DARPA’s artificial hand uses neutron technology to do just that. A mechanical arm developed by the APL at Johns Hopkins University – the investigation found the 28-year-old man’s artificial wire – used electrodes directly on his sensory cortex and motor management in his brain. He became the first to be able to ‘feel’ the physical sensation with artificial hands.

· ALGORITHM THAT CAN PREVENT HIV

Homelessness affects 2 million people between the ages of 13 and 24 in the states each year. 11% of them are HIV positive. But researchers at the University of Southern California’s School of Social Work and Engineering have developed a new algorithm called PSINET, which identifies the best homeless community members to spread important information about HIV prevention among young people. does. Uses for artificial intelligence.

· PERSONALIZED MOBILE APP

The mobile apps are meant to engage with the many possibilities surrounding app creation. Are the best From requesting physician appointments, to testing, uploading a patient’s medical history to getting test results through a mobile app. – Health organizations can create useful digital tools that are tailored to the modern-day patient. – Mobile apps also reduce stress, wait times and reception duties on medical staff.

· SOPHIA

Sofia is a social humanoid robot developed by Hansen Robotics, a Hong Kong-based company. Sofia was activated on February 14, 2016. Sofia uses Alphabet Inc.’s voice recognition technology to improve over time. It’s designed. The AI ​​program analyzes conversations and extracts data to help them improve response in the future.

FUTURE OF AI

In healthcare , AI is already changing the patient experience , how clinicians practice medicine, and how the pharmaceutical industry operates. The Future of AI in healthcare could include tasks that range from simple to complex – everything from answering the phone to medical record review ,population health trending and analytics , therapeutic drug and device design , reading radiology images , making clinical diagnoses and treatment plans and even talking with patients.

CONCLUSION

Artificial intelligence is definitely improving the healthcare industry. By providing a more accurate diagnosis of local health care and encouraging patients to take care of their own health, patients’ experience and health care skills will continue to improve.

REFERENCES

1. https://www.researchgate.net/publication/317880442_Artificial_intelligence_in_healthcare_past_present_and_future
2. https://www.journals.elsevier.com/artificial-intelligence-in-medicine/most-downloaded-articles.
3. https://builtin.com/artificial-intelligence/artificial-intelligence-healthcare
4. https://healthitanalytics.com/news/top-12-ways-artificial-intelligence-will-impact-healthcare
5. https://www.internationalsos.com/client-magazines/in-this-issue-3/how-ai-is-transforming-the- future-of-healthcare
6. https://www.pwc.com/gx/en/industries/healthcare/publications/ai-robotics-new-health/transforming-healthcare.html
7. https://mashable.com/2015/12/20/social-good-innovations-2015/
8. https://www.healthdatamanagement.com/list/7-ways-ai-could-make-an-impact-on-medical-care

Artificial Intelligence, also known as AI, is amongst the latest trend in today’s world. AI is best defined as a simulation of processes that replicates human intelligence and these processes are generally performed by machines. As we leap into a future of technological advancement, AI is estimated to play a major role in almost every aspect of life. This project will discuss why AI is and can be an issue if not minimised some of its destructive potential and also, its current and possibly future impacts on business and society.

Since Artificial Intelligence is about implementing smart algorithm that a machine can perform to replicate human intelligence, there have been many occurrences of bad or even good algorithm decisions which eventually leads up to threatening human lives. Though these good algorithms are never 100% accurate, what defines them as good algorithm is that the minor problems that may transpire won’t be catastrophic, however, that has not always been the case. One such example was when the automatic thrust restoration, also known as the ATR, of a Scandinavian flight automatically generated power due to its safety protocol, which was part of its algorithm, even when the pilot decreased the power of the aircraft during take-off.

