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Solving the Problem of Bias in Artificial Intelligence

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Emerging Tech Development & Consulting: Artificial Intelligence. Advanced Analytics. Machine Learning. Big Data. Cloud

Back in 2018, the American Civil Liberties Union found out that Amazon’s Rekognition, face surveillance technology used by police and courting departments across the US, shows AI bias. During the test, the software incorrectly matched 28 members of Congress with the mugshots of people who have been arrested for committing a crime, and 40% of the false matches were people of color.

Following mass protests wherein Amazon’s employees refused to contribute to AI tools that reproduce facial recognition bias, the tech giant has announced a one-year moratorium on law enforcement agencies using the platform.

The incident has stirred new debate about bias in artificial intelligence algorithms and made companies search for new solutions to the AI bias paradox.

In this article, we’ll dot the i’s, zooming in on the concept, root causes, types, and ethical implications of AI bias, as well as list practical debiasing techniques shared by our AI consultants that worth including in your AI strategy.

But let’s start with the basics.

What is AI bias, and why does it occur?

A simple definition of AI bias could sound like that: an anomaly in the output of AI algorithms.

Bias in artificial intelligence can take many forms — from racial bias and gender prejudice to recruiting inequity and age discrimination. The underlying reason for AI bias is human misbeliefs, either conscious or unconscious, lurking into AI algorithms at different stages of their development. So, an AI solution adopts and scales the prejudiced assumptions of the human brain, both individual and societal.

One potential source of this issue is prejudiced hypotheses made when designing AI models or algorithmic bias. Psychologists claim there’re about 180 cognitive biases, some of which may find their way into hypotheses and influence how AI algorithms are designed.

An example of algorithmic AI bias could be assuming that a model would automatically be less biased when not given access to protected classes, say, race. In reality, removing the protected classes from the analysis doesn’t erase racial bias from AI algorithms. The model could still produce prejudiced results relying on related non-protected factors, for example, geographic data — the phenomenon is known as proxy discrimination.

Another common reason for replicating AI bias is the low quality of data on which AI models are trained. The training data may incorporate human decisions or echo societal or historical inequities.

For instance, if an employer uses an AI-based recruiting tool trained on historical employee data in a predominantly male industry, chances are AI would replicate gender bias.

The same applies to natural language processing algorithms. When learning on real-world data, like news reports or social media posts, AI is likely to show language bias and reinforce the existing prejudices. This is what happened with Google Translate, which tends to be biased against women when translating from languages with gender-neutral pronouns. The AI engine powering the app is more likely to generate such translations as “he invests” and “she takes care of the children” than vice versa.

AI bias can stem from the way training data is collected and processed as well. The mistakes data scientists may fall prey to range from excluding valuable entries to inconsistent labeling to under- and oversampling. Undersampling, for example, can cause skews in class distribution and make AI models ignore minority classes completely.

Oversampling, in turn, may lead to the over-representation of certain groups or factors in the training datasets. For instance, crimes committed in locations frequented by the police are more likely to be recorded in the training dataset simply because that is where the police patrol. Consequently, the algorithms trained on such data are likely to reflect this disproportion.

A no less important source of AI bias is the feedback of real-world users interacting with AI models. People may reinforce bias baked in already deployed AI models, often without realizing it. For example, a credit card company may use an AI algorithm that mildly reflects social bias to advertise their products, targeting less-educated people with offers featuring higher interest rates. These people may find themselves clicking on these types of ads without knowing that other social groups are shown better offers, thus, scaling the existing bias.

What are the four common types of bias in artificial intelligence?

The most common classification of bias in artificial intelligence takes the source of prejudice as the base criterion, putting AI biases into three categories — algorithmic, data, and human. Still, AI researchers and practitioners urge to look out for the latter as human bias underlies and outweighs the other two. Here’re the most common types of AI bias that creep into the algorithms.

1. Reporting bias

This type of AI bias arises when the frequency of events in the training dataset doesn’t accurately reflect reality. Take an example of a customer fraud detection tool that underperformed in a remote geographic region, marking all customers living in the area with a falsely high fraud score.

