Artificial intelligence is not old news. According to Stanford’s 2021 AI Index, the global corporate AI investment is at a record high of $67 billion. PWCs 22nd Global CEO Survey shows that 77% of Fortune 500 CEOs are planning to or have already started AI initiatives. With the increasing importance of artificial intelligence, all eyes are on data science to deliver substantial results.

The advantage that comes with artificial intelligence for businesses is common knowledge in the tech world. The 2020 McKinsey Global AI Survey states that AI is contributing more than 20% of EBIT (Earnings Before Interest and Tax) for an elite group of artificial intelligence practitioners. Additionally, these companies have bigger budgets to spend on artificial intelligence initiatives than their competitors. With this comes the ability to develop AI solutions that they require in-house, instead of depending on external suppliers.

As data science advances, the pressure for the field to deliver tangible results and live up to its hype increases. A significant yet understated role for the success of artificial intelligence products in the AI Product Owner (AI PO). Here’s everything you need to know about AI PO.

Who Is A Product Owner?

Scrum framework, a popular agile development method defines the role of a product owner as someone who is responsible for maximizing the value of the scrum team. The scrum framework depends on the role of a product owner, scrum master, and developer. By this, we understand that a product owner creates a product vision, communicates the vision with the stakeholders, and creates the product backlog.  The Scrum framework also dictates that a PO is required to have a business, user experience, technical and communication skills.

An extended, specialized role of a product owner is the role of an AI PO. AI POs inherit the duties of a general PO with the difference that the AI POs’ work revolves around artificial intelligence-based products.

The Job Of An AI Product Owner

AI-powered products obviously differ from conventional software products. AI products use data to learn patterns without developer support. Unlike traditional software products, AI products improve on their own as and when the data keeps coming. Machine learning facilitates the building of products that were not possible to build before the emergence of AI like speech recognition devices, automating driving, etc. Because AI products hold such immense power, it is crucial for AI PO’s to adjust their skills to adapt themselves.

All The Necessary Skills

Firstly, all AI PO’s need to know everything about AI-based products, from how they work, their merits, and then pitfalls. Secondly, AI POs need to attentively monitor the predictions made by the AI models. Artificial intelligence is based on statistical assumptions, so their predictions need to be observed for uncertainty. AI POs should make it to a point to design AI applications to include human-decision making wherever necessary because one wrong prediction can have grave consequences. AI-based products are also dynamic. They identify how customers react to the prediction. This is how the system knows to make adjustments to data. Lastly, AI PO’s need to understand that AI development differs from software development and its workflow. While traditional software development can follow a modular and structural approach, AI developments test various hypotheses and perform quickly. Understanding that machine learning development isn’t as gradual as traditional software development is important to communicate the expectations with stakeholders.

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Artificial intelligence is not old news. According to Stanford’s 2021 AI Index, the global corporate AI investment is at a record high of $67 billion. PWCs 22nd Global CEO Survey shows that 77% of Fortune 500 CEOs are planning to or have already started AI initiatives. With the increasing importance of artificial intelligence, all eyes are on data science to deliver substantial results.

The advantage that comes with artificial intelligence for businesses is common knowledge in the tech world. The 2020 McKinsey Global AI Survey states that AI is contributing more than 20% of EBIT (Earnings Before Interest and Tax) for an elite group of artificial intelligence practitioners. Additionally, these companies have bigger budgets to spend on artificial intelligence initiatives than their competitors. With this comes the ability to develop AI solutions that they require in-house, instead of depending on external suppliers.

As data science advances, the pressure for the field to deliver tangible results and live up to its hype increases. A significant yet understated role for the success of artificial intelligence products in the AI Product Owner (AI PO). Here’s everything you need to know about AI PO.

Who Is A Product Owner?

