FTCs Tips on Using Artificial Intelligence (AI) and Algorithms – The National Law Review

Artificial intelligence (AI) technology that uses algorithms to assist in decision-making offers tremendous opportunity to make predictions and evaluate big data. The Federal Trade Commission (FTC), on April 8, 2020, provided reminders in its Tips and Advice blog post,Using Artificial Intelligence and Algorithms.

This is not the first time the FTC has focused on data analytics. In 2016, it issued a Big Data Report. Seehere.

AI technology may appear objective and unbiased, but the FTC warns of the potential for unfair or discriminatory outcomes or the perpetuation of existing socioeconomic disparities. For example, the FTC pointed out, a well-intentioned algorithm may be used for a positive decision, but the outcome may unintentionally disproportionately affect a particular minority group.

The FTC does not want consumers to be misled. It provided the following example: If a companys use of doppelgngers whether a fake dating profile, phony follower, deepfakes, or an AI chatbot misleads consumers, that company could face an FTC enforcement action.

Businesses obtaining AI data from a third-party consumer reporting agency (CRA) and making decisions on that have particular obligations under state and federal Fair Credit Reporting Act (FCRA) laws. Under FCRA, a vendor that assembles consumer information to automate decision-making about eligibility for credit, employment, insurance, housing, or similar benefits and transactions may be a consumer reporting agency. An employer relying on automated decisions based on information from a third-party vendor is the user of that information. As the user, the business must provide consumers an adverse action notice required by FCRA if it takes an adverse action against the consumer. The content of the notice must be appropriate to the adverse action, and may consist of a copy of the consumer report containing AI information, the federal summary of rights, and other information. The vendor that is the CRA has an obligation to implement reasonable procedures to ensure the maximum possible accuracy of consumer reports and provide consumers with access to their own information, along with the ability to correct any errors. The FTC is seeking transparency and the ability to provide well-explained AI decision-making if the consumer asks.

Takeaways for Employers

Carefully review use of AI to ensure it doesnotresult in discrimination. According to the FTC, for credit purposes, use of an algorithm such as a zip code could result in a disparate impact on a particular protected group.

Accuracy and integrity of data is key.

Validation of AI models is important to minimizing risk. Post-validation monitoring and periodic re-validation is important as well.

Review whether federal and state FCRA laws apply.

Continue self-monitoring by asking:

How representative is your data set?

Does your data model account for biases?

How accurate are your predictions based on big data?

Does your reliance on big data raise ethical or fairness concerns?

The FTCs message: use AI, but proceed with accountability and integrity.

Jackson Lewis P.C. 2020

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FTCs Tips on Using Artificial Intelligence (AI) and Algorithms - The National Law Review

How Artificial Intelligence is Changing the Auto Industry – Legal Examiner

For more than seven decades, Artificial Intelligence (AI) has been the talking point of a technological revolution. As stated by John McCarthy, the father of Artificial Intelligence, Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. In simpler terms, AI is the ability of a digital machine to make decisions and perform tasks associated with humans. AI deals with analyzing how a human brain thinks and how it learns, decides, and acts in a situation.

Artificial Intelligence (AI) presents never-ending opportunities to revolutionize technology in every industrial sector, and the automobile industry is not untouched by AI. For example, the autonomous or self-driven car is the hotspot in the latest research, and every car manufacturer is investing heavily in it. IHS Automotive predicts that by the end of 2020, there will be more than 150 million AI-powered cars. Before discussing the application areas of AI in cars and their accessories, lets highlight the benefits AI offers in the automobile sector:

Car manufacturers are already using several AI features like voice-control, lane-switch, collision-detection, etc. to improve driver safety. As technology evolves, car accessories like video cameras, sensors, etc. are using AI to provide maximum comfort to the drivers. Lets take a look at how AI is improving the car Industry:

Before we adapt to fully-autonomous cars, it makes sense to evaluate the capabilities of AI by incorporating driver-assist features. AI uses several sensors for blind-spot monitoring, collision detection, pedestrian detection, lane monitoring, etc. to identify dangerous situations and alert the driver accordingly. Similarly, AI-based algorithms can analyze the data from vibration sensors to detect anomalies. Moreover, with new technology coming up, you could determine the load theroof rackis carrying which can help prevent overloading.

With AI, the concept of maintenance shifts from preventive to predictive one. Rather than depending on the event-driven or time-driven approaches for scheduling the maintenance, AI can help in providing actionable insights for your car maintenance. In addition to the historical data, AI uses sensors and contextual data like geographic or weather details. By analyzing the data and through machine learning, AI can offer alerts for real-time condition-based maintenance requirements for your car.