It turned out that the training dataset the tool was relying on claimed every historical investigation in the region as a fraud case. The reason was that because of the region’s remoteness, fraud case investigators wanted to make sure every new claim is indeed fraudulent before they travel to the area. So, the frequency of fraudulent events in the training dataset was way higher than it should have been in reality.

2. Selection bias

This type of AI bias occurs if training data is either unrepresentative or is selected without proper randomization. An example of the selection bias is well illustrated by the research conducted by Joy Buolamwini, Timnit Gebru, and Deborah Raji, where they looked at three commercial image recognition products. The tools were to classify 1,270 images of parliament members from European and African countries. The study found that all three tools performed better on male than female faces and showed more substantial bias against darker-skin females, failing on over one in three women of color — all due to the lack of diversity in training data.

3. Group attribution bias

Group attribution bias takes place when data teams extrapolate what is true of individuals to entire groups the individual is or is not part of. This type of AI bias can be found in admission and recruiting tools that may favor the candidates who graduated from certain schools and show prejudice against those who didn’t.

4. Implicit bias

This type of AI bias occurs when AI assumptions are made based on personal experience that doesn’t necessarily apply more generally. For instance, if data scientists have picked up on cultural cues about women being housekeepers, they might struggle to connect women to influential roles in business despite their conscious belief in gender equality — an example echoing the story of Google Images’ gender bias.

Why should businesses engage in solving the AI bias problem?

With the growing use of AI in sensitive areas, including finances, criminal justice, and healthcare, we should strive to develop algorithms that are fair to everyone. Businesses, too, have to work on reducing bias in AI systems.

The most apparent reason to hone a corporate debiasing strategy is that a mere idea of an AI algorithm being prejudiced can turn customers away from a product or service a company offers and jeopardize the company’s reputation. On the flip side, relying on an AI solution that performs accurately for the whole spectrum of genders, races, ages, and cultural backgrounds is much more likely to deliver superior value and appeal to a broader and more diverse pool of potential customers.

Another point that could motivate businesses to dedicate themselves to overcoming AI bias is the growing debate about AI regulations. Policymakers in the EU, for example, are starting to develop solutions that could help keep bias in artificial intelligence under control. Certifying AI vendors could be one of such solutions. And along with regulating the inclusiveness of AI algorithms, obtaining an AI certification could help tech enterprises stand out in the saturated marketplaces.

How to reduce bias in machine learning algorithms

Solving the problem of bias in artificial intelligence requires collaboration between tech industry players, policymakers, and social scientists. And the tech industry has a long way to go before it could eliminate AI bias. Still, there are practical steps companies can take today to make sure the algorithms they develop foster equality and inclusion.

1. Examine the context. Some industries and use cases are more prone to AI bias and have a previous record of relying on biased systems. Being aware of where AI has struggled in the past can help companies improve fairness, building on the industry experience.

2. Design AI models with inclusion in mind. Before actually designing AI algorithms, it makes sense to engage with humanists and social scientists to ensure that the models you create don’t inherit bias present in human judgment. Also, set measurable goals for the AI models to perform equally well across planned use cases, for instance, for several different age groups.

3. Train your AI models on complete and representative data. That would require establishing procedures and guidelines on how to collect, sample, and preprocess training data. Along with establishing transparent data processes, you may involve internal or external teams to spot discriminatory correlations and potential sources of AI bias in the training datasets.

4. Perform targeted testing. While testing your models, examine AI’s performance across different subgroups to uncover problems that can be masked by aggregate metrics. Also, perform a set of stress tests to check how the model performs on complex cases. In addition, continuously retest your models as you gain more real-life data and get feedback from users.

5. Hone human decisions. AI can help reveal inaccuracies present in human decision-making. So, if AI models trained on recent human decisions or behavior show bias, be ready to consider how human-driven processes might be improved in the future.

