Monthly Archives: September 2021

We aggregated thousands of records from GlobalDatas proprietary jobs, deals, patents and company filings databases to identify the top companies in the area of artificial intelligence in the technology sector.

International Business Machines Corp and Microsoft Corp are leading the way for artificial intelligence investment among top technology companies according to our analysis of a range of GlobalData data.

Artificial intelligence has become one of the key themes in the technology sector of late, with companies hiring for increasingly more roles, making more deals, registering more patents and mentioning it more often in company filings.

These themes, of which artificial intelligence is one, are best thought of as any issue that keeps a CEO awake at night, and by tracking and combining them, it becomes possible to ascertain which companies are leading the way on specific issues and which are dragging their heels.

According to GlobalData analysis, International Business Machines Corp is one of the artificial intelligence leaders in a list of high-revenue companies in the technology industry, having advertised for 8,040 positions in artificial intelligence, made seven deals related to the field, filed 461 patents and mentioned artificial intelligence 10 times in company filings between January 2020 and June 2021.

Our analysis classified 15 companies as Most Valuable Players or MVPs due to their high number of new jobs, deals, patents and company filings mentions in the field of artificial intelligence. An additional four companies are classified as Market Leaders and zero are Average Players. Two more companies are classified as Late Movers due to their relatively lower levels of jobs, deals, patents and company filings in artificial intelligence.

For the purpose of this analysis, weve ranked top companies in the technology sector on each of the four metrics relating to artificial intelligence: jobs, deals, patents and company filings. The best-performing companies the ones ranked at the top across all or most metrics were categorised as MVPs while the worst performers companies ranked at the bottom of most indicators were classified as Late Movers.

Microsoft Corp is spearheading the artificial intelligence hiring race, advertising for 15,092 new jobs between January 2020 and June 2021. The company reached peak hiring in March 2021, when it listed 1,495 new job ads related to artificial intelligence.

International Business Machines Corp followed Microsoft Corp as the second most proactive artificial intelligence employer, advertising for 8,040 new positions. Dell Technologies Inc was third with 5,323 new job listings.

When it comes to deals, Tencent Holdings Ltd leads with 29 new artificial intelligence deals announced from January 2020 to June 2021. The company was followed by Microsoft Corp with 19 deals and Apple Inc with nine.

GlobalData's Financial Deals Database covers hundreds of thousands of M&A contracts, private equity deals, venture finance deals, private placements, IPOs and partnerships, and it serves as an indicator of economic activity within a sector.

One of the most innovative technology companies in recent months was Samsung Electronics Co Ltd, having filed 1,271 patent applications related to artificial intelligence since the beginning of last year. It was followed by Intel Corp with 505 patents and International Business Machines Corp with 461.

GlobalData collects patent filings from 100+ counties and jurisdictions. These patents are then tagged according to the themes they relate to, including artificial intelligence, based on specific keywords and expert input. The patents are also assigned to a company to identify the most innovative players in a particular field.

Finally, artificial intelligence was a commonly mentioned theme in technology company filings. Google, Inc. mentioned artificial intelligence 12 times in its corporate reports between January 2020 and June 2021. Intel Corp filings mentioned it 12 times and Microsoft Corp mentioned it 12 times.

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Which companies are leading the way for artificial intelligence in the technology sector? - Verdict

Researchers at the University of Birmingham have developed a new way to identify which patients with heart failure will benefit from treatment with beta-blockers.

Heart failure is one of the most common heart conditions, with substantial impact on patient quality of life, and a major driver of hospital admissions and healthcare cost.

The study involved 15,669 patients with heart failure and reduced left ventricular ejection fraction (low function of the hearts main pumping chamber), 12,823 of which were in normal heart rhythm and 2,837 of which had atrial fibrillation (AF) - a heart rhythm condition commonly associated with heart failure that leads to worse outcomes.

Published in The Lancet, the study used a series of artificial intelligence (AI) techniques to deeply interrogate data from clinical trials.

