Category Archives: Ai

Greater automation is seen as a route toward increased efficiency in postal and logistics services. To this end, DHL Supply Chain has launched a plug-and-play robotics platform in collaboration with Microsoft and AI specialists Blue Yonder.

The company said its new platform will reduce the time taken to integrate new automated processes into warehouse facilities. The global deployment of robots and robotic systems is integral to our strategy to support our employees and improve customer operations, said Markus Voss, global CIO and COO, DHL Supply Chain.

Automation and collaborative robotics help us make operational processes more flexible, ergonomic and more attractive to our employees by replacing monotonous, repetitive and particularly strenuous activities. The aim is not to replace employees over time, but to assign the more attractive and interesting tasks to our human workforce.

We have more than 2,000 operational sites across DHL Supply Chain, so we know how complex, time-consuming and costly it can be to integrate new robots into existing platforms and connect to our clients various warehouse management systems. This is exactly where the new platform is so effective. Our first implementation on the new platform with 6 River Systems at one of our Madrid sites is already showing a 60% reduction in integration times, but with subsequent deployments we foresee further improvements of up to 90%.

The robotics platform is powered by Blue Yonders Luminate Platform with machine-learning-driven task management capabilities that enable the greater levels of warehouse operational efficiency. DHL pointed out that the new software platform is just one part of its company-wide digitization strategy which includes the use of technologies such as robots, smart operations through wearable devices and data analytics.

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DHL partners with Microsoft and AI specialist to ease automation integration - Parcel and Postal Technology International

For years, artificial intelligence seemed on the cusp of becoming the next big thing in technology - but the reality never matched the hype. Now, the changes caused by the covid-19 pandemic may mean AI's moment is finally upon us.

Over the past couple of months, many technology executives have shared a refrain: Companies need to rejigger their operations for a remote-working world. That's why they have dramatically increased their spending on powerful cloud-computing technologies and migrated more of their work and communications online.

With fewer people in the office, these changes will certainly help companies run more nimbly and reliably. But the centralization of more corporate data in the cloud is also precisely what's needed for companies to develop the AI capabilities - from better predictive algorithms to increased robotic automation - we've been hearing about for so long. If business leaders invest aggressively in the right areas, it could be a pivotal moment for the future of innovation.

To understand all the fuss around artificial intelligence, some quick background might be useful: AI is based on computer science research that looks at how to imitate the workings of human intelligence. It uses powerful algorithms that digest large amounts of data to identify patterns. These can be used to anticipate, say, what consumers will buy next or offer other important insights. Machine learning - essentially, algorithms that can improve at recognizing patterns on their own, without being explicitly programmed to do so - is one subset of AI that can enable applications like providing real-time protection against fraudulent financial transactions.

Historically, AI hasn't fully lived up to its hype. We're still a ways off from being able to have natural, life-like conversations with a computer, or getting truly safe self-driving cars. Even when it comes to improving less advanced algorithms, researchers have struggled with limited datasets and a lack of scaleable computing power.

Still, Silicon Valley's AI-startup ecosystem has been vibrant. Crunchbase says there are 5,751 private-held AI companies in the U.S. and that the industry received $17.4 billion in new funding last year. International Data Corporation (IDC) recently forecast that global AI spending will rise to $96.3 billion in 2023 from $38.4 billion in 2019. A Gartner survey of chief information officers and IT leaders, conducted in February, found that enterprises are projecting to double their number of AI projects, with over 40% planning to deploy at least one by the end of 2020.

As the pandemic accelerates the need for AI, these estimates will most likely prove to be understated. Big Tech has already demonstrated how useful AI can be in fighting covid-19. For instance, Amazon.com partnered with researchers to identify vulnerable populations and act as an "early warning" system for future outbreaks. BlueDot, an Amazon Web Services startup customer, used machine learning to sift through massive amounts of online data and anticipate the spread of the virus in China.

Pandemic lockdowns have also affected consumer behavior in ways that will spur AI's growth and development. Take a look at the soaring e-commerce industry: As consumers buy more online to avoid the new risks of shopping in stores, they are giving sellers more data on preferences and shopping habits. Bank of America's internal card-spending data for e-commerce points to rising year-over-year revenue growth rates of 13% for January, 17% for February, 24% for March, 73% for April and 80% for May. The data these transactions generate is a goldmine for retailers and AI companies, allowing them to improve the algorithms that provide personalized recommendations and generate more sales.