6. Improve AI explainability. Additionally, keep in mind the adjacent issue of AI explainability: understanding how AI generates predictions and what features of the data it uses to make decisions. Understanding whether the factors supporting the decision reflect AI bias can help in identifying and mitigating prejudice.

The trends in tacking AI bias

Tech leaders across the globe are taking steps to reduce AI bias. And leveling out the demographics working on AI is one of their priorities. Intel, for example, is taking steps in improving diversity in the company’s technical positions. Recent data shows that women make up 24% of the company’s AI developers, ten percentage points higher than the industry average.

Google has also rolled out AI debiasing initiatives, including responsible AI practices featuring advice on making AI algorithms fairer. At the same time, AI4ALL, a nonprofit dedicated to increasing diversity and inclusion in AI education, research, and development, breeds new talent for the AI development sector.

Other industry efforts focus on encouraging assessment and audit to test algorithms’ fairness before AI systems go live and promote legal frameworks and tools that can help tackle AI bias.

If you want to develop an AI solution that is bias-free, contact the ITRex team, and we’ll connect you with our AI experts.

Also published on https://itrexgroup.com/blog/ai-bias-definition-types-examples-debiasing-strategies/.

by ITRex @itrex. Emerging Tech Development & Consulting: Artificial Intelligence. Advanced Analytics. Machine Learning. Big Data. CloudBring us your challenge!

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Artificial Intelligence

Machine Learning vs Deep Learning vs Artificial Intelligence| Know in-depth Difference

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ML vs Dl Vs AI | Know in-depth Difference – Analytics Vidhya





















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Additive Manufacturing; Perhaps, the Biggest Tectonic Shift Enabling to Industry 4.0

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3D printing or additive manufacturing is bringing monumental changes to a number of important industries. Companies have begun printing everything from fully functioning cars to Michelin-stared dinners. Medical research institutes have even experimented with printing functioning body parts.

Recently, additive manufacturing showcased its practical versatility. It is becoming a powerful tool in tackling some of the unprecedented challenges posed by the COVID-19. Helping to supply health workers with personal protective equipment and patients with ventilators.

Advanced manufacturing tools like 3D printing—also known as additive manufacturing—are essential for the types of performance-sensitive applications where this optimization is compelling.

3D printing is more cost-competitive at lower production volumes. Because you do not need to reach an economy of scale to offset setup costs. Therefore, it facilitates the mass customization that generative design makes possible. As the cost of 3D printing continues to decrease and the variety of materials increase, 3D printing is becoming practical for small and mid-volume parts for more and more applications.

The Electric Vehicle and the Automotive Industry holds a prominent position in manufacturing with the latest technologies and Innovations. Automotive electrical and electronic systems are becoming more complex. Making the task of designing today’s cars much more difficult. Infotainment, comfort and convenience features, and even safety- and mission-critical systems such as steering and throttle control are accomplished through electrically powered computers, actuators, and sensors.

Additive Manufacturing with the help of Generative design takes system definitions and requirements as input and generates architectural proposals for the logic, software, hardware, and networks of the E/E systems using rules-based automation. These rules capture the knowledge and experience of the veteran engineers to guide younger engineers throughout the design. Capturing this IP helps companies to develop both vehicle architectures and new generations of engineers as they learn and implement existing company knowledge.

It also offers the ability to consolidate parts, so a single complex geometry created by a generative algorithm and 3D printed can often replace assemblies of dozens of separate parts.

Aerospace

Additive Manufacturing

Always on the cutting edge, the aerospace industry was an early adopter of additive manufacturing. Aerospace companies must meet some of the manufacturing industry’s most stringent standards, and parts and components need to be made of the highest-performance materials. Using AM, engineers can design complex, high-strength parts while also reducing the weight of aerospace components by printing more efficient geometries and eliminating significant amounts of unnecessary material. This allows for lower fuel consumption, reduced CO2 emissions, and reduced costs (plus, better airfares).