According to the history of the driver, AI can predict the issues resulting from his absent-mindedness. By analyzing the driving pattern, AI can predict the risk that might arise from the drivers personal life or professional life. Similarly, by using fatigue monitoring devices, AI can monitor the vitals of the driver to alert him and take control of the vehicle in case of an emergency. An AI-driven camera can track drowsiness in the driver and trigger an alarm.

With AI, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication is possible. With such technology, your car can communicate with other vehicles, as well as the road signs, traffic signals, etc. By enabling vehicles to communicate with each other, you can seamlessly enjoy advanced features like lane monitoring, lane switching, cruise control, etc. Similarly, V2I communication allows you to re-route your vehicle to avoid congested roads. In a nutshell, enhanced communication reduces the chances of accidents and takes you to your destination with less hassle.

The insurance sector deals with managing data from several fields, and AI offers immense potential for improvement. For example, an in-car camera can record accidents that might be helpful during legal or insurance settlements. Similarly, AI can quickly process the data and make the claim-settlement process faster. Using the data analyzing properties of AI, one can even prepare profiles of drivers and check the fraudulent claims.

Apart from elevating the driving experience, AI can transform the way we build cars as well. For over five decades, machines have helped in the assembly lines of the vehicle manufacturers. However, by using AI, we can develop smart robots that work alongside their human counterparts rather than working for them. For example, AI helps in designing autonomous delivery vehicles to transport components in aplant. Similarly, smart, wearable robots work collaboratively with workers to offer up to 20% increase in production efficiency.

AI in the automobile sector promises to change the way we drive cars. The benefits of the AI car accessories are already visible, and its potential is endless. The rewards and opportunities of AI in elevating the overall safety and driving experience attract huge interest by tech-giants as well as startups.

The application areas mentioned above give you a flavor of the AI in the car accessories market. From making the car safer to predicting the maintenance, from easing the insurance claim process to providing hi-tech features, AI caters to the all-round improvement in the driving quality.

https://www.linkedin.com/pulse/how-artificial-intelligence-machine-learning-auto-models-mishanin

https://www.t3.com/features/5-car-innovations-that-are-right-around-the-corner

https://hackernoon.com/what-is-the-role-of-ai-in-future-cars-52c6632ec6cd

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How Artificial Intelligence is Changing the Auto Industry - Legal Examiner

Artificial Intelligence in Agriculture Market Worth $4.0 Billion by 2026 – Exclusive Report by MarketsandMarkets – PRNewswire

CHICAGO, April 28, 2020 /PRNewswire/ -- According to the new market research report "Artificial Intelligence in Agriculture Marketby Technology (Machine Learning, Computer Vision, and Predictive Analytics), Offering (Software, Hardware, AI-as-a-Service, and Services), Application, and Geography - Global Forecast to 2026", published by MarketsandMarkets, the Artificial Intelligence in Agriculture Marketis estimated to be USD 1.0 billion in 2020 and is projected to reach USD 4.0 billion by 2026, at a CAGR of 25.5% between 2020 and 2026. The market growth is driven by the increasing implementation of data generation through sensors and aerial images for crops, increasing crop productivity through deep-learning technology, and government support for the adoption of modern agricultural techniques.

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By application, drone analytics segment projected to register highest CAGR during forecast period

The market for drone analytics is expected to grow at the highest rate due to its extensive use for diagnosing and mapping to evaluate crop health and to make real-time decisions. Favorable government mandates for the use of drones in agriculture are also expected to fuel the growth of the drone analytics market. Increasing awareness among farm owners regarding the advantages associated with AI technology is expected to further fuel the growth of the AI in agriculture market.

By technology, computer vision segment to register highest CAGR during forecast period

The increasing use of computer vision technology for agriculture applications, such as plant image recognition and continuous plant health monitoring and analysis, is one of the major factors contributing to the growth of the computer vision segment. The other factors include higher adoption of robots and drones in agriculture farms and increasing demand for improved crop yield due to the rising population. Computer vision allows farmers and agribusinesses alike to make better decisions in real-time.

Browsein-depth TOC on"Artificial Intelligence in Agriculture Market"81 Tables 40 Figures 152 Pages

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AI in agriculture market in APAC projected to register highest CAGR from 2020 to 2026

The AI in agriculture market in Asia Pacific is expected to witness the highest growth during the forecast period. The wide-scale adoption of AI technologies in agriculture farms is the key factor supporting the growth of the market in this region. AI is increasingly applied in the agriculture sector in developing countries, such as India and China. The increasing adoption of deep learning and computer vision algorithm for agriculture applications is also expected to fuel the growth of the AI in agriculture market in the Asia Pacific region.