The growth in online activity also makes a compelling case for the adoption of virtual customer-service agents. International Business Machines Corporation estimates that only about 20% of companies use such AI-powered technology today. But they predict that almost all enterprises will adopt it in the coming years. By allowing computers to handle the easier questions, human representatives can focus on the more difficult interactions, thereby improving customer service and satisfaction.

Another area of opportunity comes from the increase in remote working. As companies struggle with the challenge of bringing employees back to the office, they may be more receptive to AI-based process automation software, which can handle mundane tasks like data entry. Its ability to read invoices and update databases without human intervention can reduce the need for some types of office work while also improving its accuracy. UiPath, Automation Anywhere and Blue Prism are the three leading vendors in this space, according to Goldman Sachs, accounting for about 36% of the roughly $850 million market last year. More imaginative AI projects are on the horizon. Graphics semiconductor-maker NVIDIA Corporation and luxury automaker BMW Group recently announced a deal where AI-powered logistics robots will be used to manufacture customized vehicles. In mid-May, Facebook said it was working on an AI lifestyle assistant that can recommend clothes or pick out furniture based on your personal taste and the configuration of your room.

As with the mass adoption of any new technology, there will be winners and losers. Among the winners, cloud-computing vendors will thrive as they capture more and more data. According to IDC, Amazon Web Services was number one in infrastructure cloud-computing services, with a 47% market share last year, followed by Microsoft at 13%.

But NVIDIA may be at an even better intersection of cloud and AI tech right now: Its graphic chip technology, once used primarily for video games, has morphed into the preeminent platform for AI applications. NVIDIA also makes the most powerful graphic processing units, so it dominates the AI-chip market used by cloud-computing companies. And it recently launched new data center chips that use its next-generation "Ampere" architecture, providing developers with a step-function increase in machine-learning capabilities.

On the other hand, the legacy vendors that provide computing equipment and software for in-office environments are most at risk of losing out in this technological shift. This category includes server sellers like Hewlett Packard Enterprise Company and router-maker Cisco Systems, Inc.

We must not ignore the more insidious consequences of an AI renaissance, either. There are a lot of ethical hurdles and complications ahead involving job loss, privacy and bias. Any increased automation may lead to job reductions, as software and robots replace tasks performed by humans. As more data becomes centrally stored on the cloud, the risk of larger data breaches will increase. Top-notch security has to become another key area of focus for technology and business executives. They also need to be vigilant in preventing algorithms from discriminating against minority groups, starting with monitoring their current technology and compiling more accurate datasets.

But the upside of greater computing power, better business insights and cost efficiencies from AI is too big to ignore. So long as companies proceed responsibly, years from now, the advances in AI catalyzed by the coronavirus crisis may be one of the silver linings we remember from 2020.

- - -

This column does not necessarily reflect the opinion of the editorial board or Bloomberg LP and its owners. Kim is a Bloomberg Opinion columnist covering technology.

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Coronavirus will finally give artificial intelligence its moment - San Antonio Express-News

AI-enabled advertising

In this incredibly fast paced and noisy world, its getting increasingly more difficult to get and hold peoples attention. Were on 24 hour news overload, social media platforms trying to keep us on their platforms, and the barrage of email and messages that seem never ending. With all this noise, Its a relative wonder that companies can get their messages across and effectively communicate their offerings to prospective customers.

Leaping into the foray are AI solutions that are aiming to help identify potential customers, hyperpersonalize and tailor messages to their specific needs, improve ad placement, and identify the most effective means to communicate the message. In fact, AI is particularly good at all those things: identifying potential customers through clustering and pattern matching, tailoring messages through AI-enabled hyperpersonalization, and finding the best times and means to communicate through pattern identification.