Consumer Products

Additive manufacturing comes with a plethora of advantages for manufacturing on-demand supplements for consumer-based products. For marketing teams taking a product from concept to completion, often the biggest amount of time is spent on design. To be sure the product is just right, a great deal of time is spent on creating prototypes to prove concepts to stakeholders and ultimately deliver a consumer-pleasing product.

By embracing AM, marketing teams can develop iterations of their product much quicker, and then rapidly pivot to adjust the design as needed. As additive manufacturing continues to advance with build volume and speed, more consumer products may be produced through AM technologies for quick and efficient mass production demands.

Infrastructure

It is not far-fetched to think of living in a house or walking over a bridge that is completely made out of a 3D printer anymore. Soon, this may become reality. In fact, it already is in the Netherlands where the world’s first 3D-printed pedestrian bridge was unveiled in 2018. The structure was created by additive robotics layering molten steel and measures nearly 40 feet across.

Additive manufacturing in the construction market is expanding, ushering in a new era for the industry. According to a study by Transparency Market Research, 3D printing in the infrastructure secretary is predicted to expand at a CAGR of 33 per cent by 2027. Since printed materials can be precisely applied layer by layer when building infrastructure or constructing buildings, AM reduces material waste and allows construction to be as cost-efficient as possible.

Additionally, 3D printing technology allows for complex design structures. Since the materials are printed precisely – reducing the likelihood of infrastructure accidents from construction mistakes and poor design.

While 3D printing is not being employed in everyday bridge building, there is plenty of future potentials as the industry expands. And, with a recent report finding that over a third of all U.S. bridges need major repair work or should be replaced, there may be a rapid expansion of the industry in the near future.

Medical & Pharmaceutical

Additive manufacturing has revolutionized the medical industry, turning what was once science fiction into a new reality. The technology is delivering breakthroughs to doctors, patients, and research institutions. From durable prosthetics and true-to-life anatomical models to surgical grade components, the incredible plethora of objects that have already been successfully printed in the medical field gives a glimpse into the potential that this technology holds for healthcare in the near future.

For example, additive manufacturing allows for 3D printed dental appliances and custom-made devices, such as dentures, crowns, and even Invisalign, to be constructed from a variety of substrates and prints customized to each individual. Currently, the 3D printing market for digital dentistry is valued at $2.5 billion — and is expected to only keep growing. Additionally, additive manufacturing allows for devices such as hearing aids that can be mass-produced made for a better fit to ensure the highest level of comfort for the user.

One of the latest ongoing projects is the use of additive technology by researchers to print human embryonic stem cells. By doing so, these stem cells can then be used to create tissue for testing drugs or growing replacement organs, to print skin that could replace skin that’s been burned or damaged, and to print cancer cells to study and test out new drugs on them. Surgeon Anthony Atala, Director of the Wake Forest Institute for Regenerative Medicine, has even been working on printing organs, believing that printing an organ may soon replace transplanting an organ.

Hence, Additive Manufacturing is leading the industries and manufacturers into an era where the traditional ways of manufacturing would be disrupted, leaving faster, greater, and precise solutions for the manufacturing of future industries and products.

Mayank Vashisht | ELE Times | Technology Journalist

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Frequent run-ins with Indian government complicates tech giants’ plans

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Elevate your enterprise data technology and strategy at Transform 2021.


(Reuters) — Another spat between India’s government and U.S. big tech has exacerbated disillusion among firms which have spent billions to build hubs in their largest growth market, to the extent some are rethinking expansion plans, people close to the matter said.

The government on Saturday said Twitter had not indicated compliance with new rules aimed at making social media firms more accountable to legal requests, and therefore risked losing liability exemptions for content posted on its platform.

Twitter joins compatriots Amazon.com, Facebook, and Facebook-owned WhatsApp in long being at loggerheads with the administration of Prime Minister Narendra Modi over data privacy bills and policies some executives have called protectionist, but tension has escalated in recent weeks.

Police visited Twitter last month to notify it of a probe into the tagging of a political tweet as “manipulated media”, and in February interrogated an Amazon official about the potentially adverse social impact of a political drama. Meanwhile, WhatsApp is challenging the government in court over rules it said would force it to access encrypted data.