The research showed that the AI approach could take account of different underlying health conditions for each patient, as well as the interactions of these conditions to isolate response to beta-blocker therapy. This worked in patients with normal heart rhythm, where doctors would normally expect beta-blockers to reduce the risk of death, as well as in patients with AF where previous work has found a lack of effectiveness. In normal heart rhythm, a cluster of patients was identified with reduced benefit from beta-blockers (combination of older age, less severe symptoms and lower heart rate than average). Conversely in patients with AF, the research found a cluster of patients who had a substantial reduction in death with beta-blockers (from 15% to 9% in younger patients with lower rates of prior heart attack but similar heart function to the average AF patient).

The research was led by the cardAIc group, a multi-disciplinary team of clinical and data scientists at the University of Birmingham and the University Hospitals Birmingham NHS Foundation Trust, aiming to integrate AI techniques to improve the care of cardiovascular patients. The study uses data collated and harmonized by the Beta-blockers in Heart Failure Collaborative Group, a global consortium dedicated to enhancing treatment for patients with heart failure.

First Author Dr Andreas Karwath, Rutherford Research Fellow at the University of Birmingham and member of the cardAIc group, added: We hope these important research findings will be used to shape healthcare policy and improve treatment and outcomes for patients with heart failure.

Corresponding author Georgios Gkoutos, Professor of Clinical Bioinformatics at the University of Birmingham, Associate Director of Health Data Research Midlands and co-lead for the cardAIc group, said: Although tested in our research in trials of beta-blockers, these novel AI approaches have clear potential across the spectrum of therapies in heart failure, and across other cardiovascular and non-cardiovascular conditions.

Corresponding author Dipak Kotecha, Professor & Consultant in Cardiology at the University of Birmingham, international lead for the Beta-blockers in Heart Failure Collaborative Group and co-lead for the cardAIc group, added: Development of these new AI approaches is vital to improving the care we can give to our patients; in the future this could lead to personalised treatment for each individual patient, taking account of their particular health circumstances to improve their well-being.

The research used individual patient data from nine landmark trials in heart failure that randomly assigned patients to either beta-blockers or a placebo. The average age of study participants was 65 years, and 24% were women. The AI-based approach combined neural network-based variational autoencoders and hierarchical clustering within an objective framework, and with detailed assessment of robustness and validation across all the trials.

The research was presented this week at the ESC Congress 2021, hosted by the European Society of Cardiology - a non-profit knowledge-based professional association that facilitates the improvement and harmonisation of standards of diagnosis and treatment of cardiovascular diseases.

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Artificial Intelligence approach helps to identify patients with heart failure that respond to beta-blocker treatment - University of Birmingham

Times of crisis spark innovation and creativity, as evidenced in the way organizations have come together to innovate for the greater good during the COVID-19 pandemic.

Liquor distilleries started producing hand sanitizer, 3D printing companies made face shields and nasal swabs to meet massive demandsand auto companies shifted gears to make ventilators.

Machine learning (ML)computer systems that learn and adapt autonomously by using algorithms and statistical models to analyze and draw inferences from patterns in data to inform and automate processeshas also played an important role, supporting practically every aspect of healthcare. Amazon Web Services has supported customers as they enable remote patient care, develop predictive surge planning to help manage inpatient/ICU bed capacityand tackle the unprecedented feat of developing an messenger ribonucleic acid (mRNA)-based COVID-19 vaccine in under a year.

We now have the opportunity to build on our lessons from the past year to apply ML to help address several underlying problems that plague the healthcare and life sciences communities.

Telehealth was on the rise before COVID-19, but it revealed its true potential during the pandemic. Telehealth is often viewed simply as patients and providers interacting online via video platforms but has proven capable of doing much more. Applying ML to telehealth provides a unique opportunity to innovate, scale and offer more personalized experiences for patients and ensure they have access to the resources and care they need, no matter where they're located.

ML-based telehealth tools such as patient service chatbots, call center interactions to better triage and direct patients to the information and care they requireand online self-service prescreenings are helping optimize patient experiences and streamline provider assessments and diagnostics.