Scrum framework, a popular agile development method defines the role of a product owner as someone who is responsible for maximizing the value of the scrum team. The scrum framework depends on the role of a product owner, scrum master, and developer. By this, we understand that a product owner creates a product vision, communicates the vision with the stakeholders, and creates the product backlog.  The Scrum framework also dictates that a PO is required to have a business, user experience, technical and communication skills.

An extended, specialized role of a product owner is the role of an AI PO. AI POs inherit the duties of a general PO with the difference that the AI POs’ work revolves around artificial intelligence-based products.

The Job Of An AI Product Owner

AI-powered products obviously differ from conventional software products. AI products use data to learn patterns without developer support. Unlike traditional software products, AI products improve on their own as and when the data keeps coming. Machine learning facilitates the building of products that were not possible to build before the emergence of AI like speech recognition devices, automating driving, etc. Because AI products hold such immense power, it is crucial for AI PO’s to adjust their skills to adapt themselves.

All The Necessary Skills

Firstly, all AI PO’s need to know everything about AI-based products, from how they work, their merits, and then pitfalls. Secondly, AI POs need to attentively monitor the predictions made by the AI models. Artificial intelligence is based on statistical assumptions, so their predictions need to be observed for uncertainty. AI POs should make it to a point to design AI applications to include human-decision making wherever necessary because one wrong prediction can have grave consequences. AI-based products are also dynamic. They identify how customers react to the prediction. This is how the system knows to make adjustments to data. Lastly, AI PO’s need to understand that AI development differs from software development and its workflow. While traditional software development can follow a modular and structural approach, AI developments test various hypotheses and perform quickly. Understanding that machine learning development isn’t as gradual as traditional software development is important to communicate the expectations with stakeholders.

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Sean Nierat from PayPal has explained to The Paypers why there is a need of systems that not only make accurate predictions, but also that explain why they’ve arrived at a particular answer 

Machine learning (ML) is part of a burgeoning AI industry that could soon become a multitrillion-dollar opportunity for global businesses. It is being used by PayPal, and others to help pioneer advanced data-driven fraud prevention by enhancing human intelligence with a 360-degree view of each customer. Yet, as ML becomes ubiquitous, it’s increasingly being argued that we not only need systems to make accurate predictions but also ones that explain why they’ve arrived at a particular answer. 

Tackling bias with transparency 

We’ve come a long way from the old days of fraud prevention. It’s undeniable that bad actors are getting smarter, with huge volumes of readily accessible customer data at their disposal and a wealth of tools bought on the dark web. Sophisticated fraud built on these foundations demands an equally sophisticated response. That’s why PayPal uses advanced ML to continually optimize the complex rules written by our client’s in-house fraud and data science teams, and to apply these rules to large datasets in order to spot patterns that humans may miss. 

The problem with such systems is that they’re only as good as the data they’re trained on. Increasingly, organizations are concerned about unconscious bias emanating from this data, and the algorithms designed to interpret it. With ML used today in everything from mortgage application approvals to police facial recognition systems, there are important questions to answer – especially in a new era of intense regulatory scrutiny. 

Clear box vs black box 

This is where clear box ML or ‘explainable AI’ (XAI) approaches come into their own. Black box models like artificial neural networks (ANNs) or deep learning operate so that even the humans that designed them don’t know how decisions are made. However, with XAI, businesses gain vital insight into the whole process, from data collection to decision making. 

This additional clarity and transparency offers multiple benefits including: 

• improves business confidence in an XAI-powered prediction/ outcome; 
• enhances the ability to control and manage algorithms in line with business objectives; 
• increases accountability, as systems can be audited; 
• improves regulatory compliance efforts; 
• enables teams to identify new fraud patterns faster.

A new approach 

PayPal’s enterprise Fraud Protection offerings champion clear box, advanced ML through our use of explainability methods like LIME, Shapley, and RL-LIM. Our prediction engine delivers an interpretability plot for every single event, helping to drive customer confidence in the results and continued ongoing improvements. 

