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Eliminating AI Bias

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The primary purpose of Artificial Intelligence (AI) is to reduce manual labour by using a machine's ability to scan large amounts of data to detect underlying patterns and anomalies in order to save time and raise efficiency. However, AI algorithms are not immune to bias. As AI algorithms can have long-term impacts on an organisation's reputation and severe consequences for the public, it is important to ensure that they are not biased towards a particular subgroup within a population. In layman's terms, algorithmic bias within AI algorithms occurs when the outcome is a lack of fairness or a favouritism towards one group due to a specific categorical distinction, where the categories are ethnicity, age, gender, qualifications, disabilities, and geographic location. If this in-depth educational content is useful for you, subscribe to our AI research mailing list to be alerted when we release new material. AI Bias takes place when assumptions are made incorrectly about the dataset or the model output during the machine learning process, which subsequently leads to unfair results. Bias can occur during the design of the project or in the data collection process that produces output that unfairly represents the population. For example, a survey posted on Facebook asking about people's perceptions of the COVID-19 lockdown in Victoria finds that 90% of Victorians are afraid of travelling interstate and overseas due to the pandemic. This statement is flawed because it is based upon individuals that access social media (i.e., Facebook) only, could include users that are not located in Victoria, and may overrepresent a particular age group (i.e. To effectively identify AI Bias, we need to look for presence of bias across the AI Lifecycle shown in Figure 1.


Machine learning model uses clinical and genomic data to predict immunotherapy effectiveness

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The forecasting tool assesses multiple patient-specific biological and clinical factors to predict the degree of response to immune checkpoint inhibitors and survival outcomes. It markedly outperforms individual biomarkers or other combinations of variables developed so far, according to findings published in Nature Biotechnology. With further validation, the tool may help oncologists better identify patients most likely to benefit from ICB. Discerning, prior to treatment, patients for whom ICB would be ineffective could reduce unnecessary expense and exposure to potential side effects. It could also indicate the need to pursue alternate treatment strategies, such as combination therapies. "It's important to know which treatment modalities patients are most suited for," said Dr. Chan, director of Cleveland Clinic's Center for Immunotherapy & Precision Immuno-Oncology.


Why digital transformation success depends on good governance

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The COVID-19 crisis forced businesses everywhere to fast track their digital transformation efforts. Faced with the stark choice of becoming a digital-first business, or having no business at all, companies that were previously behind the curve had to implement everything from remote working to entire digital storefronts in a matter of days. According to research by McKinsey, the digital initiatives unleashed in response to the pandemic leapfrogged seven years of progress in a matter of months as companies acted 20 to 25 times faster than they had believed was possible. In the process, this acceleration of digital during the crisis brought about a sea change in executive mindsets with regard to the role of technology in business. Fast forward to today, and corporate leaders are now investing in technology for competitive advantage, refocusing their entire business around cutting-edge technologies, and initiating a business culture where experimentation and innovation is actively encouraged.


Artificial intelligence in oncology: current applications and future perspectives – Docwire News

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Br J Cancer. 2021 Nov 26. doi: 10.1038/s41416-021-01633-1. Online ahead of print. ABSTRACT. Artificial intelligence (AI) is concretely reshaping …


If AI only had a brain: Is the human mind the best model to copy?

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Tristan covers human-centric artificial intelligence advances, quantum computing, STEM, Spiderman, physics, and space stuff. Pronouns: He/hi (show all) Tristan covers human-centric artificial intelligence advances, quantum computing, STEM, Spiderman, physics, and space stuff. The Holy Grail of AI research is called "general artificial intelligence," or GAI. A machine imbued with general intelligence would be capable of performing just about any task a typical adult human could. The opposite of general AI is narrow AI – the kind we have today.


The 9 Principles of Ethical AI in Healthcare Industry

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It is noticed that AI systems are not neutral and not providing valid outcomes. AI in Healthcare can raise ethical issues and can harm patients by not giving intended outcomes. Therefore it is necessary to use Ethical AI in the health industry. Akira AI provides Ethical AI systems that are taking care of ethical issues and values. The 9 principles that are responsible for ethical AI in healthcare and provide a framework to help technologists while designing, developing, or maintaining systems.


Global Big Data Conference

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Natural Language Processing (NLP) In Healthcare And Life Sciences report is an established source of information that presents with a telescopic view of the current market trends, situations, opportunities and status. Moreover, this market report gives idea to the clients about market drivers and restraints with the help of SWOT analysis and also provides all the CAGR projections for the historic year 2018, base year 2019 and forecast period of 2020-2027. The geometric data brought together to generate this report is mostly denoted with the graphs, tables and charts which make this report more user-friendly. This global Natural Language Processing (NLP) In Healthcare And Life Sciences market report can be relied upon for sure when thinking about key business decisions. Natural Language Processing (NLP) In Healthcare And Life Sciences Market analysis provides a high-level summary of classification, competition, and strategic actions taken in recent years.


AI must have its own goals to be truly intelligent

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Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. Is it the capacity to solve complicated mathematical problems at very fast speeds? The power to beat world champions in chess and go? The ability to detect thousands of different objects in images? Those are all manifestations of intelligence. And thanks to advances in artificial intelligence, we have been able to replicate them in computers, to different degrees of success.


The 4 Top Artificial Intelligence Trends For 2021

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Before the global pandemic struck in 2020 and the world was turned on its head, artificial intelligence (AI), and specifically the branch of AI known as machine learning (ML), were already causing widespread disruption in almost every industry. The Covid-19 pandemic has impacted many aspects of how we do business, but it hasn't diminished the impact AI is having on our lives. In fact, it's become apparent that self-teaching algorithms and smart machines will play a big part in the ongoing fight against this outbreak as well as others we may face in the future. AI undoubtedly remains a key trend when it comes to picking the technologies that will change how we live, work, and play in the near future. So, here's an overview of what we can expect during what will be a year of rebuilding our lives as well as rethinking business strategies and priorities.


Building value-chain resilience with AI

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Across industries, value chains are facing increasing uncertainty from climatic anomalies, market volatility, and the COVID-19 pandemic, among other factors. Industries as diverse as agriculture, oil and gas, and mining face essentially the same problem: they need the ability to both run with increased efficiency and recover quickly from unforeseen or unexpected challenges. But these two goals often conflict. If companies simply increase production levels, they'll inevitably run into bottlenecks--and if failures occur that worsen those bottlenecks, the entire network can slow down and become less resilient. For more on how COVID-19 has affected supply chains, see Knut Alicke, Richa Gupta, and Vera Trautwein, "Resetting supply chains for the next normal," July 21, 2020. Resolving this conflict presents several challenges.