Optimizing Ad Spend with AI

Artificial intelligence is being used to help determine how much advertisers should spend on their various campaigns. By keeping constant watch over different channels and examining how various campaigns are performing, AI systems are able to figure out the optimal cost, timing, and means for advertising promotion. The AI-enabled systems are able to account for a wide variety of factors that humans are not able to effectively keep track of. As a result, these AI-enabled systems are able to come up with complicated, highly optimized ad spend plans that can factor both the spending and target of your audience. The systems can identify the right channel, message, and even adjust ad spend plans.

These AI systems are also able to track performance not only of an advertisers campaigns but also those of the advertisers competitors. By keeping AI-enabled situational awareness, as markets shift quickly and patterns and behaviors change, the AI-based systems can adjust quickly, shifting ad spent dollars to alternate channels and changing advertising messages. In these days when the markets can change in a split second, having such responsive ad systems can be a major competitive advantage. Internet and advertising platforms have tons of data that can be used to optimize advertisements. As humans, we are unable to utilize the plethora of information we have access to, but AI systems are able to take full advantage of this information.

Knowing where to effectively place advertisements can be difficult. AI is helping with that too. Various AI-enabled systems can scan through content on the internet to determine what ads are best suited for particular audiences or channels. It can also determine the relevancy of ads for videos. Through the use of image recognition, these systems help place ads that are correlated with images that can be found on the page of an article or website.

Artificial Intelligence For Ad Creation

Increasingly, AI is being used to create ad copy as well. Many social media and online advertising platforms are starting to roll out tools that help advertisers create a more optimal message. Most notably Facebook and Instagram have AI-enabled tools that not only help you to create your ads but also to create variations for them. This can save businesses a lot of time on creating advertisements or can help guide content that will have a better response rate.

In addition to the major online ad platforms, numerous startups and emerging companies are focusing their efforts on tools to assist advertising creators to create more optimal, well-targeted ads. Some solutions are focused on optimizing ad content, while others are focused on optimizing ad spend and channel targeting. Some are even working on completely automated advertising systems that can identify audiences, target markets, create content, bid on ads, and place them in an autonomous manner. That approach is definitely not for the faint of heart, but might be a glimpse into the future of AI-enabled advertising.

AI is helping with optimization on the backend as well. Companies that offer advertisements such as the major social media platforms, use artificial intelligence to monitor ad spending, sales, tracking, and customer behavior. These companies are also using AI to increasingly monitor the ads that are being placed on their network. Social media firms are being challenged by political campaigns, foreign bots, and other advertising that challenges ethical and responsibility limits so effective, continual monitoring is becoming more important than ever.

Ordinarily, it would be up to humans to moderate that content and advertising, but the sheer quantity and speed of advertising makes human moderation not feasible. AI systems are now being employed to automatically moderate and monitor advertising content. Artificial intelligence has been helping to greatly reduce the number of fraudulent ads or ads that violate various platform or community rules. 2018 saw a doubling in the number of false ads compared to the year before making monitoring tools even more important. The use of AI-enabled ad moderation and monitoring will no doubt be a major news maker in the upcoming 2020 elections and beyond.

Despite the fact that we might not see it, artificial intelligence is changing the world of advertising. It is helping change how companies deliver ads, improve ad relevance, and monitor ad quality. While not everyone might appreciate advertisements appearing in their content, the truth is that the right ad placed at the right time in the right channel focused on the right audience with the right message with the right offer is always appreciated. With more relevant and effective ads, not only will advertisers get better results for their ad spend, but customers should get less frustrated with advertising.

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AI Makes A Splash In Advertising - Forbes

DUBLIN--(BUSINESS WIRE)--The "AI in Fintech Market - Growth, Trends, Forecasts (2020-2025)" report has been added to ResearchAndMarkets.com's offering.

The global AI in Fintech market was estimated at USD 6.67 billion in 2019 and is expected to reach USD 22.6 billion by 2025. The market is also expected to witness a CAGR of 23.37% over the forecast period (2020-2025).

Artificial Intelligence improves results by applying methods derived from the aspects of human intelligence but beyond human scale. The computational arms race since the past few years has revolutionized the fintech companies. Further, data and the near-endless amounts of information are transforming AI to unprecedented levels where smart contracts will merely continue the market trend.