“The fear is there,” said a senior tech industry executive in India. “It weighs both strategically and operationally.”

There are no indications the increasing run-ins have led to the delay or cancellation of planned investment.

Still, three senior executives familiar with the thinking of major U.S. tech firms said perceptions of India being an alternative, more accessible growth market to China are changing, and that longstanding plans for India’s role in their operations are being reviewed.

“There always used to be these discussions to make India a hub, but that is being thought through now,” said one of the executives, who works at a U.S. tech firm. “This feeling is across the board.”

Four other executives and advisors also expressed concern about rising tension. All declined to be identified due to the sensitivity of the matter and because discussions were private.

Twitter, Amazon, Facebook, WhatsApp and India’s Ministry of Electronics and Information Technology did not respond to requests for comment.

Misinformation

The government has argued that its rules are needed to stem the spread of misinformation that can spark violence – such as in 2017 when kidnapping rumours shared on message apps including WhatsApp led to lynching. It also said the rules are necessary to hold large technology companies accountable for practices that hurt domestic businesses or compromise customer privacy.

India is a massive market for U.S. tech giants. It is the biggest market for both Facebook and WhatsApp by user numbers, showed data from Statista, and third for Twitter. Amazon has committed as much as $6.5 billion to invest in the country.

To attract small businesses through WhatsApp, Facebook last year invested $5.7 billion in Reliance Industries‘s media and telecommunications arm, Jio Platforms.

Alphabet’s Google also pumped $4.5 billion into Jio last year from a newly created $10 billion fund earmarked for investment in India over five to seven years.

Compliance

The government has tried to balance attracting high-tech investment with nationalist policies aimed at protecting local businesses and, critics say, advancing its political agenda.

A border confrontation with China prompted it to effectively ban Chinese social media apps, including TikTok and WeChat.

The government has also forced foreign firms to store data locally against fierce lobbying, and its promotion of a domestic payment card network prompted Mastercard to complain to the U.S. government about the use of nationalism.

In 2019, compliance issues with new regulations saw Amazon remove thousands of products from its e-commerce platform. The e-tailer is separately facing scrutiny by the Competition Commission of India for its retailing practices.

Twitter publicly refused to comply with some government demands to remove content, a stance which some industry executives said may have aggravated its current situation.

WhatsApp has gone to court rather than comply with a new law requiring social media firms to trace the origin of dangerous or criminal posts on their platforms. The message app operator said it cannot comply without breaking encryption, while observers said yielding could prompt similar demands in other countries.

At the same time, WhatsApp has faced regulatory delays that have limited its payment service to just 4% of its 500 million customers. Nevertheless, it is pressing ahead with hiring for a service it has called a “globally significant” opportunity.

Government officials have shown little patience for objections. IT minister Ravi Shankar Prasad said any robust democracy must have accountability mechanisms, such as the ability to identify the originator of messages.

“A private company sitting in America should refrain from lecturing us on democracy when you are denying your users the right to effective redressal forum,” Prasad said in an interview with the Hindu newspaper published on Sunday.

Still, continued antagonism could imperil Modi’s ambition of making India a go-to investment destination.

“It’s a question of what you would develop in a three-to-five-year horizon,” said another executive familiar with the thinking of U.S. firms. “Do you do that in India or do you do that in another country. That’s where the conversation is.”

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Artificial Intelligence

Global Economic Impact of AI: Facts and Figures

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Summarization of Research Insights from Emerj, Harvard Business Review, MIT Sloan, and Mckinsey

Wall Street, venture capitalists, technology executives, data scientists — all have important reasons to understand the growth and opportunity in the artificial intelligence market to access business growth and opportunities. This gives them insights on funds invested in AI and analytics as well potential revenue growth and turnover. Indeed, the growth of AI, continuing research, development of easier open source libraries and applications in small to large scale industries are sure to revolutionize the industry the next two decades and the impact is getting felt in almost all the countries worldwide.

