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The ever-increasing adoption of AI-powered systems in all areas of the economy could either lead to potential discrimination against women or open up new career prospects. Lets find out what needs to be done to achieve the second scenario.

For years, there have been debates that artificial intelligence will change the labor market. Nowadays the focus has shifted from the inevitability of the future to an assessment of how and when the world will change and what it means for all of us. There have been several attempts to create AI, but Artificial General Intelligence the one portrayed in films and books is still a long way from being built. What do we have now?

There are many narrow smart algorithms that solve niche tasks and can find patterns in large amounts of data.They help to make decisions on, for example, whether a suspicious transaction should be stopped or allowed, where cargo should be sent, or whether a loan should be granted.The probability of a mistake in these systems is lower comparing to a human beings performance.Further development of algorithms capable of supplementing or replacing human decision-making will reduce the number of roles requiring routine decision-making, which are often taken on by women. It is already difficult to imagine a bright future of tour operators, taxi dispatchers, clerks, and some other professions. According to a forecast made by IMFresearchers, 11% of jobs held by women will be automated within the next two decades. The focus will also shift into designing these algorithms.

More and more decisions are made automatically and, sadly, not in womens favor. For example, two years ago Amazon, the largest online retailer in the world, was forced to dismiss an automated CV-reviewing system because it discriminated against female developers.This situation occurred because the data used to train machine learning models represented a hiring history for 10 years. At that time, the majority of candidates were male, but this does not prove that men are better at technical work.Considering that algorithms are becoming in charge of making an ever-increasing number of decisions, similar side effects can be an issue.

It is widely believed that algorithms just find patterns within an existing data set. However, focusing on data, it is easy to forget two aspects of this problem: the limitations of existing algorithms and, more importantly, the role of people who train them. Most algorithms just catch the correlation within the data, without understanding anything about it. Even the best data is meaningless as long as people who can solve problems and ask the right questions are not involved, so the algorithms will simply reflect our own biases.

Developers of AI systems must carefully monitor the way datasets are formed and track any biases that might occur.Mistakes made by the algorithm should be tracked: sometimes the percentage of errors is quite low, but they could be related to one group of people.For example, a scoring model systematically refuses to give loans to residents of Chinatown. Such behavior is particularly dangerous because we are shifting more and more responsibility for decision-making onto the system. Some advanced algorithms which are currently in use cannot even be interpreted; i.e. we are unable to understand why a certain decision has been made at all, or which factors influenced it.

Its interesting to note that the rise of computer science is strongly connected with women:Ada Lovelace created programming, Betty Holberton designed the first general-purpose computer, and Margaret Hamilton developed the software for the Apollo project.Today, only15% of specialists in the field of artificial intelligence around the world are women, which is disappointing for many advocates of gender equality.Some people worry that the future will be created by men for men.The presence of female analysts eases the situation since they can spot the problems with products that are not easy to discover if you dont face discrimination on a daily basis.

Women already spend a lot of energy ensuring that they are treated honestly and fairly. Perhaps a legal base is needed. Some regulatory bodies have already taken up this issue. For example, the EU General Data Protection Regulation (GDPR) requires companies to explain the reasons behind decisions made by AI systems and to monitor them to prevent any discrimination. Currently, only large companies and governments can afford the software and hardware required to launch the most advanced AI models. This barrier could be used to help set up some basic rules before the technology becomes widely used.

Many futurists are optimistic about womens chances of success. Why? The most valuable skills in this brave new world are those that algorithms cannot master: the use of soft skills and emotional intelligence. As such, 83% of organizations surveyed byCapgemini believe that emotional intelligence will be a prerequisite for success in the coming years. The new labor market will value compassion, multi-tasking, cooperation, and empathy traits that are traditionally associated with women, meaning that women will have greater chances of being hired. However, there is a nuance here. To succeed in an AI-ruled world, a great work of retraining and adaptation is necessary. After all, it will be not the strongest and intelligent that survives, it is the one that is most adaptable to change.

Extra risks for women will be caused by an inability to adapt.The new challenges of automation are added to conventional difficulties, creating barriers to gender equality. Nowadays there is a need for mobility and flexibility among employees as it is now easier to change profession, employer, industry, and even country than ever before. Women are often less mobile than men, because of their second job at home. Whats more, they are oftenexcluded from networking, which allows men to improve their skills, find mentors, and new employment opportunities.