Key Highlights

Major Market Trends

Quantitative and Asset Management to Witness Significant Growth

North America Accounts for the Significant Market Share

Competitive Landscape

AI in Fintech market is moving towards fragmented owing to the presence of many global players in the market. Further various acquisitions and collaboration of large companies are expected to take place shortly, which focuses on innovation. Some of the major players in the market are IBM Corporation, Intel Corporation, Microsoft Corporation, among others.

Some recent developments in the market are:

Key Topics Covered

1 INTRODUCTION

1.1 Study Deliverables

1.2 Scope of the Study

1.3 Study Assumptions

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET DYNAMICS

4.1 Market Overview

4.2 Industry Attractiveness - Porter's Five Force Analysis

4.2.1 Bargaining Power of Suppliers

4.2.2 Bargaining Power of Buyers/Consumers

4.2.3 Threat of New Entrants

4.2.4 Threat of Substitute Products

4.2.5 Intensity of Competitive Rivalry

4.3 Emerging Use-cases for AI in Financial Technology

4.4 Technology Snapshot

4.5 Introduction to Market Dynamics

4.6 Market Drivers

4.6.1 Increasing Demand for Process Automation Among Financial Organizations

4.6.2 Increasing Availability of Data Sources

4.7 Market Restraints

4.7.1 Need for Skilled Workforce

4.8 Assessment of Impact of COVID-19 on the Industry

5 MARKET SEGMENTATION

5.1 Offering

5.1.1 Solutions

5.1.2 Services

5.2 Deployment

5.2.1 Cloud

5.2.2 On-premise

5.3 Application

5.3.1 Chatbots

5.3.2 Credit Scoring

5.3.3 Quantitative and Asset Management

5.3.4 Fraud Detection

5.3.5 Other Applications

5.4 Geography

5.4.1 North America

5.4.2 Europe

5.4.3 Asia-Pacific

5.4.4 Rest of the World

6 COMPETITIVE LANDSCAPE

6.1 Company Profiles

6.1.1 IBM Corporation

6.1.2 Intel Corporation

6.1.3 ComplyAdvantage.com

6.1.4 Narrative Science

6.1.5 Amazon Web Services Inc.

6.1.6 IPsoft Inc.

6.1.7 Next IT Corporation

6.1.8 Microsoft Corporation

6.1.9 Onfido

6.1.10 Ripple Labs Inc.

6.1.11 Active.ai

6.1.12 TIBCO Software (Alpine Data Labs)

6.1.13 Trifacta Software Inc.

6.1.14 Data Minr Inc.

6.1.15 Zeitgold GmbH

7 INVESTMENT ANALYSIS

8 MARKET OPPORTUNITIES AND FUTURE TRENDS

For more information about this report visit https://www.researchandmarkets.com/r/y1fj00

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Artificial Intelligence in Fintech - Global Market Growth, Trends and Forecasts to 2025 - Assessment of the Impact of COVID-19 on the Industry -...

A South Korean startup is holding a competition to fill one of the worlds first galleries for machine learning-generated art in a bid to foster a nascent artificial intelligence creativity scene in the country.

The company, Pulse9, which makes AI-powered graphics tools, is soliciting art pieces that make use of machine learning tech in some waywhether to produce an image out of whole cloth or restyle or supplement an artists workthrough the end of September.

The project is a notable addition to a burgeoning global community of technologists, new media artists and other creatives who are exploring the bounds of machine creativity through art, spurred by recent research advances that have made AI-generated content more realistic and elaborate than ever.

The medium had perhaps its biggest mainstream breakthrough in 2018, when Christies Auction House sold its first piece of AI-generated art for nearly half a million dollarsa classical style painting of a fictional character named Edmond de Belamy. That was also the moment that inspired the team at Pulse 9, which had just launched an AI tool to help draw and color a Korean style of digital comic called webtoons earlier that year.

We asked ourselves, Could we also sell paintings? and we started looking for art platform companies to work with, Pulse 9 spokesperson Yeongeun Park said.

The company teamed with an art platform called Art Together on a series of crowdfunded AI pieces that proved to be more popular than they had expectedone hit its goal a full week ahead of scheduleand the team began considering parlaying it into a bigger project.