RELATED:Global investment in telehealth, artificial intelligence hits a new high in Q1 2021

For example, GovChat, South Africa's largest citizen engagement platform, launched a COVID-19 chatbot in less than two weeks using an artificial intelligence (AI) service for building conversational interfaces into any application using voice and text. The chatbot provides health advice and recommendations on whether to get a test for COVID-19, information on the nearest COVID-19 testing facility, the ability to receive test resultsand the option for citizens to report COVID-19 symptoms for themselves, their family membersor other household members.

In addition, early in the COVID-19 crisis, New York City-based MetroPlusHealth identified approximately 85,000 at-risk individuals (e.g., comorbid heart or lung disease, or immunocompromised) who would require additional support services while sheltering in place. In order to engage and address the needs of this high-risk population, MetroPlusHealth developed ML-enabled solutions including an SMS-based chatbot that guides people through self-screening and registration processes, SMS notification campaigns to provide alerts and updated pandemic informationand a community-based organizations referral platform, called Now Pow, to connect each individual with the right resource to ensure their specific needs were met.

By providing an easy way for patients to access the care, recommendationsand support they need, ML has given providers the ability to innovate and scale their telehealth platforms to support diverse and continuously changing community needs. Agile, scalableand accessible telehealth continues to be important as providers look for ways to reach and engage patients in hard-to-reach or rural areas and those with mobility issues. Organizations and policymakers globally need to make telehealth and easy access to care a priority now and going forward in order to close critical gaps in care.

Beyond the unprecedented shifts in the approach to engaging, supporting and treating patients, COVID-19 has dictated clear direction for the future of patient care: precision medicine.

Guidelines for patient care planning care have shifted from statistically significant outcomes gathered from a general population to outcomes based on the individual. This gives clinicians the ability to understand what type of patient is most prone to have a disease, not just what sort of disease a specific patient has. Being able to predict the probability of contracting a disease far in advance of its onset is important to determining and initiating preventative, intervening, and corrective measures that can be tailored to each individual's characteristics.

RELATED:What's on the horizon for healthcare beyond COVID-19? Cerner, Epic and Meditech executives share their takes

One of the best examples of how ML is enabling precision medicine is biotech company Modernas ability to accelerate every step of the process in developing an mRNA vaccine for COVID-19. Moderna began work on its vaccine the moment the novel coronaviruss genetic sequence was published. Within days, the company had finalized the sequence for its mRNA vaccine in partnership with the National Institutes of Health.

Moderna was able to begin manufacturing the first clinical-grade batch of the vaccine within two months of completing the sequencinga process that historically has taken up to 10 years.

Personalized health isn't only about treating disease, it's about providing access to resources and information specific to a patient's needs. ML is playing a key role in curating content that can help to educate and support patients, caregivers and their families.

Breastcancer.org allows individuals with breast cancer to upload their pathology report to a private and secure personal account. The organization uses ML-based natural language processing to analyze and understand the report and create personalized information for the patient based on their specific pathology.

RELATED:Healthcare AI investment will shift to these 5 areas in the next 2 years: survey

For the last decade, organizations have focused on digitizing healthcare. Today, making sense of the data being captured will provide the biggest opportunity to transform care. Successful transformation will depend on enabling data to flow where it needs to be at the right time while ensuring that all data exchange is secure.

Interoperability is by far one of the most important topics in this discussion. Today, most healthcare data is stored in disparate formats (e.g., medical histories, physician notes and medical imaging reports), which makes extracting information challenging. ML models trained to support healthcare and life sciences organizations help solve this problem by automatically normalizing, indexing, structuring and analyzing data.

ML has the potential to bring data together in a way that creates a more complete view of a patient's medical history, making it easier for providers to understand relationships in the data and compare specific data to the rest of the population. Better data management and analysis leads to better insights, which lead to smarter decisions. The net result is increased operational efficiency for improved care delivery and management, and most importantly, improved patient experiences and health outcomes.

