Artificial Intelligence Emerging Technologies – AI at work

Introduction

Artificial intelligence is a technology that is already impacting how users interact with and are affected by the Internet. Soon its impact is likely to only continue to grow. AI has the potential to vastly change the way that humans interact, not only with the digital world, but also with each other, through their work and through other socioeconomic institutions – for better or for worse.

AI traditionally refers to an artificial creation of human-like intelligence that can learn, reason, plan, perceive, or process natural language.

Artificial intelligence is further defined as “narrow AI” or “general AI”. Narrow AI, which we interact with today, is designed to perform specific tasks within a domain (e.g. language translation). General AI is hypothetical and not domain specific but can learn and perform tasks anywhere. This is outside the scope of this paper. This paper focuses on advances in narrow AI, particularly on the development of new algorithms and models in a field of computer science referred to as machine learning.

Current Uses of Artificial Intelligence

• Email filtering: Email services use artificial intelligence to filter incoming emails. Users can train their spam filters by marking emails as “spam”.
• Personalization: Online services use artificial intelligence to personalize your experience. Services, like Amazon or Netflix, “learn” from your previous purchases and the purchases of other users in order to recommend relevant content for you.
• Fraud detection: Banks use artificial intelligence to determine if there is strange activity on your account. Unexpected activity, such as foreign transactions, could be flagged by the algorithm.
• Speech recognition: Applications use artificial intelligence to optimize speech recognition functions. Examples include intelligent personal assistants, e.g. Amazon’s “Alexa” or Apple’s “Siri”.

The advantages of Artificial intelligence applications

• Reduction in Human Error: With Artificial intelligence, the decisions are taken from the previously gathered information applying a certain set of algorithms. So errors are reduced and the chance of reaching accuracy with a greater degree of precision is a possibility
• Takes Human risk: Overcome many risky limitations of humans by developing an AI Robot which in turn can do the risky things for us
• Available 24x7: Make machines work 24x7 without any breaks and they don’t even get bored, unlike humans.
• Best for Repetitive Jobs: Automate these mundane tasks and can even remove “boring” tasks for humans and free them up to be increasingly creative
• Reduction in Human Error: The digital assistants also used in many websites to provide things that users want. We can chat with them about what we are looking for
• Faster Decisions: While taking a decision human will analyze many factors both emotionally and practically but AI-powered machine works on what it is programmed and delivers the results in a faster way
• Daily Applications: Apple’s Siri, Window’s Cortana, Google’s OK Google are frequently used in our daily routine whether it is for searching a location, taking a selfie, making a phone call, replying to a mail and many more

Important areas for current growth in AI and Machine Learning

• Data availability: Just over 3 billion people are online with an estimated 17 billion connected devices or sensors. That generates a large amount of data which, combined with decreasing costs of data storage, is easily available for use. Machine learning can use this as training data for learning algorithms, developing new rules to perform increasingly complex tasks.
• Computing power: Powerful computers and the ability to connect remote processing power through the Internet make it possible for machine-learning techniques that process enormous amounts of data.
• Algorithmic innovation: New machine learning techniques, specifically in layered neural networks – also known as “deep learning” – have inspired new services but is also spurring investments and research in other parts of the field.

How EY leverages AI

EY is broadly infusing AI in two ways:

• Incrementally adding AI to current approaches, and
• Researching in new business models and using AI to develop them

Examples:

• In Tax and Assurance services, an intelligent tax classifier that uses different machine learning models to classify and identify various taxable transactions is being actively used.
• EY Fraud Investigation and Dispute Services (FIDS) has assisted global pharmaceutical companies with email, instant messaging, voicemail and document reviews to comply with regulator inquiries, using AI and advanced analytics technologies. FIDS also helped a multinational food and beverage company with a fraudulent invoicing case that resulted in 97% accuracy in identifying suspicious invoices using ML technologies.

EY Trusted AI Platform

EY Trusted AI Platform provides an integrated approach to evaluate, quantify and monitor the impact and trustworthiness of artificial intelligence (AI).

By developing a quantitative score of an AI system’s residual risk, the EY Trusted AI Platform provides the ability to:

• Map residual risk across an organization’s AI portfolio and against their risk tolerance level
• Drill-down into the drivers of risk to develop targeted risk mitigation strategies
• Perform dynamic risk management to forecast the risk impact of AI design changes

How the platform works:

The EY Trusted AI Platform uses interactive, web-based schematic and assessment tools to build the risk profile of an AI system. It then uses an advanced analytical model to convert the user responses to a composite score comprising technical risk, stakeholder impact and control effectiveness of an AI system. 

EY Trusted AI Platform

• To help determine technical risk, the platform evaluates the technical design of an AI system, measuring risk drivers that include its underlying technologies, technical operating environment and level of autonomy. 
• To help determine stakeholder risk, the platform considers the goals and objectives of the AI system. It also considers the financial, emotional and physical impact on the external and internal users, as well as the reputational, regulatory and legal risk. 
• The control effectiveness score considers the existence and operating effectiveness of controls and acts as a mitigating factor to reduce the risks of AI.
• By combining the three scores, the EY Trusted AI Platform calculates the residual risk of an AI system. Based on the anticipated impact on users, stakeholder risk acts as a multiplier to technical risk, considering social and ethical implications. The evaluation of governance and control maturity acts as a mitigating factor to reduce residual risk of an AI system.
• User-friendly visualizations provide a quick snapshot of the relative risk scores across an organization’s AI portfolio, with drill-down capabilities to reveal additional details. 

Managing the Risks

Building trust in AI will take a coordinated approach. EY team believes there are five pillars of trust:

1.  Advocacy – Do stakeholders understand the benefits of AI and how it will enhance the products and services they receive?
2.  Proficiency – Does AI enhance and improve an organization's brand, product, service and stakeholder experience?
3.  Consistency – Is the AI use in alignment with an organization's stated purpose and support its achievement over time?
4.  Openness – Has the organization effectively communicated and engaged with its core stakeholder groups on its use of AI and the potential benefits and risks?
5.  Integrity – Is the organization’s approach to the design and operation of trusted AI in line with the expectations of its stakeholders?

In establishing the five pillars of trust, the overarching element that connects them all is “Accountability.”

Conclusion

• AI has already begun to disrupt the way that we work and live. Organizations that will thrive in an AI world will be those that can optimize both data and trust feedback loops to attract more users and accelerate their adoption of AI.
• By acting in good faith, developing a robust AI risk management system and involving users in their AI journey, organizations will go a long way in establishing user trust as a competitive differentiator and translating risk foresight into AI insight.
• Organizations must put trust at the heart of their AI systems and leverage risk foresight to accelerate their access to AI insights. Advanced AI tools can assist an organization in their journey by providing insights on the sources and drivers of risk and guiding an AI design team in developing targeted risk mitigation strategies.