To dive deep into the growth of AI and future trends, an insight into the type and size of the market is essential along with (a) AI-related industry market research forecasts and (b) data from reputable research sources for insight into AI valuation and forecasting.

The blog is structured as follows :

  • To provide a short consensus on well-researched projections of AI’s growth and market value in the coming decade.
  • To understand the per capita income and GDP of each country from businesses driven by AI and analytics.

Impact of AI is so widespread, touching and vivid that:

IBM’s CEO claims a potential $2 trillion dollar market for “cognitive computing”).

Google co-founder Larry Page states that “Artificial intelligence would be the ultimate version of Google. The ultimate search engine is capable of understanding everything on the web. It will become so much AI driven that in near future ,it would understand exactly what you wanted and it would give you the right thing. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we’re working on”.

Different sectors exhibit dynamics in terms of adopting and absorbing AI, leading to different levels of economic impact.

Source

On comparing different industry-sectors we see from the figure above:

In high-tech industries like Telecom and media has already adopted AI relatively rapidly and looking for transformations in all possible avenues. They are then followed by Consumer, Financial Services and Professional Services.

Healthcare and Industrial Sector are adopting AI slowly. Energy and Public Sector are the slowest adaptors to this transition.

Further, the economic impact in the telecom and high-tech sector could be more than double that of healthcare in 2030. If the national average of macroeconomic impact is 100, healthcare might experience 40 percent lower impact (i.e. 60). The fast and rapid adopters like the telecom and high-tech sector are highly influenced by AI and could experience 40 percent higher impact (i.e. 140) than the national average.

Several internal and external factors specific to a country or a state, have been known to affect AI-driven productivity growth, including labor automation, innovation, and new competition. In addition, certain micro factors, such as the pace of adoption of AI, and macro factors such as a country’s global connectedness and labor-market structure also plays a certain factor to the size of the impact.

The end result is to grow the AI value chain and boost the ICT sector, making an important economic contribution to an economy.

Production channels: Direct economic impact of AI aims to automate production and save cost. It primarily considers three production dimensions. Firstly it includes calling labor and capital “augmentation”, where new AI capacity is developed, deployed, and operated by new engineers and big data analysts. Second, investment in AI technologies saves labor as machines take over tasks that humans currently perform. Thirdly, better AI-driven innovation saves overall cost (including infrastructure), enabling firms to produce the same output with the same or lower inputs.

Augmentation: Relates to increased use of productive AI-driven labor and capital.

Substitution: AI-driven technologies offer better results in the field like automation, where it has been found to be more cost-effective. It has also discovered ways and means to substitute other factors of production. Advanced economies could gain about 10 to 15 percent of the impact from labor substitution, compared with an impact of 5 to 10 percent in developing economies.

Product and service innovation and extension: Motivation for investment in AI beyond labor substitution can produce additional economic output by expanding firms’ portfolios, increasing channels for products and services (for e.g. AI-based recommendations), developing new business models, or combination of the three.

Externality channels: It serves as one of the external channels where the application of AI tools and techniques can contribute to economic global flows (for e.g. chatbots, news aggregation engines). Such flow happens inter-country (states and geographical boundaries) and even between countries that facilitate more efficient cross-border commerce. It is found that countries that are more connected and participate more in global flows would clearly benefit more from AI. Further AI could boost supply chain efficiency, reduce complexities associated with global contracts, classification, and trade compliance.

Wealth creation and reinvestment: AI is contributing to higher productivity of economies, efficiency gains. Further innovations result in an increase in wages for workers, entrepreneurs, and firms in the form of profits, higher consumption, and more productive investment.

Transition and implementation costs: Several costs incurred while executing the transition to AI like organization restructuring costs, adoption to new solutions, integration costs, and associated project and consulting fees are known to affect the transition in a negative way. Businesses should do a trade-off between cost and benefit analysis and correctly strategize their roadmap.

Negative externalities: AI could induce major negative distributional externalities affecting workers by depressing the labor share of income and potential economic growth.