Looking ahead, imagine a time when our pernicious medical conditions like cancer and diabetes can be treated with tailored medicines and care plans enabled by AI and ML. The pandemic was a turning point for how ML can be applied to tackle some of the toughest challenges in the healthcare industry, though we've only just scratched the surface of what it can accomplish.

Taha Kass-Hout is the director of machine learning for Amazon Web Services.

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Industry VoicesWhy the COVID-19 pandemic was a watershed moment for machine learning - FierceHealthcare

The Covid-19 pandemic has accelerated the development of artificial intelligence across the globe.

Organizations are using artificial intelligence to increase the productivity ofremote workers, enhance the virtual shopping experience, drive the digital transformation process and speed up the development of important drugs to end this on-going pandemic.

Real artificial intelligence is creating value by making humans more efficient, not redundant.

There are several levels ofknowledge, research, education, theory, practice, and technology:

Specialization: Narrow AI, Specialists, Scientists, Learned Ignoramus, which divides, specializes, and thinks inspecialcategories.

Disciplinarity: Analytical science and traditionally fragmenteddisciplines.

Interdisciplinarity: Itintegrates information, data, techniques, tools, concepts, and/or theories from within two or more disciplines.

Interdisciplinarity is about the interactions between specialised fields and cooperation among special disciplines to solve a specific problem. It concerns the transfer of methods and concepts from one discipline to another, allowing research to spill over disciplinary boundaries, still staying within the framework of disciplinary research.

Transdisciplinarity:Synthetic science and technology and society,the ideas of a unified scienceand technology and human society,universalknowledge, synthesis and the integration ofallknowledge, total convergence of knowledge, technology and people, Trans-AI = Narrow AI, ML, DL + Symbolic AI + Human Intelligence.

Transdisciplinarity is radically distinct from interdisciplinarity, multidisciplinarity and mono-disciplinarity.

Transdisciplinarity analyzes,synthesizes and harmonizes links between disciplines into a coordinated and coherent whole, a global system where all interdisciplinary boundaries dissolve.

It is aboutaddressingthe worlds most pressing issuesandseeing the worldin asystemic,consistent, andholisticway at three levels:

(1) theoretical, (2) phenomenological, and (3) experimental (which is based on existing data in a diversity of fields, such asexperimental science and technology, business,education, art, and literature).

Transdisciplinarity is a way of being radically distinct from interdisciplinarity, as well as multidisciplinarity and mono-disciplinarity.

Transdisciplinarity integrates the natural, social, andengineeringsciences in aunifyingcontext, a whole that is greater than the sum of its partsand transcends their traditional boundaries.

Transdisciplinarityconnotes a research strategy that crosses many disciplinary boundaries to create a holistic approach.

Transdisciplinary research integrates information, data, concepts, theories,techniques, tools, technologies, people, organizations, policies, and environments,asall sides of the real-world problems.

Transdisciplinarity takes this integration of disciplines on the highest level. It is a holistic approach, placing these interactions in an integral system. It thus builds a total network of individual disciplines, with a view to understand the world in terms of integrity and unity and discovery.

Monodisciplinary: Itinvolvesa single academic discipline.Itrefers to a single discipline or body of specialized knowledge.

Multidisciplinarity: Itdraws on knowledge from different disciplines but stays within their boundaries.Inmultidisciplinarity, two or more disciplines work together on a common problem, but without altering their disciplinary approaches or developing a common conceptual framework.

In the context oftheunprecedented worldwidepandemic-enhancedcrises, the transdisciplinarityappears asan all-sustainableway ofsolving complex real-world problemspursuinga general search for a unity of knowledgeor Real-World AI.

The Trans-AI paradigm means that the classic studies of Plato, Aristotle, Kant, Leibnizs Logic as Calculation and Booles Logic as Algebra withmodern ontological, scientific, mathematical and statistical research of reality/knowledge/intelligence/data formalization/computing/automation are a key to [Real] AI.

For example, the conception of AI was inherently implied in Aristotles Analytics, Prior and Posterior, Metaphysics/Ontology and Categories.