Though the main culprit behind the crash of this flight was ice, the ATR was equally to blame because even though the pilots took the right steps to prevent a major catastrophe, the computer code which governed the ATR undermined the pilots inputs and decided to act upon itself which eventually lead up to a crash and consequently, placed hundreds of human lives at risks. But what if faults were due to a bad algorithm itself? Last year, a self-driving uber killed a pedestrian in Arizona. And when a flawed algorithm using voice recognition system to detect immigration fraud led the UK to deport thousands of students in error, it only posed question for the type of algorithm being used within the system. (Bayle, 2018) The examples mentioned above are all occurrences of a flawed algorithm which eventually threatened human lives, posing an important question for the society that is: knowing that even good algorithms can pose danger to human lives, why is artificial intelligence available in various machines that are ultimately used by a large number of people?

From assisting human decisions to threatening our autonomy. AI is reducing substantial number of tasks from humans, but it also has a damaging side effect on people’s skills and their ability to use machines especially when a machine fails which eventually leads to incremental of human errors. This phenomenon is known as the “automation paradox.” For example, systems like Google Map or Apple Maps assist drivers by providing different routes, but it also reshapes the way people drive their vehicles as now, they rely upon the application to provide them with directions. No one ever thinks about what routes they should be taking if they got lost when the AI fails to deliver its optimal outcome because as described an article by Wired.com, “the more reliant we become on technology, the less prepared we are to take control in the exceptional cases when the technology fails.” (Guszcza, 2018) Therefore, though AI is making our lives easier, simultaneously, is it making us humans incapable to function as efficiently as we would be if AI were not present in our everyday lives?

Abstract

Artificialintelligenceis thehumanlikeintelligence exhibited by machines or software.It is an important part of computer science .The advancement in computer technology has encouraged the researchers to develop software for assisting doctors in making decision without consulting the specialists directly.The software development exploits the potential of human intelligence such as reasoning, makingdecision, learning (byexperiencing) and many others. Artificial intelligence is not a new concept, yet it has been accepted as a new technology in computer science. It has been applied in many areas such as education, business, medical and manufacturing. This paper explores the potential of artificial intelligence techniques particularly for web-based medical applications. Keyword : Artificial intelligence, making decision, web-based medical application. I. INTRODUCTION In most developing countries insufficient of medical specialist has increased the mortality of patients suffered from various diseases.The insufficient of medical specialists will never be overcome within a short period of time. The institutionsofhigherlearningcouldhowever,take an immediate action to produce as many doctors as possible.It is a long procedure to transform a general doctor to specialist. Medical practitioner may not have enough expertise or experience to deal with certain high-risk diseases. However, the waiting time for treatments normally takes a few days, weeks or even months. By the time the patients see the specialist,the diseases may have already spread out. As most of the high-risk disease could only be cured at the early stage, the patients may have to suffer for the rest of their life.

Computer program or software developed by emulating human intelligence could be used to assist the doctors in making decision without consulting the specialists directly. The software was not meant to replace the specialist or doctor, yet it was developed to assist general practitioner and specialist in diagnosing and predicting patient’s condition from certain rules or”experience”.Employing the technology Artificial Intelligence (AI) techniques in medical applications could reduced the cost, time, human expertise and medical error. Computer program known as Medical Decision-Support System was designed to help health professionals make clinical decision [1].The system deals with medical data and knowledge domain in diagnosing patients conditions as well as recommending suitable treatments for the particular patients. Computer program known as Medical DecisionSupport System was designed to help health Patient-Centred Health Information Systems is a patient centered medical information system developed to assist monitoring, managing and interpret patient’s medical history [2].

The system serves to improve the quality of medical decision-making, increases patient compliance and minimizes iatrogenic disease and medical errors. While diagnosing the patient, doctor can refer to patient’s history record for a history treatment. A prescription of medicine can automatically sent to the dispensary. The advancement in computer technology and communication encourages health-care provider to providehealth-careovertheInternetortelemedicine . Telemedicine can improve access to care, increase health-care quality and reduce the cost . Patients from rural areas can access to the same quality of health-care as those in big city. In general, telemedicine means the use of computer andcommunicationstechnologiestoaugmentthe deliveryofhealth-careservices[3].Theapproach reduces the cost and time for both patients and doctors. This paper discussed about artificial intelligence in medicine, centralized database,webbased medical diagnosis and prediction, specifically for medical practitioners.

