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AI systems are ‘only as good as the data we put into them’

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Artificial Intelligence systems are only as good as the data we put into them,” notes a recent IBM article on human bias in AI systems.

Machines are not technically biased, but when data imputed to them is biased in some way, then any data regurgitated by the machine retains that deficiency. And until now hardly anyone has tried to solve this huge problem.

As IBM explains, the largest issue has arisen from bad data that can contain implicit racial, gender or ideological biases. But IBM also believes that bias can be tamed and that the AI systems that will tackle bias will be the most successful.

It’s all in the algorithmic model

There are over 180 human biases that have been defined and classified, and any one of them can affect how we make decisions. According to The Next Web, there are also “confirmation biases” (when a person accepts a result because it confirms a previous belief) or “availability biases” (placing greater emphasis on information relevant to the individual than equally valuable information of less familiarity). All of these can compound through the use of biased data sets, affecting the quality of work and the intended functions of AI.

A team of scientists from the Czech Republic and Germany recently completed a study on bias and AI. The research concludes that when human mistakes become part of the selection of a training rule that shapes the creation of a machine learning model, then we are not really creating artificial intelligence, we are just highlighting our own flawed observations.

Satya Nadella, Microsoft’s CEO, penned an article on the partnership between humans and AI, noting that the most productive debate isn’t whether AI is good or evil, but about “the values instilled in the people and institutions creating this technology.”

He noted six principles needed to be discussed and debated by industry and society alike as we delve further into AI. They include: fairness; reliability and safety; privacy and security; inclusiveness; transparency; and accountability. The application of these principles also entails weeding out bias, either intentional or unintentional.

Taking responsibility for the data

Here is an example of an unintended bias based on data put into a machine-learning system: An AI model for hiring recommendations is trained solely on the existing data and past employees. What if those employees are not diverse? Maybe they are all young white males? The resulting model would likely be unfairly biased against candidates who are older, racial minorities, and female.

The responsibility to eliminate bias in AI is going to be front-and-center as more AI-powered systems come into play throughout society. We will soon see vehicles operated by machines and a large number of surgeries and medical procedures will be conducted by robots. That’s going to put AI developers in the spotlight when tragedy strikes and people look for someone to blame.

The MIT-IBM Watson AI Lab believes it is essential to mitigate bias in artificial intelligence systems if we are to build trust between humans and machines that learn. As the article says, “In the process of recognizing our bias and teaching machines about our common values, we may improve more than AI. We might just improve ourselves.

Karen Graham
Author: Karen Graham

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The importance of data access for digital initiatives

A new report from MuleSoft found that just 37% of organizations have the skills and technology to keep up with digital projects.

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In a global survey of over 1,700 line of business employees in organizations with at least 250 employees, MuleSoft found that just 37% of organizations have the skills and technology to keep up with digital projects.

The resulting report — The State of Business and IT Innovation — reveals four key ideas that IT leaders need to know in order to drive digital innovation forward.

These four key findings are:

  • Collaboration is key 
    • 68% of respondents believe IT and LoB users should jointly drive digital innovation.
  • Keep up the pace 
    • 51% expressed frustration with the speed at which IT can deliver projects.
  • Integration challenge
    • 37% cite security and compliance as the biggest challenge to delivering new digital services, followed by integration (i.e. connecting systems, data, and apps) at 37%.
  • Data access
    • 80% say that in order to deliver on project goals faster, employees need easy access to data and IT capabilities.  

“This research shows data is one of the most critical assets that businesses need to move fast and thrive into the future,” said MuleSoft CEO Brent Hayward

“Organizations need to empower every employee to unlock and integrate data — no matter where it resides — to deliver critical, time-sensitive projects and innovation at scale, while making products and services more connected than ever.”

Want to read through the whole report? Download it from MuleSoft

DX Journal Staff
Author: DX Journal Staff

DX Journal covers the impact of digital transformation (DX) initiatives worldwide across multiple industries.

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Where is the financial value in AI? Employing multiple human-machine learning approaches, say experts

According to a new study, only 10% of organizations are achieving significant financial benefits with AI.

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AI is everywhere these days — especially as we work to fight the spread of COVID-19

Even in the “before times,” AI was a hot topic that always found itself in the center of most digital transformation conversations. A new study from MIT Sloan Management Review, BCG GAMMA, and BCG Henderson Institute, however, prompts a crucial question:

Are You Making the Most of Your Relationship with AI?