Without the reality/category theory, as the mind theory for human minds, and prior data analytics, no deep AI/ML/DL classifiers with effective classification algorithms are possible, where classes are targets, labels, or categories. ML/DL predictive modeling is NOT just the task of approximating a mapping function (f) from input variables (X) to output variables (y). Therefore, it is widely recognized that the lack of reality with causality is the black hole of current machine learning systems.

The Trans-AI is about the real-world data ontology, causality, real intelligence, science, computer model, semantics and syntax and pragmatics, universal knowledge/data synthesis vs. expert knowledge/data analytics, thus enabling a comprehensive machine understanding of data points, elements, sets, patterns, and relationships.

Without comprehensive causal worlds models integrating disciplinary, inter-, multi-, and trans-disciplinary knowledge, there is no real-world AI. A holistic research strategy integrating worlds knowledge into a meaningful whole is the systematic way of building the General Human-AI Platform as an Integrative General-Purpose Technology.

The current disciplinary approach to AI/ML/DL and Robotics is, at best or worst for humanity, ending up with superhuman narrow human-mimicking AI applications, integrated in our smart networks, devices. processes and services.

Some, who limit AI as augmenting or substituting biological intelligence with machine intelligence, believe transdisciplinarity is a way to a human-level AI.

The mono-disciplinary narrow AI of machine deep learning is blooming today, bringing its stakeholders unprecedented profits.Five top-performing tech stocks in the market, namely, Facebook, Amazon, Apple, Microsoft, and Alphabets Google, FAAMG, represent the U.S.'sNarrow AI technology leaderswhose productsspan machine learning and deep learning or data analytics cloud platforms, mobile and desktop systems, hosting services, online operations, and software products. The five FAAMG companies had a joint market capitalization of around $4.5 trillion a year ago, and now exceed $7.6 trillion, being all within the top 10 companies in the US.As to the modest Gartner's predictions, the total NAI-derived business value is forecast to reach $3.9 trillion in 2022.

The future superhuman narrow AI applications are here, within us, in our smart networks, devices. processes and services.

Special-designed automated intelligence outperforms humans in strategic games, chess/go playing, video gaming, self-driving mobility, stock trading, financial transactions, medical diagnosis, NLP, language translation, patterns/object/face recognition, manufacturing processes, etc.

And it is ONLY the narrow AI/ML/DL fragmented applications designed for narrow human-like tasks and jobs, as more efficient and effective than human labor, mental or menial.

The existential question isWhen Will Robots/Machines/Computers Emerge as a General-Purpose Real-World AI?

But most people are still blind to see the disruptive fundamental force of AI technology, its critical impact on our future.

Our company is proud to inform that EIS Encyclopedic Intelligent Systems LTD has completed studying, modeling, and designing the Real-World AI as a Causal Machine Intelligence and Learning, trademarked as Causal Artificial Superintelligence (CASI) GPT Platform complementing human intelligence, collective and individual.

Thecurrent disciplinary approach to AI/ML/DL and Robotics is ending up with superhumannarrow AI applications,integratedin our smart networks, devices. processes and services.

Special-designed automated intelligence outperforms humans in strategic games, chess/go playing, video gaming, self-driving mobility, stock trading, financial transactions, medical diagnosis, NLP, language translation, patterns/object/face recognition, manufacturing processes, etc.

It isstillONLY the narrowAnthropomorphic and AnthropocentricAI/ML/DL fragmented applications designed for narrow human-like tasks and jobs.Many scientists are trying to move the field of AI beyond data analytics, predictions and pattern-matching towards machines that could solve real-world problems. Some people think it might be enough to take what we have and just grow the size of the dataset, the model sizes, computer speedto just get a bigger brain (Conference on Neural Information Processing Systems (NeurIPS 2019) Yoshua Bengio)

Still, theexistentialquestionis open: What IfRobots/Machines/Computerswere toOutsmartHumans in allspecialrespects?

To address themoral and existentialissues ofdisciplinaryAI/ML/DL and robotics fragmentation,as Europes Responsible and Trustworthy AI,we have developeda TransdisciplinaryRealAI model, as not competing with, but complementing human intelligence.