LITERATURE REVIEW

Modern medicine is faced with the challenge of acquiring, analysing and applying the large amount of knowledge necessary to solve complex clinical problems. The development of medical artificial intelligence has been related to the development of AI programs intended. to help the clinician in the formulation of a diagnosis, the making of therapeutic decisions and the prediction of outcome. They are designed to support healthcare workers in their every day duties, assisting with tasks.

METHODOLOGY

Artificial intelligence in medicine. It Produces new tools to support medical decision-making, training and research. Integrates activities in medical, computer, cognitive and other sciences. Centralized database. The patients records are valuable information for the knowledge-based system. The current patients data would enhance and strengthen the validity of the system reasoning [4]. • Web-based medical diagnosis and prediction. Prediction module utilizes neural networks techniques to predict patients illness or conditions based on the previous similar cases. Diagnosis module consists of expert system and fuzzy logic techniques to perform diagnosis tasks. IV. AI IN MEDICAL A. AI IN MEDICINE Experienced Based Medical Diagnostics Systemaninteractivemedicaldiagnosticsystem is accessible through the Internet[5] . Case Based Reasoning (CBR) was employed to utilize the specific knowledge of previously experienced and concrete problem or cases. The system can be used by patients to diagnose themselves without having to make frequent visit to doctors and as well as medical practitioner to extend their knowledge in domain cases (breast cancer).

B. Centralized database The patients records are valuable information for the knowledge-based system. The current patients data would enhance and strengthen the validity of the system reasoning.This implies that patients information in one system can only be used by that particular system. On the other hand, other systems require another databases for other patients or for the same patients whose records were kept in other databases. Another problem with standalone database is that, the database for the same system in another places would differ as the number of patients using the systems increases.

TELEMEDICINE

Telemedicine is the integration of telecommunications technologies, information technologies, human-machine interface technology and medical care technologies for the purpose of enhancing health care delivery across space and time . define telemedicine as any instance of medical care occurring via the Internet and using real-time videoteleconferencing equipment as well as more specializedmedicaldiagnosticequipment.In general, telemedicine means the use of computer and communications technologies to augment the delivery of health-care services . Telemedicine can improve access to care, increase health-care quality and reduce the cost . Patients from rural areas can access to the same quality of health-care as those in big city.

D. Fuzzy Expert Systems Fuzzy logic is another branch of artificial intelligence techniques. It deals with uncertainty in knowledge that simulates human reasoning in incomplete or fuzzy data. applied fuzzy relational inference in medical diagnosis. It was used within the medical knowledgebased system, which is referred to as Clinaid. It deals with diagnostic activity, treatment recommendations and patient’s administration.Fuzzy logic has also been used to predict survival in patients with breast cancer[6].

E. Diagnosis Diagnosis module consists of expert system and fuzzy logic techniques to perform diagnosis tasks. A set of rules will be defined using the patients and patients-disease databases as well as the expert knowledge on the disease domain. Expert system uses the rules to diagnose patient’s illness based on their current conditions or symptoms. In addition, fuzzy logic is integrated to enhance the reasoning when dealing with fuzzy data.

The combination of expert system and fuzzy logic that forms a system could increase the system performance.

RESULT ANALYSIS

Each AI technique has its own strengths and weaknesses.The used of computer and communication tools can change the medical practice into a better implementation. Consolidation in health-care provider will happen by focusing on cost and later on quality of services. Advancement in technology will form a platform for development a better design of telemedicine application. Telephone line and Internet will be the most important tools in medical applications. Centralized medical record helps doctors to improve the quality of treatment and provide a better diagnosis based on patients medical history. In addition, researchers in medical applications could use the data in their investigation of a new medical solution, patient’s management and treatment. fuzzy logic will be suitable techniques for dealing with partial evidence and with uncertainty regarding the effects of proposed interventions. For the prediction tasks, Neural Networks have been proven to produce better results compared to other techniques. Such techniques are worth to explore and integrate in the system for medical diagnosis and prediction.