On the other hand, companies will be more motivated to push their employees to develop skills that cannot be automated. If women are proactive and able to adapt, they will have more employment opportunities.Bear in mind that being good at something no one needs is the biggest waste of time. In the future, two categories of skills will be most important: the ability to negotiate and standing your ground, and the ability to see trends and build strategies.

Organizations need employees who can talk to both machines and people. Recently, technologies have been significantly democratised, meaning that you do not need a PhD to work with AI.Today is the best time to gain insight on the foundations of a technology which at first seems complicated, even if your profession is not connected with data analysis at all.

It would be useful for executives to at least make themselves comfortable with the methods of machine learning for analyzing different types of data. Such analysis will make it possible to assess use cases for AI implementation in a specific area and to build an effective strategy for digital transformation. A deep dive into the topic and acquisition of practical skills, such as programming and creating machine learning models, will be useful for those who spend a lot of time running routine analysis and who want to automate the decision-making process.

Implemented projects from industries that are more mature when it comes to AI adoption, such as IT companies, banks, and retailers, can be good sources of inspiration. Even such a conservative industry as manufacturing has started digital transformation programs for production optimization, for example, to forecast machinery breakdowns.

According toIDC forecasts, by 2021 AI systems will be implemented in one form or another at 75% of enterprises. So, the relevant skills will be required in almost all industries. Allocate an hour and watch a video on the principles of machine learning. Only by having a clear understanding of how it works, it is possible to ask the right questions and set goals. Knowledge of a few principles is more important than understanding detailed implementation ways for all algorithms in a software package.

Once you understand the basic principles, its important to understand which tasks are the most relevant for applying AI and to try delegating some responsibilities to the machine to increase the quality of work and performance. By studying successful scenarios of AI application, many people realize that this technology can be used for a wide range of processes and tasks that involve working with large amounts of data.Women should learn how to find prospective use cases for new technologies, have sufficient motivation to look for answers, and take under control their education and career.

Data analysis skills are relevant to a huge number of professions, from marketing experts to mechanics working with a CNC machine. For example, AI can identify anomalies in a technological process. Currently, there is a lack of personnel in data science and artificial intelligence space. The problem is so urgent that large companies are offering free platformsfor studying. After the training, you may not be able to perform as good as an experienced data scientist, but you will be able to translate a task from a business domain for the researcher, for example, regarding an analysis of CV-collection or even writing music.

According to the Institute of Electrical and Electronics Engineers, an international non-profit association of specialists in the field of computer science, The need for specialists in artificial intelligence has revealed itself in almost every area of life. Experts are urging for AI training for specialists in areas such as healthcare, agriculture, and logistics.

Conventional methods of working are rapidly becoming obsolete. Humanity has to choose once again: to lament about fate and talk about the rise of the machines or to get ready for the future and acquire the skills that are in demand. The Luddites have lost in their fight against the machines, simply because machines are economically efficient. And, as we know now, after all the number of jobs created thanks to the adoption of machines has been far greater than the number of jobs gone.

The most insightful employees were not afraid of industrialization, and made use of and reaped the benefits of new technologies. So, why cant women do the same?

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Artificial Intelligence and Discrimination: To Be or Not To Be? - IoT For All

Automation during the industrial revolution led to a profound change in working practices across the 18th and 19th centuries. Currently, we are in the midst of a fourth industrial revolution, with globalisation and automation affecting every aspect of our working lives and leisure activities. The COVID-19 pandemic has provided a further driver for change, altering how we work and interact with remote working and reduction of human interaction at the centre of global initiatives to try and reduce the spread of the virus.

Healthcare systems generate large quantities of complex datasets pertaining to patients. Artificial Intelligence (AI) can offer solutions to medical problems by attempting to replicate decisions that would otherwise require human intelligence. Specific algorithms can be created in order to make associations between data and predict future outcomes. The COVID-19 pandemic has accelerated change within healthcare systems and driven interest in automated algorithms capable of assisting hospitals in diagnostics, decision making and repetitive clerical tasks thus reducing the potential footfall of staff on site. Automation and intelligent algorithms that learn and improve with further iterative cycles require data and the ethics behind large personal data sets, the challenges of anonymisation remain in their infancy.

AI applications within healthcare can be broadly categorised into diagnosis, research, management and system analysis. Ultimately in the time of COVID, workforce adaptations have led healthcare providers to use their workforce wisely, reducing the staff requirements on site and moving towards remote working.