International Business Machines Corp. (IBM) (US), Deere & Company (John Deere) (US), Microsoft Corporation (Microsoft) (US), Farmers Edge Inc. (Farmers Edge) (Canada), The Climate Corporation (Climate Corp.) (US), ec2ce (ec2ce) (Spain), Descartes Labs, Inc. (Descartes Labs) (US), AgEagle Aerial Systems (AgEagle) (US), and aWhere Inc. (aWhere) (US) are the prominent players in the AI in agriculture market.

Related Reports:

Artificial Intelligence Marketby Offering (Hardware, Software, Services), Technology (Machine Learning, Natural Language Processing, Context-Aware Computing, Computer Vision), End-User Industry, and Geography - Global Forecast to 2025

Artificial Intelligence in Manufacturing Marketby Offering (Hardware, Software, and Services), Technology (Machine Learning, Computer Vision, Context-Aware Computing, and NLP), Application, Industry, and Geography - Global Forecast to 2025

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Artificial Intelligence in Agriculture Market Worth $4.0 Billion by 2026 - Exclusive Report by MarketsandMarkets - PRNewswire

MIT conference reveals the power of using artificial intelligence to discover new drugs – MIT News

Developing drugs to combat Covid-19 is a global priority, requiring communities to come together to fight the spread of infection. At MIT, researchers with backgrounds in machine learning and life sciences are collaborating, sharing datasets and tools to develop machine learning methods that can identify novel cures for Covid-19.

This research is an extension of a community effort launched earlier this year. In February, before the Institute de-densified as a result of the pandemic, the first-ever AI Powered Drug Discovery and Manufacturing Conference, conceived and hosted by the Abdul Latif Jameel Clinic for Machine Learning in Health, drew attendees including pharmaceutical industry researchers, government regulators, venture capitalists, and pioneering drug researchers. More than 180 health care companies and 29 universities developing new artificial intelligence methods used in pharmaceuticals got involved, making the conference a singular event designed to lift the mask and reveal what goes on in the process of drug discovery.

As secretive as Silicon Valley seems, computer science and engineering students typically know what a job looks like when aspiring to join companies like Facebook or Tesla. But the global head of research and development for Janssen the innovative pharmaceutical company owned by Johnson & Johnson said its often much harder for students to grasp how their work fits into drug discovery.

Thats a problem at the moment, Mathai Mammen says, after addressing attendees, including MIT graduate students and postdocs, who gathered in the Samberg Conference Center in part to get a glimpse behind the scenes of companies currently working on bold ideas blending artificial intelligence with health care. Mathai, who is a graduate of the Harvard-MIT Program in Health Sciences and Technology and whose work at Theravance has brought to market five new medicines and many more on their way, is here to be part of the answer to that problem. What the industry needs to do, is talk to students and postdocs about the sorts of interesting scientific and medical problems whose solutions can directly and profoundly benefit the health of people everywhere he says.

The conference brought together research communities that rarely overlap at technical conferences, says Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science, Jameel Clinic faculty co-lead, and one of the conference organizers. This blend enables us to better understand open problems and opportunities in the intersection. The exciting piece for MIT students, especially for computer science and engineering students, is to see where the industry is moving and to understand how they can contribute to this changing industry, which will happen when they graduate.

Over two days, conference attendees snapped photographs through a packed schedule of research presentations, technical sessions, and expert panels, covering everything from discovering new therapeutic molecules with machine learning to funding AI research. Carefully curated, the conference provided a roadmap of bold tech ideas at work in health care now and traced the path to show how those tech solutions get implemented.

At the conference, Barzilay and Jim Collins, the Termeer Professor of Medical Engineering and Science in MITs Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering, and Jameel Clinic faculty co-lead, presented research from a study published in Cell where they used machine learning to help identify a new drug that can target antibiotic-resistant bacteria. Together with MIT researchers Tommi Jaakkola, Kevin Yang, Kyle Swanson, and the first author Jonathan Stokes, they demonstrated how blending their backgrounds can yield potential answers to combat the growing antibiotic resistance crisis.

Collins saw the conference as an opportunity to inspire interest in antibiotic research, hoping to get the top young minds involved in battling resistance to antibiotics built up over decades of overuse and misuse, an urgent predicament in medicine that computer science students might not understand their role in solving. I think we should take advantage of the innovation ecosystem at MIT and the fact that there are many experts here at MIT who are willing to step outside their comfort zone and get engaged in a new problem, Collins says. Certainly in this case, the development and discovery of novel antibiotics, is critically needed around the globe.