Our platform is purpose-built to handle both the complex fraud challenges businesses face today and to make the necessary adjustments to help address those of tomorrow. With PayPal, businesses can take a dynamic approach to fraud – streamlining the experience for good customers and adding protection layers when necessary. 

PayPal leverages fraud and risk knowledge from its 2-Sided-Network of over 330 million customers and 25 million merchants transacting 12 billion times a year as well as integrated third-party feeds to enable the processing and correlation of vast amounts of heterogeneous data to help deliver actionable business intelligence. 

Here’s how: 

• a purpose-built data lake stores structured and unstructured data from various sources; 
• powerful Device Recon analyses hundreds of mobile and desktop device characteristics and behaviours, and applies machine learning models for risk scoring and clustering; 
• easy-to-update rules and machine learning algorithms help businesses adapt to changing fraud schemes; 
• robust link analysis and data visualization help enable businesses to proactively uncover anomalous patterns indicative of fraud; 
• real-time complex authentication helps differentiate trusted from suspicious users.

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Though everyone seems to be piling on the machine learning bandwagon, it’s a game that only the rich can play, as I’ve written. While open source machine learning projects like Google’s TensorFlow and Amazon’s DSSTNE lower the bar to would-be machine learning engineers, resolving the skills deficit that Gartner analyst Merv Adrian called the biggest hurdle to machine learning success, no amount of training can resolve a thornier issue: Lack of data.

Yandex, the Google of Russia, has plenty of data, coupled with experience wrangling it to machine learning success. It’s therefore fascinating to hear Alexander Khaytin, COO of its sister site Yandex Data Factory, talk through the best ways to bridge the data divide that keeps the vast majority of enterprises from achieving machine learning success.

But first, you’re going to need data. Lots of data.

Teaching your data to fish

Data, of course, is needed to train machine learning algorithms. Many companies simply don’t have the data assets necessary for such training. However, according to Khaytin, for the kinds of companies that undertake serious machine learning projects, volume of data isn’t the issue—getting it into one place is:

While most companies undertaking machine learning projects inevitably own and store vast quantities of data, this data is not always ready to use. With data often siloed in separate storage and processing systems, the aggregation of data can be time-consuming and difficult. Additionally, when extracting data, companies must take data security into consideration with almost all data being “poisoned” by personal or sensitive kind of data.

Compounding the problem, many organizations lack the willingness to experiment, a key component of machine learning, and are especially reluctant to do so on live, production systems. As he stated, “[W]hen it comes to prescriptive analytics, the measure of business impact can only truly be assessed by actually applying a machine learning model in the real business process. For most companies, often at the start of their digital transformation, the prospect of launching large scale machine learning projects which haven’t already demonstrated their value in previous trials can be daunting.”

Kissing cousin to this willingness to experiment, Khaytin concludes, is business agility. “There are no beaten paths with machine learning yet: The technology is new, the success is not guaranteed, and the experimentation is crucial. By ensuring agile and flexible business processes, companies will spend less time, effort, and money on unsuccessful projects.”

All of which is easier said than done. How can enterprises overcome data silos and embrace a culture of experimentation and agility?

Open source can help

While not a panacea, open source offers a way for organizations to experiment without locking themselves into expensive software or infrastructure that inhibits agility. Though open source won’t aid in eradicating data silos, it lowers the bar to trial-and-error.

As Michael St. James wrote to me of his machine learning work in the music industry, “In my world, open source makes it easier to try to invent/deploy ML stuff that may not be monetized.” MuckRock founder Michael Morisy agreed, telling me, open source machine learning projects like TensorFlow “make[] it easy to experiment and in some domains [enable you to] get meaningful results without [a] ton of expertise.”

Because the only cost to getting started is one’s time (and renting infrastructure), open source makes it easier to learn to scale machine learning projects, starting with exceptional, trusted code from Google, Facebook, and more. Over time, such open source tinkering can bleed into the larger organization, fostering the curiosity and agility that Khaytin insists is critical to machine learning success.

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