The Transdisciplinary AIConferences are now emerging,but still considered as an interdisciplinary collection ofacademic research themes:

Transdisciplinary AI 2021 (TransAI 2021) is technically sponsored by the IEEE Computer Society.

Trans-AI aims to integrate disciplinary AIs, symbolic/logical or statistic/data, asML Algorithms (DL,ANNs), which are designed to substitute biological intelligence with machine intelligence.

Trans-AI is developed as a Man-Machine Global AI (GAI) Platform to integrate Human Intelligence with Narrow AI, ML, DL, Human-level AI, or Superhuman AI, all as Neural Information Processing Systems. It relies on fundamental scientific worlds knowledge, cybernetics, computer science, mathematics, statistics, data science, computing ontologies, robotics,psychology, linguistics, semantics, and philosophy.

The Trans AI model is mapped as an interdependent, mutually reinforcing, transdisciplinary quadrivium of the worlds knowledge depicted by the global knowledge graph (see the extended version).

The Trans-AI isa systematic, holistic and analytical means of obtaining knowledge about the world.

The Trans-AI is technologically designed as a Causal Machine Intelligence and Learning Platform, to be served as Artificial Intelligence for Everybody and Everything, AI4EE.

The Trans-AI technology could make the most disruptive general-purpose technology of the 21st Century, given an effective ecosystem of innovative business, government, policy-makers, NGOs, international organizations, civil society, academia, media and the arts.

TheTrans-AI asHuman-AI Global Platform is designed to extract knowledge from massive digital data forcreatingbreakthroughs in all parts of human life, from government to industry to education to healthcare to global security.

It isaimedtoprocess structured and unstructured digital data within unifying world-intelligence-data models and causal algorithms, shifting from supervised to self-supervised real learning. Making breakthroughs in these areas will be the matter of life or death for thefuture ofhumanity.

Why Trans-AI could be the disruptive discovery, innovation and unifying general-purpose technologyand the best smart investment

The Trans-AI could be the most disruptive research and breakthrough discovery, innovation and technology meetingthe founding fathers of AIdreamsto make machines use language, form abstractions and concepts,Google mission to organize the worlds information and make it universally accessible and useful, and best human ambitions for a unified knowledge of the world.

Among other disruptive changes, the Trans-AI enriches, updates and scales up the disciplinary AIs, as proposed by the EC'sHIGH-LEVEL EXPERT GROUP ON ARTIFICIAL INTELLIGENCE:

Artificial intelligence (AI) refers to systems that display intelligent behaviour by analysing their environment and taking actions with some degree of autonomy to achieve specific goals. AI-based systems can be purely software-based, acting in the virtual world (e.g. voice assistants, image analysis software, search engines, speech and face recognition systems) or AI can be embedded in hardware devices (e.g. advanced robots, autonomous cars, drones or Internet of Things applications).

The most concern of humanity must be the current accelerated growth of Big Techs Narrow and Weak AI of Machine Learning, ANNs and Deep Learning, as a Non-Real AI vs. Real World AI. It is fast emerging as narrow-minded automated super intelligences outperforming humans in any narrow cognitive tasks, and implemented as LAWs or military AI, ML/DL drones, killer robots, humanoid robots, self-driving transportation, smart manufacturing machines, RPAs, cyborgs, trading algorithms, smart government decision makers, recommendation engines, medical AI system, etc.

The whole idea of Anthropomorphic and Anthropocentric AI (AAAI) as the narrow or general ones, aimed at simulating human intelligence, cognitive skills, capacities, capabilities, and functions, as well as intelligent behavior and actions in computing machines is raising a number of undecidable social, moral, ethical and legal dilemmas.

The narrow and weak Deep-Learning AI programs classify tremendous amounts of data without any understanding of the world and meaning of their inputs or outputs (e.g., the recommendation to treat or a risk score or behaviour changes).

These consequences could be much worse than human cloning, which is prohibited in most countries, and massive technological unemployment without any compensation effects is just the beginning of the end.