AIDM showed the power of collaboration, inviting experts from major health-care companies and relevant organizations like Merck, Bayer, Darpa, Google, Pfizer, Novartis, Amgen, the U.S. Food and Drug Administration, and Janssen. Reaching capacity for conference attendees, it also showed people are ready to pull together to get on the same page. I think the time is right and I think the place is right, Collins says. I think MIT is well-positioned to be a national, if not an international leader in this space, given the excitement and engagement of our students and our position in Kendall Square.

A biotech hub for decades, Kendall Square has come a long way since big data came to Cambridge, Massachusetts, forever changing life science companies based here. AIDM kicked off with Institute Professor and Professor of Biology Phillip Sharp walking attendees through a brief history of AI in health care in the area. He was perhaps the person at the conference most excited for others to see the potential, as through his long career, hes watched firsthand the history of innovation that led to this conference.

The bigger picture, which this conference is a major part of, is this bringing together of the life science biologists and chemists with machine learning and artificial intelligence its the future of life science, Sharp says. Its clear. It will reshape how we talk about our science, how we think about solving problems, how we deal with the other parts of the process of taking insights to benefit society.

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MIT conference reveals the power of using artificial intelligence to discover new drugs - MIT News

What Opportunities are Appearing Thanks to AI, Artificial Intelligence? – We Heart

The AI sector is booming. Thanks to several leaps that have been made, we are closer than ever before to developing an AI that acts and reacts as a real human would do. Opportunities in this sector are flourishing, and there is always a way for you to get involved.

Photo by Annie Spratt.

Employees: If you are searching for a job in the tech sector, one of the most rewarding you could find is working with AI. It is a mistake to assume that all AI development is focussed on developing android technologies. There are many other applications for AI and each one needs experts at the helm to help bring it to fruition.

Whether you are a graduate, or you are looking for a change in careers, there is always a job opening that you could look into. Even if you dont have a background in this tech, there are many other ways you could get involved, whether you are working on an AIs cognitive abilities or even just testing out the product. Whatever your background and skillset might be, there is always a way for you to get involved.

Investors: AI development is incredibly costly. While many of the smaller developers may have a great idea that could be world-changing if they bring it to fruition. However, they often lack the finances to be able to do so. This is where investors can come in.

Investors like Tej Kohli, James Wise, or Jonathan Goodwin may have little expertise in these areas from their own personal experience, but they know how to recognise a viable idea when presented with one. Whether you are looking to get into venture investment yourself or you are a tech company looking for financial backing, their activities should give you some idea about the paths you need to follow.

Photo, Bence Boros.

Consumers: The world of AI isnt just open to investors and tech gurus. There is now a vast range of AI-driven tech emerging onto the market. You, as a consumer, get to be an instrumental part of driving this new tech forward as it means that the developers gain some insight into what features are popular and which arent.

Just look at the boom in home assistants that has erupted in the past few years. We are now able to live in fully functioning smart homes with music playing and lights turning off with a simple voice command. By exploring what AI has to offer through the role of the consumer, this all feeds back to the developers and helps them create the next generation of products.

No matter how interested you are in this sector, there is always going to be something you can pursue that will help to develop AI overall. This is an incredibly exciting era to live in, and AI is just one of the pieces of tech that could transform the world as we know it. Take a look at some of the roles and opportunities and see where you could jump in today.

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What Opportunities are Appearing Thanks to AI, Artificial Intelligence? - We Heart

How artificial intelligence is helping scientists find a coronavirus treatment – Brandeis University

Photo/Getty Images

An illustration of COVID-19

By Julian Cardillo '14April 27, 2020

More than 50,000 academic articles have been written about COVID-19 since the virus appeared in November.

The volume of new information isnt necessarily a good thing.

Not all of the recent coronavirus literature has been peer reviewed, while the sheer number of articles makes it challenging for accurate and promising research to stand out or be further studied.

Computer science and linguistics professor James Pustejovsky is leading a Brandeis team in creating an artificial intelligence platform called Semantic Visualization of Scientific Data or SemViz that can sort through the growing mass of published work on coronavirus and help biologists who study the disease gain insights and notice patterns and trends across research that could lead to a treatment or cure.

Pustejovsky, an expert in theoretical and computational modeling and language, is partnering with colleagues at Tufts University, Harvard University, the University of Illinois, and Vassar College. He discussed his work with BrandeisNOW.

Can you provide a birds-eye view of the way youve applied your background as a computational linguist to current coronavirus research?

