Artificial Intelligence For Good - Also Makes Business Sense

Artificial Intelligence (AI) has been put forward as a potential solution for many of the gravest problems facing society, from the opioid crisis to poverty and famine.

But although technology clearly has the potential to do a great deal of good, there’s a sound business reason that tech companies often pour large amounts of resources into social projects that don’t seem to align with their core business of selling software and services.

This is down to the fact that tackling social issues often involves developing solutions to problems very similar to those faced by businesses. Additionally, working with governments or NGOs on building these solutions can often mean access to new datasets. Learning derived from these datasets can later be developed into products and services to offer to clients (even if the data itself isn't).

In 2016, IBM launched a program of initiatives called Science for Social Good. It aimed to develop technology-driven solutions to 17 issues highlighted by the United Nations as Sustainable Development Goals (SDGs). These include reducing poverty, inequality, and damage to the environment, as well as raising standards of healthcare and education across the world.

Today IBM has announced progress that has been made across 15 of those 17 initiatives thanks to technology and research it has carried out. I got the chance to speak to two people involved with this work - IBM fellow Aleksandra Mojsilovic and principal researcher Kush Varshney – about why this work is valuable to IBM itself, as well as society as a whole. 

But although technology clearly has the potential to do a great deal of good, there’s a sound business reason that tech companies often pour large amounts of resources into social projects that don’t seem to align with their core business of selling software and services.

This is down to the fact that tackling social issues often involves developing solutions to problems very similar to those faced by businesses. Additionally, working with governments or NGOs on building these solutions can often mean access to new datasets. Learning derived from these datasets can later be developed into products and services to offer to clients (even if the data itself isn't).

Mojsilovic told me “Around 2013 or 2014, we were trying to figure out a way to do something good with our skills and one of the emerging things back then was the Ebola epidemic – we thought that putting our data skills to work to help with that would be fantastic and we learned a lot of lessons from doing so.

"But we found that people were mainly 'doing good' as a volunteer effort – weekends and hackathons, that sort of thing, which didn't quite seem right. IBM research is pretty big, we have over 3,000 researchers around the world, and we decided there had to be a way to leverage these skills more broadly and in a coherent way … it didn't really seem right that we were just trying to solve these problems in our spare time."

This was where the idea of integrating IBM’s Science for Social Good with the UN’s SDG’s first emerged, and its researchers realized the importance of collaborating with governmental and NGOs with expertise in their fields.

“One thing we learned from Ebola was that we were a bit arrogant,” explains Mojsilovic. “We thought we have all these skills which we can use to do a great deal of good; then we got a wake-up call because we realized that these incredibly difficult problems have components to them, and some are solved by truly understanding the problem, and others by truly understanding the technology.

“We learned that having a program that's really focused on creating tools or technology won't work without the participation of those who really work with these problems."

Several initiatives were developed that tied directly into the UNs SDGs. These included one that aimed at fairness around risk assessment in financial services, including health insurance in the US and mobile-based money lending programs in east Africa. Here, the aim was to use technology to mitigate against the risk of bias leading to unfair or discriminatory outcomes.

Another aimed the opioid epidemic currently plaguing the US, by harnessing machine learning to determine which patients were more likely to become addicts after being prescribed opioid treatments.

Other initiatives include driving data-driven research into multiple sclerosis, developing AI-driven systems to assist those on low incomes with managing their finances, assisting the UN in driving its sustainable development goals and predict outbreaks of Zika virus.

As well as knowing that they were helping to solve some of the most difficult scientific problems facing our species and planet, researchers were spurred on by the fact that their work also has value for IBM and their clients.

Mojsilovic tells me, "For example … the opioid project involves causal modeling – a really hot topic right now in machine learning. 

"So when we started looking at problems where causal modeling could help, many of them are in this space of social intervention, policy development, healthcare intervention, genomics – so now you get phenomenal material to try out your techniques, and the big problems feedback to inform you … you create a library of reusable assets, which you can apply to so many problems."

Working on world-scale social problems also helps IBM and other tech companies to investigate ways of scaling their solutions to fit world-scale business problems.

One example of how this is already being used to help IBM clients is the AI Fairness 360 platform. Elements of this were developed thanks to the work on improving social inclusion and fairness in finance and lending, Varshney tells me.

“Fairness is a big topic right now in machine learning research. Several projects in the Science for Good program are around fairness … gender equality or other forms of equality, and working on fairness in those settings help us to move forward on this journey. Then it’s about pulling it all together to create these core technologies … and putting some of those capabilities into the IBM product.”

 So, while it’s still true that virtue can be its own reward – helping to tackle world problems at UN-scale as part of their day-to-day work is likely to increase job satisfaction – there are certainly sound business reasons behind IBM (and others’) decision to dedicate resources to solving the world’s problems.

As Varshney tells me, “We’re also very aware these are huge problems, not the kind of things we will solve with a project or two – they are going to take decades to solve.

“We’re building blocks that others can use and re-use, and take them further – it would be arrogant of us to say we’re here to save the world, don’t want the message to come across like that!”

Thank you for reading my post. Here at LinkedIn and at Forbes I regularly write about management and technology trends. I have also written a new book about AI, click here for more information. To read my future posts simply join my network here or click 'Follow'. Also feel free to connect with me via Twitter, Facebook, Instagram, Slideshare or YouTube.

About Bernard Marr

Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a strategic business & technology advisor to governments and companies. He helps organisations improve their business performance, use data more intelligently, and understand the implications of new technologies such as artificial intelligence, big data, blockchains, and the Internet of Things.

LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent contributor to the World Economic Forum and writes a regular column for Forbes. Every day Bernard actively engages his 1.5 million social media followers and shares content that reaches millions of readers.