The following figure illustrates the detailed overall economic impact sustained due to the wider adoption of AI techniques and strategies by businesses.

Source

AI-driven businesses have led to a positive impact on the growth of revenue over consecutive years. More so, the statements made by renowned founders, CEOs, entrepreneurs and visionary leaders is evident from the figure below as it shows the impact of AI on global GDP, the maximum being obtained from venture-backed startups.

Source: https://emerj.com/ai-sector-overviews/valuing-the-artificial-intelligence-market-graphs-and-predictions/

“Tractica forecasts that the revenue generated from the direct and indirect application of AI software is estimated to grow from $643.7 million in 2016 to $36.8 billion by 2025. This represents a significant growth curve for the 9-year period with a compound annual growth rate (CAGR) of 56.8%.”

Tractica has taken a conservative adoption of AI in the hedge fund and investment community, with an assumption that roughly 50% of the hedge fund assets traded by 2025 will be AI-driven. Under this estimate, the algorithmic trading use case remains the top use case among the 191 use cases identified by Tractica.

Further as per reports from Tractica, the market for enterprise AI systems will increase from $202.5 million in 2015 to $11.1 billion by 2024, as depicted in the following figure.

View of Worldwide growth of AI revenue, Source — Tractica

The growth forecasts over the next decade clearly show China’s dominance over the AI market yielding a significant increase in GDP, followed by USA, Nothern Europe, and other nations.

In China, AI is projected to give the economy a 26% boost over the next 13 years, measuring an equivalent of an extra $7 trillion in GDP, helping China to rise to the top. As North America’s companies are widely using AI, the adaptation is at an accelerating phase that it can expect a 14.5% increase in GDP, worth $3.7 trillion.

As the GDP growth varies across continents and nations, the level of AI absorption also varies significantly between the country groups with the most and the least absorption. The below figure demonstrates statistics of economies with higher readiness to benefit from AI. Such countries achieve absorption levels about 11 percentage points higher than those of slow adopters by 2023, and this gap looks set to widen to about 23 percentage points by 2030. This further gives an indication of the digital divide created from AI, between advanced and developing economies.

Source: Mckinsey

The resulting gap in net economic impact between the country groups with the highest economic gains and those with the least is likely to become larger, for e.g. a large gap in economic impact between the leading and the lagging — between Sweden and Zambia. The gap could widen from three percentage points in 2025 to 19 percentage points in 2030 in terms of net GDP impact.

AI is internationally recognized as the main driver of future growth and productivity, innovation, competitiveness and job creation for the 21st century. However, there remain certain technical challenges, that need to be overcome to take it to the next step. The key challenges include

  • Labeled training data
  • Obtaining sufficiently large data sets. 
  • Difficulty explaining results
  • Difficulty generalizingScaling challengesRisk of bias

Apart from the common technical challenges, risks, and barriers faced by organisations implementing AI are evident.

It is now the responsibility of policymakers and business leaders to take measurable actions to address the challenges, support researchers, data scientists, business analysts, and all included in the AI ecosystem to drive the economy with huge momentum.

As rightly quoted by Stephen Hawking, Famous Theoretical Physicist, Cosmologist, and Author:

“Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks.”

References

  1. Valuing the Artificial Intelligence Market, Graphs and Predictions: https://emerj.com/ai-sector-overviews/valuing-the-artificial-intelligence-market-graphs-and-predictions/
  2. NOTES FROM
    THE AI FRONTIERMODELING THE IMPACT OF AI ON THE WORLD ECONOMY: 
    https://www.itu.int/dms_pub/itu-s/opb/gen/S-GEN-ISSUEPAPER-2018-1-PDF-E.pdf
  3. USA-China-EU plans for AI: where do we stand: https://ec.europa.eu/growth/tools-databases/dem/monitor/sites/default/files/DTM_AI%20USA-China-EU%20plans%20for%20AI%20v5.pdf
  4. https://hbr.org/insight-center/interacting-with-ai
  5. https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/

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