Im a researcher who focuses on language and extracting information from large amounts of text, like the COVID-19 dataset, which now includes more than 50,000 academic articles. Biologists on the front lines of coronavirus are trying to find connections between genes, proteins and drugs, and how they interact with the virus in the cells of the human body.

SemViz combs through the existing papers and manuscripts and enables scientists to make connections and generalizations that are not obvious from reading one paper at a time.

So how might a biologist studying coronavirus actually use SemViz?

This tool gives a rapid way for biologists studying coronavirus to see a global overview of inhibitors, regulators, and activators of genes and proteins involved in the disease.

For example, what are the drugs and proteins regulating the receptor for the COVID-19 virus? This could help discover therapies that decrease the expression of the receptor for the virus in patients lungs. This is important because millions of people currently take blood pressure medicines that can alter this receptor and possibly increase their risk of contracting the disease.

SemViz creates a visualization landscape that helps biologists make both global and specific connections between human genes, drugs, proteins and viruses. The overall program Im working on contains three components: two semantic visualization outputs based on the entire coronavirus research dataset, as well as a natural language-based question-answering application.

Whats the language application grid and how does it work?

It is essentially a computer-based reading machine that interprets tens of thousands of research articles on coronavirus and presents the results of this process to biologists in a form that is visually accessible and easily analyzed and interpreted.

It is more informative than a search engine, because it utilizes a host of language understanding tools and AI that can be applied to different domains (economics, news, science, literature) and text types (tweets, articles, books, email).

What are the implications of SemViz?

I think its hard to overstate the challenge brought about by information overload, particularly now with the coronavirus literature.

Biologists are interested in the mechanisms and functions of specific chemicals and proteins. SemViz can be the roadmap that scientists use to sort through large amounts of research to find these kinds of functions and relationships.

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How artificial intelligence is helping scientists find a coronavirus treatment - Brandeis University

Mayo Clinic is using artificial intelligence in its COVID-19 research – KIMT 3

ROCHESTER, Minn. - Artificial intelligence has a vital role in helping researchers in their efforts to fight COVID-19 and is an important tool in the work being done at Mayo Clinic.

Dr. Andrew Badley is an infectious diseases specialist and leads Mayo Clinics COVID-19 Research Task Force. He explains thatthey created a real-time tracking platform to measure the rate of positive cases throughout all counties in Minnesota.

"When we did that, we noticed that there was an outlier which occurred in Martin County. The rate of a positive test in Martin County was approaching ten percent, whereas the rate of positive testing for most of the other counties was in the neighborhood of one or two percent. Based on that, we said were probably not doing enough testing in Martin County. We redeployed tests to that area. Weve deployed personal protective equipment to the healthcare workers in that area who were doing the tests. Quite rapidly we investigated, we identified a significant number of additional cases. After we identified those cases, we counseled on self-quarantining and therapy as indicated. And wed like to think that doing that activity has helped to prevent new transmission," said Dr. Badley.

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Mayo Clinic is using artificial intelligence in its COVID-19 research - KIMT 3

Artificial Intelligence breaks barriers where policymakers may go wrong – The Nation

The COVID-19 outbreak has highlighted the importance of working on public health and technology together in order to fight the crisis. Countries across the world are opting for different measures where several technologies are at play to tap the positive COVID-19 cases to stop the further spread of the virus.

China was the first country to report COVID-19 cases and is now witnessing the return of normalcy, but it also had to resort to technology to contain the spread. China used technologies such as smart imaging, drones and mobile apps totrace virus-carrying individuals.

The US and Europe, however, took a slightly different approach, using data derived via artificial intelligence to stop the spread of the virus. One such data provider is US-based Mobilewall, which serves countries with data to serve public health.

In an interview with Sputnik,Anind Datta, the CEO and chairman ofMobilewall, a consumer intelligence platform that is working with US task forces and other municipalities to fight the coronavirus, reflects on the importance of the use of artificial intelligence technologies to deal with the present-day crisis, especially in highly densely populated regions like South Asia.

Question: Where has Mobilewall successfully carried out data distribution?

Anind Datta:Mobilewall data is being used by health services organizations and governmental entities around the world to better predict the spread of the Novel Coronavirus at both the macro (city/county/state/country) and micro (predicting patients at a hospital) level. Mobilewall is working with various businesses and municipalities, providing data around individual mobility that acts as a proxy for social distancing. We can provide both a social isolation score and separate data attributes, features that can be used to build a custom score. Such data includes individual mobility metrics (indicating the daily distance traveled and unique locations), cluster identification (gatherings of a high number of devices) and individual device data at both the micro and macro levels. These are all foundational inputs that can be used in COVID-19 prediction models.