Healthcare records in most countries have moved from paper-based records and notes to digital media. The electronic health record (EHR) allows the capture of large quantities of data across patient groups. Future patient care aims to develop tailored treatments for patients in a cost-effective manner.

The use of large datasets requires an element of data curation. Data needs to be retrieved from multiple disparate data sources. Data needs to then be cleaned to remove anomalies and harmonised to ensure similar data sets are compared across patient records. An element of these processes needs human oversight to ensure the correct data is fed into the algorithms.

Administrative applications are resource-heavy and repetitive. These applications include workflow management such as uploading referral letters from primary care, setting up referral assessment services and booking patients to the correct service provider in secondary care. Robotic process automation (RPA) consists of computer programmes that obey rules for these manual, resource-heavy tasks.

Additionally, process management systems (PMS), while embedded within commercial businesses, are still in their infancy within a healthcare application. Patients are individuals and tailoring care to them necessitates being able to react to changing physiological parameters within the confines of the organisation. In order to standardise care, clinical pathways have been developed to manage patient care from referral, to ordering diagnostic tests and eventual treatment pathway. While standardisation across patient groups allows for automation of some of these processes (templates for referral, standard test orders), allowing a more tailored approach which is patient-specific is the ultimate goal; thus generating a conflict to resolve between standardisation and a tailored patient approach specific to their individual needs.

Flexibility within a process management system requires technological skills to allow tasks to be postponed or reorganised. However, healthcare professionals lack the technical skillset to implement this, requiring a user-friendly interface. Further work in user interface and user experience (UI/UX) is therefore required to ensure a system that allows flexibility without losing the advantages of rapid automation and processing of large numbers of patients within the pathway management systems.

Increasingly call centre staff for websites have been replaced by chatbots that use natural language processing (NLP) to provide callers with information and manage queries. A chatbot is a type of AI programme that can conduct an intelligent conversation via text or auditory methods. It is predicted that by 2025 the global chatbot market will be worth $1.23bn. For hospitals dealing with upwards of 10,000 patient appointments per week, the use of chatbots to handle patient queries regarding appointment queries is still in its infancy when compared to more established sectors such as banking or commerce.

The current COVID-19 pandemic has highlighted the need for rapid screening and testing of patients to improve treatment pathways and also reduce the risk of cross infection. Clinical testing requires taking biological samples from patients which can be resource heavy and incorporates a time lag before results are available from real-time polymerase chain reaction testing (RT-PCR). The use of AI within this environment accessing electronic health records (EHR) of routinely ordered tests and vital signs can produce an effective tool to screen patients in emergency departments and hospital admission units.

Predictive analytics utilise AI algorithms to analyse healthcare data from EHRs to predict future outcomes thus aiming to improve outcomes and the patient experience as well as reducing costs. Data collected from EHRs can be supplemented with data from wearable technology and medical devices. Risk prediction models utilising AI would improve with successive data collection cycles aiming to supplement decision making by clinicians. Applications include management of chronic diseases such as chronic renal failure, diabetes and cardiovascular disease. Specific patient populations can vary across geographical healthcare providers and the ability of a predictive model to learn from its local population provides an advantage over established static modelling. Scalability across healthcare providers can therefore be challenging due to differences in socio-economic factors and populations based on geographical location. The ethical implications in terms of health insurance and risk stratification are in their infancy; and issues around data governance and data sharing may have a significant impact that is yet to be fully regulated.

Diagnostic applications of AI technological advances have exploded over the past ten years with multiple applications. Imaging studies such as breast mammograms or histological analysis rely on skilled scientists or clinicians performing repetitive tasks to manually identify abnormalities. An inaccurate diagnosis can have serious consequences for patient care. AI programmes can be trained to perform these tasks and have shown an accuracy in correctly diagnosing abnormalities which in some studies has been shown to be as accurate as a trained clinician. Further future applications in diagnostic imaging include the field of radiomics which extracts nuanced features peculiar to imaging modalities such as wavelength, texture and shape. This additional information can provide further data for diagnosis and prognostic indicators specific to patients.

With the potential application of AI within the healthcare setting, the question remains how will this impact the workforce? The fourth industrial revolution has 50% of companies to predict that by 2022, automation will decrease their numbers of full-time staff and that by 2030 robots will replace 800 million workers across the world (McKinsey Global Institute reports). Automation of clerical processes and care pathways could potentially impact on the non-clinical workforce within a healthcare setting. Specialties such as radiology where imaging reports can be automated and produced by AI algorithms may soon be the reality. The ethics of data sharing and the implications for patients and their insurers is a further area of controversy. We enter a brave new world.