With great attention from the public and the good funding results, we gained confidence in pioneering the Korean AI art market, Park said. So, we eventually decided to open our own AI art gallery.

The company acknowledges that questions of authorship and originality still hang over the concept of AI art but stresses that the gallery is about collaboration between humans and technology rather than AI simply replacing artists. Even pieces generated entirely by machines require a host of human touches, whether its curating a collection of visuals for training or adjusting training regimens to achieve a desired results.

The theme of this competition is Can AI art enhance human artistic creativity?' Park said. We hope that this competition will also be an opportunity to discover creative, competent and new artists who would like to engage AI tools as a new artistic medium in their artwork.

The goal is to establish AIA Gallery as a well-recognized institution in the art world and educate people on the potential for AI-powered creativity. The organizers hope the process will also inspire other efforts and create an AI creativity hub in the country.

Groups or communities of AI artists have formed and are gradually growing, especially overseas, Park said. In the case of Korea, the AI Art market has not been well-recognized yet, but weve been continuing to play our role with our own initiative.

The AIA Gallery recently partnered with one of the leading startups in the new space, Playform, which is led by Rutgers University Art and AI Lab director Ahmed Elgammal (after learning about the company from an Adweek article).

Progress in generative AI creativity isnt confined to the art world, either. Agencies have started to experiment with various AI-generated graphics in campaigns, and brands have filed a slew of patent applications around the central technology powering the revolutiona neural net structure called a generative adversarial network.

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This Startup Is Trying to Foster an AI Art Scene in Korea - Adweek

Following the rise of digitalization, heightened numbers of world-renowned organizations, such as Amazon, Tesla, Uber, and many more, have embraced the technical abilities of automation. That said,however, its believed that small businesses are leading the charge in relation to their use of labor-saving technology. So, lets take a look at three examples of smaller companies that have successfully adopted AI and automation.

In recent years, the implementation of both artificial intelligence and automation has become central to the long-term success of a wide array of diverse sectors. Crucially, this has resulted in modern-day technology altering the outlook of the trading industry. Fundamentally, while there are many reasons why small businesses have sought to integrate automated software, many of them do so in an effort to reduce costs and improve the speed at which their service operates.

Source: Unsplash

This approach of speed, combined with convenience, is evident within Bitcoin Revolution. The service itself is a fully automatic trading robot that is designed to trade cryptocurrencies using the data provided by sophisticated algorithms. As a result, this means that the platforms members only need to dedicate a few minutes of their time to achieve profitable results. Moreover, the Bitcoin Revolution app in the UK functions through its use of advanced programming technologies. In turn, this enables it to independently conduct market research, as well as placing orders.

Aside from the technology that surrounds the trading industry, the food and drink sectors have also taken steps to automate their typically consumer-faced service. A prime example of this comes in the form of Chowbotics automated made-to-order meal service. Since being founded in 2014, the Californian company has made significant strides regarding revolutionizing the catering industry. Primarily, theyve been able to provide an automated service through the development of the worlds first fresh food robot.

Much like Bitcoin Revolution, Chowbotics robot, named Sally, guarantees prospective consumers a time-efficient service. The development uses groundbreaking robotics to assemble meals, according to the companys official website. Crucially, the six-year-old businesss use of automation has provided the industry with a forward-thinking route to making, selling, and dealing with fresh food.

By definition, there are subtle differences between artificial intelligence and automation. Generally speaking, AI refers to programming machines to mimic human behavior, while automation, on the other hand, relates to the act of being able to do things without human intervention. Over the last few years, both have become instrumental to established fashion retailers, such as H&M and Zara. However, AI, in particular, is proving undeniably beneficial to small fashion businesses.

The Yes, which was founded by Julia Bornstein in 2018, plans on transforming the nature of the fashion industry through completely replacing personal shoppers with AI software. Through a selection of questions, the technology-centered retailer aims to independently tailor digital shopping experiences to each respective shopper. In turn, this will provide a platform allowing The Yes to compete with well-known retailers over the coming years as a result of their AI-focused service.