CONCLUSION

There are many different AI techniques availablewhicharecapableofsolvingavariety of clinical problems.There is compelling evidencethatmedicalAIcanplayavitalrole in assisting the clinician to deliver health care efficiently in the 21st century. There is little doubt that these techniques will serve to enhance and complement the ‘medical intelligence’ of the future clinician. fficiently in a given space. AI can be applied to perform several types of tasks like diagnosis and prognosis, medical imaging and signal processing, and planning and scheduling. The principles of Genetic algorithms have been used to predict outcome in critically ill patients,lung cancer.The approach reduces the cost and time for both patients and doctors.

REFERENCE

1. Shortliffe,E.H.(1987).ComputerPrograms to Support Clinical Decision Making. Journal of the American Medical Association, Vol. 258, No. 1. Szolovits, P., Doyle, J., Long, W. J., Kohane, I., and Pauker, S. G. (1994).
2. Patient- Centred Health Information Systems. Technical Report MIT/LCS/TR-604.Massachusetts Institute of Technology. Chellappa, M. (1995).
3. Telemedic-Care. NCIT’95: 8’th National Conference Information Technology’95 (16-18 August 1995).
4. Gabungan Komputer Nasional Malaysia. Manickam, S., and Abidi, S. S. R. (1999).
5. Experienced Based Medical Diagnostics System Over The World Wide Web (WWW), Proceedings of The First National Conference on Artificial Intelligence Application In Industry, Kuala Lumpur, pp. 47 – 56.
6. Shortliffe, E. H., Barnet, G. O., Cimino, J. J., Greenes, R. A. and Patel(2003), InterMed: An Internet-Based Medical Collaboratory,Canada Seker H, Odetayo MO, Petrovic D, Naguib RNG, Bartoli C, Alasio L et al (2002).

Here, by systems thinking gender bias and sustainability challenges, the issues with artificial intelligence are considered. Having the quick development of artificial intelligence the biased information can affect various predictions that are made by the machines. As one has the dataset of different human decisions, this involves bias in it. It comprises of the hiring of decisions, medical diagnosis, grading the exams for the student and approval of loans. Further, any aspect that is demonstrated in the test, the vice and images needs the processing of information. It can be influenced through the race, gender and cultural biases (Caliskan, Bryson and Narayanan 2017). Here, the “wicked problem” is that though AI comprises of the potential for making decisions ineffective and less biased way. This can never be actually a clean state. AI is just useful as the data within it can power it. The quality relies on the way the creators can program that what, learn, decide and think. Due to this reason, AI is able to inherit and amplify its creator’s biases. These developers are commonly unaware of the biases created by them. Otherwise AI can use biased information. Here, the outcomes of these technologies are life-altering (Buolamwini and Gebru 2018). The already existing gap in workplaces has included the present gaps to promote and hire the females. This can broaden as the biases get written unintentionally to the code of AI. Otherwise, the AI can learn to make discrimination.

The various vital terms or ideas involving this area of concern is discussed hereafter. Artificial Intelligence is found to disruptive in all the sectors of life. This consists of the well the business can seek talent. Moreover, the organizations have been aware of Return of Investment coming from finding the proper person for a suitable task. Again, the women have been analyzed in negative view from the other’s side. This happens as the behavioral differences granular in nature present between the men and women. Again, colossal scale meta-analysis, on the other hand, has shown that females have more enormous benefits as that coming to soft skills. This redisposes the people in becoming more efficient leaders. They can adopt more efficient “leadership style” than the males. Apart from this, as the leaders are chosen as per the self-awareness, coach ability, integrity and emotional intelligence. Besides, most of the leaders who have been women instead of being men.