Caroline B HingYasmin AntoniouAI for Goodwww.aiforgood.co.uk

This article is from issue 15 of Health Europa. Clickhere to get your free subscription today.

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Artificial Intelligence in healthcare and clinical practice in the COVID era - Health Europa

Since 2010, the Vatican Apostolic Library has worked to digitize its sprawling collection of more than 80,000 manuscripts, making a trove of rare historical treasures freely accessible to anyone with an internet connection.

But the tricky work of uploading the contents of the Roman Catholic Churchs historic library comes with new risks in the digital age. As Harriet Sherwood reports for the Observer, the library recently hired cybersecurity firm Darktrace to defend its digitized vault against attacks that could manipulate, delete or steal parts of the online collection.

Founded by University of Cambridge mathematicians, Darktrace uses artificial intelligence (A.I.) modeled on the human immune system to detect abnormal activity in the Vaticans digital systems, writes Brian Boucher for artnet News. On average, the A.I. system defends the library against 100 security threats each month, according to a Darktrace statement.

The number of cyber threats faced by the library continues to increase, its chief information officer, Manlio Miceli, tells the Observer. Threats to digital security come in many shapes and sizes, but Miceli notes that criminals can tamper with the librarys digitized files or conduct a ransomware attack, in which hackers effectively hold files ransom in exchange for a hefty sum.

While physical damage is often clear and immediate, an attack of this kind wouldnt have the same physical visibility, and so has the potential to cause enduring and potentially irreparable harm, not only to the archive but to the worlds historical memory, Miceli tells the Observer.

He adds, These attacks have the potential to impact the Vatican librarys reputationone it has maintained for hundreds of yearsand have significant financial ramifications that could impact our ability to digitize the remaining manuscripts.

Though the Vatican Library dates back to the days of the first Roman Catholic popes, little is known about the contents of its collections prior to the 13th century, per Encyclopedia Britannica. Pope Nicholas V (14471455) greatly expanded the collection, and by 1481, the archive held the most books of any institution in the Western world, according to the Library of Congress.

To date, about a quarter of the librarys 80,000 manuscripts have been digitized. As Kabir Jhala reports for the Art Newspaper, holdings include such treasures as Sandro Botticellis 15th-century illustrations of the Divine Comedy and the Codex Vaticanus, one of the earliest known copies of the Bible. Other collection highlights include notes and sketches by Michelangelo and the writings of Galileo.

The Vatican debuted the digitized version of its prized Vergilius Vaticanus in 2016. One of the few remaining illustrated manuscripts of classic literature, the fragmented text features Virgils Aeneid, an epic poem detailing the travels of a Trojan named Aeneas and the foundation of Rome. The ancient documentlikely crafted around 400 A.D. by a single master scribe and three paintersstill bears its vivid original illustrations and gilded lettering.

The library isnt the only section of the Vatican thats prone to cyber breaches. As the New York Times reported in July, Chinese hackers infiltrated the Holy Sees computer networks this summer ahead of sensitive talks in Beijing over the appointment of bishopspart of ongoing discussions that will determine how the Catholic Church operates in China.

The only way to make an organization completely secure is to cut it off from the internet, Miceli tells the Observer. Our mission is to bring the Vatican Library into the 21st centuryso we wont be doing that any time soon.

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Vatican Library Enlists Artificial Intelligence to Protect Its Digitized Treasures - Smithsonian Magazine

Accenture is a global professional services company with leading capabilities in digital, cloud and security. They are known for delivering unmatched experience and specialized skills across more than 40 industries, through their Strategy and Consulting, Interactive, Technology and Operations servicesall powered by the worlds largest network of Advanced Technology and Intelligent Operations centers. Their 506,000 people deliver on the promise of technology and human ingenuity every day, and serve clients in more than 120 countries. Accenture embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities.

Laetitia Cailleteau is the Managing Director, UKI Emerging Technology and Global Lead for Conversational AI for Accenture.She is also Accenture Technologys lead for Data & AI in Europe driving innovation, sales, and delivery for multiple industries and clients. She has more than 20 years of experience in the consulting industry, has authored several academic publications, and holds patents in the conversational AI domain. She founded the Liquid Studio in London, an applied innovation lab, where her team offers flagship capabilities within AI and cognitive processes, as well as offering expertise in augmented and virtual reality, Internet of Things, Cloud, Blockchain and Security, amongst other essential technology solutions, while practicing a user-centered approach. She is also a reserve member of the Artificial Intelligence High-Level Group at the European Commission.