Ultimately, few can argue against the fact that both AI and automation are proving to be central to the growth of a whole host of small businesses. Crucially, the above services place user convenience at the forefront of their long-term plans. Because of this, it allows them to expand their audience base, and, perhaps more importantly, impact upon the outlook of their respective industries.

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3 examples that show how automation and AI are not just for big businesses - AZ Big Media

As businesses across the United States have gradually begun to reopen, a growing number of companies are investing in camera technology powered by artificial intelligence to help enforce social distancing measures when people may be standing too closely together.

"[If] I want to manage the distance between consumers standing in a line, a manager can't be in all places at once," Leslie Hand, vice president of retail insights for the International Data Corporation, told Business Insider. "Having a digital helper that's advising you when folks are perhaps in need of some advice is useful."

Businesses throughout the country have started operating again under restrictions, such as enforcing social distancing measures, requiring customers to wear masks, and reducing capacity. New York City, which was the epicenter of the virus' outbreak in the US, is set to enter Phase II of its reopening plan on Monday.

The White House's employer guidelines for all phases of reopening include developing policies informed by best practices, particularly social distancing. And some experts believe smart cameras can help retailers and other companies detect whether such protocols are being followed.

"There's some technology coming out on the horizon that will be able to be incorporated into the nuts and bolts that you already have in your store," Barrie Scardina, head of Americas retail for commercial real estate services firm Cushman & Wakefield, said to Business Insider.

Some companies have already begun experimenting with such technologies. Amazon said on June 16 that it developed a camera system that's being implemented in some warehouses to detect whether workers are following social distancing guidelines. The company's so-called "Distance Assistant" consists of a camera, a 50-inch monitor, and a local computing device, which uses depth sensors to calculate distances between employees.

When a person walks by the camera, the monitor would show whether that person is standing six feet apart from nearby colleagues by overlaying a green or red circle around the person. Green would indicate the person is properly socially distanced, while red would suggest the people on camera may be too close together. Amazon is open-sourcing the technology so that other companies can implement it as well.

Motorola Solutions also announced new analytics technology in May that enables its Avigilon security cameras to detect whether people are social distancing and wearing masks. The system uses AI to collect footage and statistical patterns that can be used to provide notifications to organizations about when guidelines around wearing face masks or honoring social distancing measures are being breached.

Pepper Construction, a Chicago-based construction company, has also begun using software from a company called SmartVid.io to keep an eye on where workers may be grouping, as Reuters reported in late April.

Scardina offered some examples illustrating how smart cameras can help retailers enforce social distancing. Workers can use such technologies to see where customers are clustering so that they can make decisions about how to arrange furniture and fixtures within the store. If a table needs to be moved further away from another display because customers don't have space to stand six feet apart, AI camera technology can help retailers spot this.

As far as how widespread that technology will become in stores, Scardina says it will depend on factors such as a retailer's budget and the size of the shop.

While more companies may be investing in either developing or implementing new camera technologies, there will inevitably be challenges that arise when putting them into practice, says Pieter J. den Hamer, senior director of artificial intelligence for Gartner Research.

Not only could implementing such tech raise privacy concerns, but there are also practical limitations. A camera may not know if two people standing close together belong to the same household, for example.

All 50 states have reopened at some capacity, putting an end to stay-at-home orders that had been in effect since March to curb the coronavirus' spread, and some states are now seeing a spike in cases. The New York Times recently reported that at least 14 states have experienced positive cases that have outpaced the average number of administered tests.

The coronavirus has killed at least 117,000 people in the US and infected more than 2.1 million as of June 18, according to the Times, and experts predict there will be a second wave. But President Trump has said the country won't be closing again.

"It's a very, very complex debate full of dilemmas," den Hamer said. "Should we prioritize opening up the economy, or should we prioritize the protection of our privacy?"

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AI cameras may be used to detect social distancing as US is reopening - Business Insider - Business Insider

As momentum builds to address race-based injustices in America, the National Institute of Standards and Technology (NIST) last week announced a workshop focused on understanding and addressing bias in Artificial Intelligence (AI) systems. The event will bring together members of the public and private sector to seek consensus on what 'bias' means in the context of AI and how to measure it.