Question: In a country where a huge population resides in rural areas, how can AI be implemented?

Anind Datta:The purpose ofAI is to support decision makingby revealing patterns that emerge from large amounts of data. AI is particularly useful in scenarios where (a) data can be collected at a scale allowing reliable patterns to emerge, and (b) where manual efforts to both collect and analyse data do not work well.

In remote rural areas, manual data collection is challenging, and even if possible, such data is reliability-challenged due to the social barriers against honest disclosures of questions perceived as personal. In the current COVID-19 crisis, where data collection involves gathering information about personal habits and symptoms related to infection, these impediments only increase. Yet, a lot of this information can be gathered from behaviour exhibited on mobile phones, which have spread well into India's rural areas. Mobile data, accumulated at a scale, can allow for inferences to be made to help critical decision-making both in urban and rural areas.

Sputnik: Please, describe the ways in which AI and data can be used to battle COVID-19.

Anind Datta:In the context of COVID-19, data and AI technologies are being used in new ways, particularly in countries that adopt a scientific approach to public health. Data scientists are creating machine learning models to predict infection and mortality rates and to determine resource needs and allocation based on these predictions.

AI can be used to power two key tasks of pandemic mitigation: infection tracking and infection spread prediction. If done correctly, AI can help uncover three foundational pieces of information, crucial to tracking and predicting the spread: measuring social isolation by observing individual mobility, identifying clusters of more than a certain number of individuals and identifying the corresponding locations; and risk assessment of individuals and locations, at scale, by understanding the movement of infected individuals.

Question: Do you have some suggestion for the government regarding use of AI in slums and high density population?

Anind Datta:AI is particularly suited for analysing large amounts of data collected via machines. In slums and other high density areas, in context of the COVID-19 crisis, it is difficult to both maintain and track social distancing. For this reason, these regions can be triggers of infection waves that could provide deadly for the entire country. AI offers a mechanism to both collect and track behavioural signals from this area, which can then inform early-warning and alert systems that can drive tactical pandemic management activities.

AI, particularly,big data and machine learning techniquescan be used to identify the infection risk of individuals, which can then be projected to those individuals and others in the geographic locations they have visited. Data scientists are creating models to track the spread of the virus and to determine resource needs and allocation based on the prediction of hard-hit areas. AI is an enabler; it identifies patterns and provides insights at speeds well beyond what humans can do manually.

But, the key to the successful use of AI relies on the data that is being fed into the models. If this data is inaccurate or lacks scale the ability of the model to predict outcomes will be impacted in a negative way. Data can be obtained in various ways, either by requesting information directly from individuals (such as what populous countries is attempting to do with the Arogya Setu app or by seeking data from other available sources.

Question: Government's have been advocating app's which is also a mobile platform to fight against COVID-19. How useful is app in terms of contact tracing?

Anindya Datta:Arogya Setu app is a worthy effort and could serve as a useful consumer tool to minimise risky behaviour and receive current COVID-19 information. However, it is important to understand that the app by itself is simply a front end to information delivery. The effectiveness of the app is only as good as the information it has access to, but the app itself is not producing that information.

The quality of the risk information and therefore, the usefulness of the app, depend on a number of variables outside of the control of the app, including the magnitude of infection detection, which depends on testing. It is easy to see that less the testing, lower the value of the information disseminated via the app. What also matters is the risk models that are being used to build risk scores for geographies and sub-geographies. If the risk models are ineffective, even with adequate testing, the information delivered will be of little value.

In South Asia, where social stigma still plays a key part in social interaction, one might question the likelihood of truthful disclosures at scale.

Another, perhaps more reliable option, is to use other available data sources that can model the activities of the population at scale. In many cases location data and behavioural data can be used as inputs to COVID-19 predictive models.

Question: Certain groups have been opposing the medics. Can AI help medics find ways to track them without going to the location?

Anind Datta:Yes, location data of these groups can help doctors to track them. Location-based data can be used to track individual mobility without in-person engagement. Depending on the source of the data, it is also possible to use this data to communicate risk of infection in an anonymous manner using digital identification or communication through mobile devices.

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Artificial Intelligence breaks barriers where policymakers may go wrong - The Nation

New research shows Artificial Intelligence still lags behind humans when it comes to recognising emotions – Dublin City University

New DCU led research into the accuracy of artificial intelligence when it comes to reading emotions on our faces has shown that it still lags behind human observers when it comes to being able to tell whether were happy or sad. The difference was particularly pronounced when it came to spontaneous displays of emotion.