During her early 30s, Laetitia found it challenging to juggle the responsibilities of client travel and being a new mother when she landed in the UK, in the middle of what was then the biggest recession in history.

For Laetitia, it was a roller-coaster of new emotions, insecurity and cultural adaptation. It demanded a drive and energy to keep her work and priorities relevant and impactful, and it was finding this balance that helped her center herself. Laetitia realized the importance of the support network which helped bring her new perspective from being a workaholic with not many responsibilities outside work, to being a working mother who needed to cater to so many things and reinvent herself. This support network concept had not struck her as essential before, but it quickly became a matter of survival.

The importance of time became very obvious for her, and so did choosing where to spend it. It was then that she started to engage with WomenInTech in Accenture and charities like riseUp and the Cherie Blair Foundation to help younger individuals from deprived backgrounds and fellow women find their own ways. These were mutually beneficial relationships, helping Laetitia as much as it helped them.

When Laetitia was promoted to Managing Director (MD) just before the birth of her third child, it opened up a number of new doors and presented the perfect opportunity for Laetitia to discover her capabilities through new responsibilities. I landed the perfect job I was asked to create our applied innovation capabilities in the UK. Even better I had a completely blank sheet of paper to start from, says Laetitia.

From there, things quickly gained traction the impact of her teams passion, skills and the technology they managed started to be recognized in both the UK market and on a global scale. Laetitia reminisces, This was when I started to pile-on second hats to my absolute delight, juggling many dimensions of this ever-growing space of Data & AI. She asserts that the breadth of people, clients, problems, and solutions she was interacting with daily helped further fuel her passion for her work.

Laetitia cites that keeping an eye out on the horizon and spotting market opportunities are integral to a leader with a visionary mindset. That skill, coupled with strong communication can enable leaders to demonstrate perseverance, acclimatize to changes, and challenges. They should be able to inspire people, and demonstrate the right cultural and community leadership.

Laetitia shares that the inspiration behind Accentures products usually comes from a mix of things colliding and forming an idea this can be a conversation with people or through conferences, TED talks, news, client challenges, internal targets and thought leadership played against a basis of knowledge of what seems possible or could possibly be. This is where the importance of a diverse team plays in. In addition to the above, surrounding oneself with a diversity of thinking can enable an employee to innovate for a range of target audiences. Upending the hierarchical corporate structure in an organizational group, breaking siloes, and promoting equal opportunities for younger colleagues to lead within the organization also goes a long way. This action dramatically speeds up the flow of ideas and the pace of innovation, and helps define if the ideas are worth pursuing.

Accenture is now a part of a very disruptive tech world. The company can digitize a whole new range of activities that were not possible before. These new technologies blur the boundaries of industries, enabling new levels of collaboration and creating new products and services, only dreamt of before.

Laetitia upholds the notion that the role of a leader in the digital age is to inspire and democratize this knowledge and ensure all employees are on board, while being attentive to the benefits and responsibilities shared across every team and every person within the business.

Laetitia confirms that the 4th industrial revolution has formally kick-started. Companies are aiming to switch towards more human-centered services from industry siloes.Disruptive technologies like AI, machine learning, and big data enable organizations to work smarter and focus on the more rewarding aspects of work and boost productivity.

Hopefully equating to less work and longer weekend for most of us! says Laetitia humorously.

That being said, Laetitia forsees a new world that will be much more inclusive and balanced. She feels that our current mental model of the various phases of life university, work, retirement is somewhat outdated and needs to synch-up with modern life.

Laetitia advises budding leaders to, first and foremost, elevate their individual work in the market and public sphere, get perspectives, take it all in and be relentless with things they believe in. She further encourages to listen, adapt, and not be afraid of getting knocked down, as long as you get back up! According to her, one must chase their dreams and go after things one is passionate about.

Laetitia also affirms that there will be female leaders cheerleading young minds on the way to the top and asks that one must also make sure to lend a hand to pull others up too.

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Laetitia Cailleteau: Accelerating the Future of Artificial Intelligence with Disruptive Innovation - Analytics Insight