NIST believes that finding common ground on these questions 'will lay important groundwork for upcoming efforts in NIST's AI work more broadly, including the development of standards and recommendations for achieving trustworthy AI.' The workshop will be held virtually on August 18, 2020, and organizations looking to take concrete actions to reduce biases based on race, ethnicity, gender, sexuality, and other protected characteristics in their products should consider participating.

The benefits and utility of AI are now well establishedimplementing systems and processes that leverage AI and machine learning can enhance efficiency, increase output, deliver insights, and much more. In addition, using AI to make or facilitate decisions provides an opportunity for organizations to eliminate or reduce explicit bias that may arise from humans making certain decisions, but algorithms will still reflect prejudices in the data they see.

Uncritically implementing facial recognition AI, for example, can result in high error rates when trying to identify racial minorities if almost all of the faces in the data that trained the AI were white. In other contexts, AI can unwittingly reproduce prejudices against women, members of the LGBTQ community, or other marginalized groups. And because AI systems are not always transparentsome systems do not explain the reasons for their decisionsit can be difficult to detect any inherent biases that may arise from incomplete or inaccurate training data.

The potential for bias in AI systems has become a significant concern in recent years, recognized by privacy advocates, academics, and many private companies. Some states and the federal government have also addressed the issue in a variety of ways:

Given the plethora of government and private-sector efforts to address bias in AI, what might NIST's role be? As its name suggests, NIST specializes in creating standards, and the announcement of the workshop this August states that it will focus on (1) how to define 'bias' in the context of AI and (2) how to measure such bias.

Making progress on the definition of bias in AI would be a significant achievement since there is so little consensus regarding the meaning of many terms in this field. For example, New York City's AI report uses the term 'disproportionate impact' without defining it; Washington State's legislation similarly uses the terms 'bias' and 'disparate impact' without specifying their meaning; and Illinois's Artificial Intelligence Video Interview Act does not even define 'artificial intelligence.'

NIST views this workshop as one step in its ongoing work on AI issues. As noted above, one of the long-term goals mentioned in the announcement is to lay important groundwork for upcoming efforts in NIST's AI work more broadly, including the 'development of standards and recommendations for achieving trustworthy AI.'

This language suggests that NIST may hope eventually to produce a framework for organizations to use to mitigate bias in their AI systems, similar to the highly influential NIST Cybersecurity Framework. Ideally, NIST could produce another framework for developing 'trustworthy AI' that is flexible enough to be implemented by both startups and large multinational corporations; imaginative enough to remain relevant in the rapidly changing world of AI for years to come; and comprehensive enough that state, local, and federal governments do not feel the need to pass significant additional top-down laws or regulations in this space.

The NIST Cybersecurity Framework was developed with significant feedback from industry, and many organizations now voluntarily comply with it due to its strong reputation and flexibility.

Companies that want to provide input during NIST's process of developing standards and recommendations related to bias in AIwhatever form those may eventually takeshould consider participating in the Bias in AI Workshop this August 18. And attending the workshop can help organizations ensure that they maintain their commitment to fighting prejudice even after injustices based on race, gender, or other statuses fade from the front page.

[View source.]

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NIST Announces Workshop Focused on "Bias in AI" - JD Supra

Introduction

Artificial intelligence (AI) is a potentially transformational technology that will impact how people work and socialize and how economies grow. AI will also have wide-ranging international implications, from national security to international trade. In this submission, we address the significance of international cooperation as a vehicle for realizing the ambitious goals in the key areas of AI innovation and regulation set out in the European Commissions white paper on AI. We focus particularly on the EU relationship with the U.S., which as both a major EU trading partner and a world leader in AI, is a logical partner for such cooperation.

The white paper talks to the importance of international cooperation. Specifically, the white paper observes that the EU will continue to cooperate with like-minded countries, but also with global players, on AI, based on an approach based on EU rules and values. The white paper also goes on to note that the Commission is convinced that international cooperation on AI matters must be based on an approach that promotes the respect of fundamental rights, including human dignity, pluralism, inclusion, non-discrimination and protection of privacy and personal data and it will strive to export its values across the world. The U.S. and the EU, as the worlds leading economies with strong ties grounded in common values, provide a strong basis for AI governance that can work for the EU and the U.S. and provide a model globally.