The primary purpose of the following study is to evaluate the gender bias rising from the area of artificial intelligence. The study is made around various sources that are negated to the discussion. Its analysis is presented critically and two sides of the arguments are developed. Examples are to be provided where needed. Instead of just making a reporting, the published work is summarized, assessed, explained and evaluated. Ultimately, the study answers the primary concern to what extent the statement that artificial intelligence can five rises in gender bias or can to do away with gender bias is evaluated.

Discussion on gender bias and artificial intelligence:

To understand the scenario, the way AI can serve as the equalizer for the bias is to be assessed. For this, the instances of artificial intelligence developing human processes are determined. Next, the bad news regarding how the bias in AI is the barrier to the inclusion is confirmed. Then, the instances of AI bias long with how the AI creators can be more diverse in nature is understood from here. Further, the questions to consider are highlighted. Lastly, the AI consortiums, research teams, along with the start-ups, are demonstrated (Osoba and Welser IV 2017).

The argument regarding AI serving as the equalizer:

AI can serve as the equalizer as it can decrease the decisions for people what has been naturally subjected to their individual consciousness and make predictions with various algorithms on the basis of the data. The algorithms can develop the process of decision-making ranging from loan applications to gets hired for the job (Levendowski 2017).

Instances of AI developing human processes:

The algorithm is successfully identified by boars inaccurate way permitting the evaluation of characteristics like making. There are few organizations building the tools limiting the bias though analyzing the applications. This is on the basis of abilities, skills, and specific data. Here, monitoring the AI tools can assure that bias never creep in. Besides, there are tools to scan the tracking system of applicants and additional career sites for seeking the candidates and eradicate the names from the overall program to decrease the bias (Zhao et al. 2017). Besides, few tools can obscure the appearance of candidate and voice as the interview process goes on. This can diminish the potential for bias.

The argument regarding AI bias is a barrier to inclusion:

The quality is dependable on the way the creators are able to program that to act, learn, decide and think. Due to this, AI might inherit and amplify the creator’s biases. They are unaware of their individual biases. Otherwise, AI can use the data biased (Flekova et al. 2016).

Instances of bias in AI for the above situation:

At one place an employer has been advertising for job opening under the male dominated sector. This is through the platform of social media. The ad algorithm of the platform has been pushing the jobs to the men in maximizing the returns of quality and number of applicants. Another tech business has been spending prolonged months developing the tool of AI hiring. This is through feeding the resumes from the candidates at the top level. The function of AI has been making review of the resumes of candidates and then recommend the promising ones (Yapo and Weiss 2018). Since the industry has been dominated by males most of the resumes utilized for teaching the AI has been from men. This led to the AI discriminating against the women recommended. It resulted in discriminating against the women suggested. Moreover, the face-assessment programs of AI have been displaying racial and gender bias. It demonstrated the lesser errors to find out the gender of men who have light skins (Leavy 2018). This was against the high errors for finding the gender has for women with dark complexions. Apart from this, the voice-activated innovation in the cars are able to resolve the distracted driving. Nevertheless, various systems of vehicles have been tone-deaf towards the voice of women. Additionally, they had complexity to identify the accents of foreign language (Castro and New 2016).

The solution to the concern of gender bias in AI:

AI has not been the only objective. This technology with its algorithms are able to reflect the creator’s biases. Here, those with the unbiased at inception are also able to understand the biases of the human trainers in due time. This is intended to be programmed, audited, monitored and reviewed. This is to assure that this has never been biasing and turn the bias on the basis of the data and algorithms. Including more women and many diverse kinds of workers having technical expertise is a method to decrease bias (Savulescu and Maslen 2015). Through delivering the extra viewpoints and much security of failure creates the creation and training of AI to be much accurately put reflection on the inclusive and diverse societies. Further, higher diversity is also able to decline the thinking of the entire group and develop the decision making of them. It has been leveraging the greater variety of view-points for quicker and undertakes detailed decisions. The homogeneous teams of AI and the people conducting researches might never pay close attentions for finding the time of bias to get crept in. This scenario also involves the time when the scene affects the time AI is trained and created (Hacker 2018).