This is what good minds forewarned humanity about the possibilities and possible perils of AAAI, mimicking human learning and reasoning by machines and humanoid robots:

The development of full artificial intelligence could spell the end of the human raceItwould take off on its own, and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldnt compete, and would be superseded. Stephen Hawking told the BBC

I visualise a time when we will be to robots what dogs are to humans, and Im rooting for the machines. Claude Shannon

Im increasingly inclined to think that there should be some regulatory oversight, maybe at the national and international level, just to make sure that we dont do something very foolish. I mean with artificial intelligence were summoning the demon. Elon Musk warned at MITs AeroAstro Centennial Symposium

All that we need, is a radically new kind of AI, Real and True MI, Real World AI, the Trans-AI, which is to simulate and understand or compute reality, causality, and mentality in digital reality machines.

This is becoming clear even for profit-seeking industrialists, as E. Musk, who understands that without the Real-World AI no really intelligent machine is possible. Self-driving requires solving a major part of real-world AI, so its an insanely hard problem, but Tesla is getting it done. AI Day will be great. Nothing has more degrees of freedom than reality.

The rise of real artificial intelligence will create and destroy new jobs, improve healthcare, disrupt smart cities, and minimize the impact of the next pandemic. Despite the concerns about the dark side of artificial intelligence, we are still far away from super artificial intelligence.

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The Need of A Real-World Artificial Intelligence in The Pandemic Era - BBN Times

Article by ManageEngine director of research Ramprakash Ramamoorthy.

Artificial intelligence (AI) has long been a feature of modern technology and is becoming increasingly common in workplace technologies. According to ManageEngines recent 2021 Digital Readiness Survey, more than 86% of organisations in Australia and New Zealand reported increasing their use of AI even as recently as two years ago.

But despite an increased uptake across organisations in the A/NZ region, only 25% said their confidence in the technology had significantly increased.

One possible reason for the lack of overall confidence in AI is the potential for unethical biases to work their way into developing AI technologies. While it may be true that nobody sets out to build an unethical AI model, it may only take a few cases for disproportionate or accidental weighting to be applied to certain data types over others, creating unintentional biases.

Demographic data, names, years of experience, known anomalies, and other types of personally identifiable information are the types of data that can skew AI and lead to biased decisions. In essence, if AI is not properly designed to work with data, or the data provided is not clean, this can lead to the AI model generating predictions that could raise ethical concerns.

The rising use of AI across industries subsequently increases the need for AI models that arent subject to unintentional biases, even if this occurs as a by-product of how the models are developed.

Fortunately, there are several ways developers can ensure their AI models are designed as fairly as possible to reduce the potential for unintentional biases. Two of the most effective steps developers can take are:

Adopting a fairness-first mindset

Embedding fairness into every stage of AI development is a crucial step to take when developing ethical AI models. However, fairness principles are not always uniformly applied and can differ depending on the intended use for AI models, creating a challenge for developers.

All AI models should have the same fairness principles at their core. Educating data scientists on the need to build AI models with a fairness-first mindset will lead to significant changes in how the models are designed.

Remaining involved

While one of the benefits of AI is its ability to reduce the pressure on human workers to spend time and energy on smaller, repetitive tasks, and many models are designed to make their own predictions, humans need to remain involved with AI at least in some capacity.

This needs to be factored in throughout the development phase of an AI model and its application within the workplace. In many cases, this may involve the use of shadow AI, where both humans and AI models work on the same task before comparing the results to identify the effectiveness of the AI model.

Alternatively, developers may choose to keep human workers within the operating model of the AI technology, particularly in cases where an AI model doesnt have enough experience, which will let them guide the AI.

The use of AI will likely only continue to increase as organisations across A/NZ, and the world, continue to digitally transform. As such, its becoming increasingly clear that AI developments will need to become even more reliable than they currently are to reduce the potential for unintentional biases and increase user confidence in the technology.

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Why ethics is essential in the creation of artificial intelligence - IT Brief Australia