The recently published study, A performance comparison of eight commercially available automatic classifiers for facial affect recognition, looked at eight out of the box automatic classifiers for facial affect recognition (artificial intelligence that can identify human emotions on faces) and compared their emotion recognition performance to that of human observers.

It found that the human recognition accuracy of emotions was 72% whereas among the artificial intelligence tested, the researchers observed a large variance in recognition accuracy, ranging from 48% to 62%.

The work was conducted by Dr. Damien Dupr from Dublin City Universitys Business School, Dr. Eva Krumhuber from the Department of Experimental Psychology at UCL, Dr. Dennis Kster from the Cognitive Systems Lab, University of Bremenand Dr. Gary J. McKeown from the Department of Psychology at Queens University Belfast.

Key data points

How the study was done

Two well-known dynamic facial expression databases were chosen: BU-4DFE from Binghamton University in New York and the other from The University of Texas in Dallas.

Both are annotated in terms of emotion categories, and contain either posed or spontaneous facial expressions. All of the examined expressions were dynamic to reflect the realistic nature of human facial behavior.

To evaluate the accuracy of emotion recognition, the study compared the performance achieved by human judges with those of eight commercially available automatic classifiers.

Dr. Damien Dupr said

AI systems claiming to recognise humans emotions from their facial expressions are now very easy to develop. However, most of them are based on inconclusive scientific evidence that people are expressing emotions in the same way.

For these systems, human emotions come down to only six basic emotions, but they do not cope well with blended emotions.

Companies using such systems need to be aware that the results obtained are not a measure of the emotion felt, but merely a measure of how much ones face matches with a face supposed to correspond to one of these six emotions."

Co-author Dr. Eva Krumhuber from UCL added

AI has come a long way in identifying peoples facial expressions, but our research suggests that there is still room for improvement in recognising genuine human emotions.

Dr. Krumhuber recently led a separate study published in Emotion (also involving Dr. Kster) comparing human vs. machine recognition across fourteen different databases of dynamic facial expressions.

Researchers

Dr. Damien Dupr - Business School, Dublin City University

Dr. Eva Krumhuber - Department of Experimental Psychology, UCL

Dr. Dennis Kster - Cognitive Systems Lab, University of Bremen

Dr. Gary J. McKeown - Department of Psychology, Queens University Belfast

Photo byAndrea PiacquadiofromPexels

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New research shows Artificial Intelligence still lags behind humans when it comes to recognising emotions - Dublin City University

The ethics of artificial intelligence and automation amid a global pandemic – Morning Star Online

BRITAINS healthcare crisis has been catapulted centre stage recently as our beloved NHS warriors battle Covid-19 deprived of personal protective equipment.

Yet these indispensable heroes have long toiled exhaustinghours, struggling under a lethal concoction of heightened demand and a lack of resources,a Brexodus of staff and the coronavirus pandemic is just icing on the crumbling cake.

Vacancies are widespread. In social care, it is estimated shortages have spiralled to 122,000.

Thousands of retirees have flocked to the NHS frontlines these past few weeks, but once Britainreturns to some degree of normality, the post-Brexit immigration plan swoops in to exacerbate the vacancies once more.

Migrant healthcare staff will face exorbitant visa fees as soon as December while care workers flatly wont even qualify for a skilled worker visa.

However, the Home Office has designed a rather unorthodox and arguably unscrupulous alternative: to replace the grit and graft of flesh-and-blood migrant staff with artificial intelligence and automated robots.

Already the initiative has swallowed 284million allinall, but the government will need to pluck some more golden leaves from its magic money treeif it is to realistically transform sectors that are reliant on EU labour with automation in nine months time.

Still, the practicality of this mission when the Home Office is notorious at underdelivering and delaying projects is one thing when there is a hot debate over whether care robotsshould be wheeled in at all.

The inclusion of technology in healthcare is often framed as a step towards depravity: it invokes a consensus that society inches towards a dystopian nightmare where humans become enslaved to sentient androids, 15m jobs become sacrificed at the altar of an AI-modified world and military killer machines surpass human intelligence to bring a nuclear winter to the human race.

Stephen Hawking himself did warn us of this possibilityas have Google employees who walked out in defiance of AI warfare.

Still, job losses seem quite inevitable, but should this be an acceptable consequence of progress?

Labour MPYvette Cooperargued that a technological revolution could further entrench the stark inequalities that already exist in Britain and make extreme poverty a permanent part of our social fabric.

And she isnt wrong: Japan, at the core of technological enlightenment, has seen automation overthrowmultiple industries with robot-run hotels, restaurants and conveyor belts of food being common.