Finding value

Despite the proliferation of the technology and increased investment, according to the report, just 10% of organizations are achieving significant financial benefits with AI. The secret ingredient in these success stories? “Multiple types of interaction and feedback between humans and AI,” which translated into a six-times better chance of amplifying the organization’s success with AI.

“The single most critical driver of value from AI is not algorithms, nor technology — it is the human in the equation,” affirms report co-author Shervin Khodabandeh.

 

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From a survey of over 3,000 managers from 29 industries based in 112 countries — plus in-depth interviews with experts — the report outlined three investments organizations can make to maximize value:

  • The likelihood of achieving benefits increases by 19% with investment in AI infrastructure, talent, and strategy.
  • Scalability. When organizations think beyond automation as a use case, the likelihood of financial benefit increases by 18%.
  • “Achieving organizational learning with AI (drawing on multiple interaction modes between humans and machines) and building feedback loops between human and AI increases that likelihood by another 34%.”

According to report co-author Sam Ransbotham, at the core of successfully creating value from AI is continuous learning between human and machine:

“Isolated AI applications can be powerful. But we find that organizations leading with AI haven’t changed processes to use AI. Instead, they’ve learned with AI how to change processes. The key isn’t teaching the machines. Or even learning from the machines. The key is learning with the machines — systematically and continuously.” 

Continued growth

While just 1 in 10 organizations finds financial benefits with AI, 70% of respondents understand how it can generate value — up from 57% in 2017.

Additionally, 59% of respondents have an AI strategy, compared to 39% in 2017, the survey found. Finally, 57% of respondents say their organizations are “piloting or deploying” AI — not a huge increase from 2017 (46%). 

One of the biggest takeaways? According to co-author David Kiron, “companies need to calibrate their investments in technology, people, and learning processes.”

“Financial investments in technology and people are important, but investing social capital in learning is critical to creating significant value with AI.”

DX Journal Staff
Author: DX Journal Staff

DX Journal covers the impact of digital transformation (DX) initiatives worldwide across multiple industries.

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Bringing DX to the food supply chain in a pandemic

In a new paper, supply chain stakeholders share how COVID-19 has affected the transformation of the sector.

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There’s little doubt that COVID-19 had a profound effect on the food supply chain.

As one example, just think back to roughly March of this year, when virus transmission was rapidly picking up speed. Remember the reports of food and beverage companies only producing their most popular or essential products? Or how it would take slightly longer than usual to restock certain products? What about the rush to integrate — or quickly improve the efficiency of — digital and e-commerce. 

Panning out a bit, think about food safety and quality professionals. The need to stay safe — and in many cases, stay at home — meant performing the very hands-on job of monitoring, auditing, inspecting at a distance, i.e. digitally. 

When the food supply chain was hit by storages, delays, breakdowns, and lockdowns, the end result was — like in so many sectors — a rapid digital transformation.

As The Food Safety Market — an SME-powered industrial data platform dedicated to boosting the competitiveness of European food certification — elaborates in a new discussion paper, “technology has played an important role in enabling business continuity in the new reality.”

The paper — Digital Transformation of Food Quality & Safety: How COVID-19 accelerates the adoption of digital technologies across the food supply chain — features industry experts from companies like Nestlé, Ferrero, PepsiCo, McCormick & Company, and more discussing the effects of the pandemic on the supply chain.

A few highlights from the paper:

  • John Carter, Area Europe Quality Director for Ferrero put the issue of food access into perspective at the start of his interview:

“The production of food defines our world. The effects of agriculture on our daily lives are so omnipresent that they can be easy to overlook; landscapes and societies are profoundly influenced by the need to feed our growing population. But much has been taken for granted. Only occasionally are we forced to consider: ‘where does our food come from?'”

  • Ellen de Brabander, Senior Vice President of R&D for PepsiCo provided insight on the cost benefits of digital transformation:

“The need for customization is a big driver for accelerating digital transformation and moving away from a ‘one size fits all’ approach. This means that the cost to develop and produce a product must be lower and digital technologies provide a clear opportunity here.” 

  • Clare Menezes, Director of Global Food Integrity for McCormick & Company brought up one area where digital tools need to go:

“There aren’t any areas where digital tools “fail”, but there is a need for tools that ‘prove out’ predictions around where the next integrity event will play out and how it could lead to quality or food safety failure. These tools are an obvious candidate for AI given the number of PESTLE factors that might come into play.” 

Want to read all of the interviews? Check out the paper here.

DX Journal Staff
Author: DX Journal Staff

DX Journal covers the impact of digital transformation (DX) initiatives worldwide across multiple industries.

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