Various questions to be considered here:

• What are the strategies to diversify the talent pool in creating a diverse workforce with staffs from multiple backgrounds, languages, worldviews and perspectives?
• What are the programs can be instituted for rising the areas of the unconscious bias and the way to combat with that under the human workforce and the systems for artificial intelligence? How can the business assure that they ate hiring and the AI systems of talent management that are free from any bias?
• What is the process in place assuring that the business ate monitoring the algorithms routinely for the bias? In what way one can quickly address the bias as they find that t be creeping to the processes, actions and decisions of AI?
• What kinds of steps are to be developed for an inclusive workplace where the people can fee secured for speaking up? How can business the culture value accountability and respect? This is simpler for catching AI bias as the humans have been interacting with tools to understand they can be complex of present systems a place despite any adverse repercussions?
• What are the best practices ethical considerations and ethical regulations for the vigilance occurring against the perpetuating bias making the business champion under the AI space?

It concentrates in determining and eradicating bias in addressing gender distinction in hiring and tech education.

It is understood from the above study that artificial intelligence has been rousingly affecting the behavior and opinions of daily life. Nonetheless, the over-representation of male while developing the innovations can undo various decades of enhancements inequality of genders. In due time, human beings have come across critical theories for informed decisions and avoid that are based solely over experience at personal level. Nevertheless, machine intelligence has been found to learning mainly from the observing of information that has been seen to be presented with. As the ability of machine has been processing tremendous amount of information might able to address this, as the data gets laden with various stereotypical ideas of gender. This application of innovation can perpetuate bias. Few of the current studies have been fetching the ways of eradiating the bias from different learning algorithms ignoring in decades of study on how the ideology of gender has been embedded in the language. Various awareness of the research and including that towards various approaches of machine learning from the text is helpful to secure the creation of various based algorithms (Levendowski 2018). Different leading thinkers who are women suggested that people potentially impacted by the bias can see, attempt, and understand more likely to solve that. Therefore the gender bias is vital to secure algorithms from perpetuating the gender concepts that bring disadvantages to women. It is understood that the end-users and builders of various services and products that are AI-enabled is required for future. Through changing the women, roles and perception of females within the society, one can is able to correct the bugs at digital level perpetrating the current bias and make AI lifecycle to be trustworthy. Again, the technology is able to perform various impressive aspects and never resolve every issue for human beings. As one is unable to be careful this can end up with making the matters worse through institutionalizing the bias and the exacerbating the inequality. For securing that from any occurring, the business required to understand gender bias while implementing and developing artificial intelligence. Hence, the leaders must be understanding who are those liable to design and develop I in business and whether they come from diverse disciplines and backgrounds. It is to be also found out whether they can meet the diverse requirements of the stakeholders. Furthermore it is also to be understood how businesses can attract women to jobs in the field of artificial intelligence and how can one can re-skills women to bring benefits and use from AI applicants. Ultimately, the leaders need to evaluate whether they are creating suitable frameworks and policies for mandating gender equality at private and public areas around the full spectrum to the industries.

References

1. Caliskan, A., Bryson, J.J. and Narayanan, A., 2017. Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), pp.183-186.
2. Levendowski, A., 2017. How Copyright Law Creates Biased Artificial Intelligence. Washington Law Review, 579.
3. Zhao, J., Wang, T., Yatskar, M., Ordonez, V. and Chang, K.W., 2017. Men also like shopping: Reducing gender bias amplification using corpus-level constraints. arXiv preprint arXiv:1707.09457.
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6. Hacker, P., 2018. Teaching fairness to artificial intelligence: Existing and novel strategies against algorithmic discrimination under EU law. Common Market Law Review, 55(4), pp.1143-1185.
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