Yet others, tech giants and their allies of dreamers, envision a post-work utopia where tech bridges societies into a new world of fullyautomated luxury communismand where its gains are shared equally by all.

On this note, it is ironic that Covid-19 has pressed the Home Office to make a dramatic U-turn from its submissive acceptance of job losses.

Commiting to pay 80 per centof workers wages who are most likely to become affected by automation in the next 20 years might seem like a change of heart, but sceptics might best believe that the infrastructure for automation just simply isnt ready yet and the cogs are still needed to keep the economy oiled up until this point.

In terms of healthcare, however, there are additional concerns.

AI still bitterly lacks the empathy required for the job while algorithms are shown to absorb the darkest depths of human biases.

AI systems can only look at the world through the peripheral granted to it by its makers who, by and large, are mostly male and white.

The result has seen recognition software repeatedly misinterpret facial expressions and body language on the assumption that everyone expresses themselves in the same way as Westerners while AI favours men over women in job interviews and even prefers European-American names over African-American ones.

Not that the government pays much attention to this acute factor: its very own visa algorithm has been found to discriminate against applicants of a certain nationality.

At a very basic level, one would expect care robotsto be equipped to administer some form of care.

Yet humanoids lack the intellectual problem-solving and altruism needed to adhere to physically demanding and emotionally intuitive surroundings.

At best, they can dance, entertain, push a tray of food and deliver medication to a specified destination.

But they are defunct of tactile touch. It cannot brush hair, dry tears or offer a hand to hold with comforting words in the darkest of days.

It cannot compute the nuances of human emotion and speech. Social care was ranked as one of the least automatable jobs of all in only 2016 as a result, but others just flatly find care robotsto camp in the category of undignified.

Only 26 per centof respondents to a survey said they would feel comfortable being hoisted and attended to by a robot when in care or a hospital, and many understandably have concerns around camera-fitted and potentially hackable devices in the rise of spy campornography.

However, the coronavirus pandemic may have considerably shaken the narrative and has, by twist of fate in the governments favour, propelled the case for AI in British healthcare forward.

Technology has undoubtedly played a vital role in this unparalleled era of segregation; FaceTiming loved ones, YouTube yoga classes, Skype work conferences and live-streamed concerts and theatre shows have kept Britons indoors while still relatively connected and entertained as before.

Yet even further afield, AI has become pivotal in delaying the spread of Covid-19.

In one hospital at the heart of the outbreak in Wuhan, China, robots outnumbered doctors as they patrolled the corridors, disinfected areas and monitored patients temperature and overall wellbeing.

The CEO behind this remarkable technology argued thatrobots do not carry disease, and robots can be easily disinfected.

Other countries, such as Singapore, Iran and Israel, have resorted to far more draconian invasions on civil liberties through the use of tech.

Yetspymobile tracking apps and ramped-up surveillance haveproved paramount in curbing the death toll and a similarly designed app by the NHS may be coming to Britain in the next few weeks.

Even so, care robots overseas appear quite revolutionary.

Consider Pepper, a humanoid bot, that is able to entertain residents with knitting and exercise classes in care homes and help the staff with mundane tasks.

The therapeutic cuddly seal, Paro, has been proven to soothe Alzheimers sufferers.

Kirobo by Toyota similarly comforts childless adults; RoBear can physically lift patients from wheelchairs and Leka can break through barriers to communicate with autistic children.

Already the NHS uses digital aides which can outperform human hands and eyes in intricate surgeries and when detecting breast cancer and the early onset of Alzehimers disease.

Evidently, tech can be a force for good if executed right. The University of Oxford, McKinsey Global Institute, PwC and Shift Commission predict that although millions of jobs will fall victim to automation, social and healthcare will emerge largely unscathed.

The NHS will still be dominated by human staff, yet tech could vastly alleviate doctors attendance to paperwork by 5.7m hours, generating a saving of13 billion.

Similarly, social care could save 6bn according to surgeonLord Darzi.

However, the biggest battle for tech remains in public confidence; confidence that has waned in the government as it arbitrarily stifles migration while the frail become collateral damage.

Tech wont be able to slice through social inequalities for as long as overzealous benefit assessors penalise disabled and vulnerable people for making improvements in their lives.

This move towards automation risks exacerbating the wealth and class division system in Britain and appears little more than another hostile political ploy to warrant the governments anti-migrant agenda.

Olivia Bridge is a political correspondent for the Immigration Advice Service,an organisation of Britainand Irelandimmigration lawyers.

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The ethics of artificial intelligence and automation amid a global pandemic - Morning Star Online