This submission is divided into two parts. The first outlines why transatlantic cooperation on AI is important. The second identifies three broad areas for transatlantic cooperation on AI: innovation, regulation, and standards, including with respect to data.

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The importance and opportunities of transatlantic cooperation on AI - Brookings Institution

Algorithmic bias, lack of artificial intelligence (AI) explainability and failure to seek meaningful consent for personal data collection and sharing are among the biggest barriers facing AI, according to analysis from the Centre for Data Ethics and Innovation(CDEI).

The CDEIs AI Barometer analysis was based on workshops and scoring exercises involving 100 experts. The study assessed opportunities, risks and governance challenges associated with AI and data use across five key UK sectors.

Speaking at the launch of the report, Michael Birtwistle, AI Barometerlead at the CDEI, said: AI and data use have some very promising opportunities, but not all are equal. Some will be harder to achieve but have high benefits, such as realising decarbonisation and understanding public health risk or automatic decision support to reduce bias.

Birtwistle said the CDEI analysis showed that what these application areas have in common is complex data flows about people that affect them directly. We are unlikely to achieve the biggest benefits without overcoming the barriers, he added.

Roger Taylor, chair of the CDEI, said: AI and data-driven technology has the potential to address the biggest societal challenges of our time, from climate change to caring for an ageing society. However, the responsible adoption of technology is stymied by several barriers, among them low data quality and governance challenges, which undermine public trust in the institutions that they depend on.

As we have seen in the response to Covid-19, confidence that government, public bodies and private companies can be trusted to use data for our benefit is essential if we are to maximise the benefits of these technologies. Now is the time for these barriers to be addressed, with a coordinated national response, so that we can pave the way for responsible innovation.

The report found that the use of biased algorithmic tools due to biased training data, for example entrenches systematic discrimination against certain groups, such as reoffending risk scoring in the criminal justice system.

During a virtual panel discussion at the launch of the AI Barometer, Areeq Chowdhury, founder of WebRoots Democracy, discussed how technology inadvertently amplifies systemic discrimination. For instance, while there is a huge public debate about the accuracy rate of facial recognition systems to identify people from black and Asian minorities, the ongoing racial tension in the US has shown that the problem is wider than the actual technology.

According to Chowdhury, such systemic discrimination builds up from a collection of policies over a period of time.

The experts who took part in the CDEI analysis raised concerns about the lack of clarity over where oversight responsibility lies. Despite AI and data being commonly used within and across sectors, it is often unclear who has formal ownership of regulating its effects, said the CDEI in the report.

Cathryn Ross, head of the Regulatory Horizon Council, who also took part in the panel discussion, said: A biting constraint on the take-up of technology is public trust and legitimacy. Regulations can help to build public trust to enable tech innovation.

Mirroring her remarks, fellow panellist Annemarie Naylor, director of policy and strategy at Future Care Capital, said: Transparency has never been so important.

The AI Barometer also reported that the experts the CDEI spoke to were concerned about low data quality, availability and infrastructure: It said: The use of poor quality or unrepresentative data in the training of algorithms can lead to faulty or biased systems (eg diagnostic algorithms that are ineffective in identifying diseases among minority groups).

Equally, the concentration of market power over data, the unwillingness or inability to share data (eg due to non-interoperable systems), and the difficulty of transitioning data from legacy and non-digital systems to modern applications can all stymie innovation.

The CDEI noted that there is often disagreement among the public about how and where AI and data-driven technology should be deployed. Innovations can pose trade-offs such as between security and privacy, and between safety and free speech, which take time to work through.

However, the lockdown has shown that people are prepared to make radical changes very quickly if there are societal benefits. This has implications for data privacy policies.

The challenge for regulators is that existing data regulations are often sector-specific. In Rosss experience, technological innovation with AI cuts across different industry sectors. She said a fundamentally different approach that coordinated regulations was needed.

Discussing what the coronavirus has taught policy-makers and regulators about peoples attitudes to data, Ross said: Society is prepared to take more risk for a bigger benefit, such as saving lives or reducing lockdown measures.

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AI bias and privacy issues require more than clever tech - ComputerWeekly.com