IBM's announcements offer capabilities to promote more transparency across AI use cases to reduce any potential resistance to automating business processes. IBM today showcased additional artificial intelligence (AI) capabilities that include an AI Factsheets capability that promises to make it simpler to understand and track the provenance of AI models. Those factsheets are intended for artificial intelligence models to be roughly the equivalent of nutrition labels for foods or information sheets for appliances. There is also now a more efficient intent classification model for Watson Assistant that makes it simpler to train the artificial intelligence tool using less data. That capability is significant because many processes that IT organizations are trying to automate don't have the amount of data that previously would have been required to train a virtual assistant.

Show your working: generations of mathematics students have grown up with this mantra. Getting the right answer is not enough. To get top marks, students must demonstrate how they got there. Now, machines need to do the same. As artificial intelligence (AI) is used to make decisions affecting employment, finance or justice, as opposed to which film a consumer might want to watch next, the public will insist it explains its working.

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).

The explosion of artificial intelligence (AI) is not just a boon for business. It is also helping solve some of the world's biggest social problems, from reducing crime to eradicating disease and tackling climate change. The amount of available data and technology that can process it intelligently has snowballed as the internet has increasingly integrated with our lives through tablets, phones and wearables. The advent of the internet of things – the extension of internet connectivity into everyday objects – has taken this even further. These advances have enabled a wide range of bodies, including companies, governments and non-governmental organisations, to start working together to use AI for social good and has already produced some groundbreaking results in vital areas.

IBM Research today introduced AI Explainability 360, an open source collection of state-of-the-art algorithms that use a range of techniques to explain AI model decision-making. The launch follows IBM's release a year ago of AI Fairness 360 for the detection and mitigation of bias in AI models. IBM is sharing its latest toolkit in order to increase trust and verification of artificial intelligence and help businesses that must comply with regulations to use AI, IBM Research fellow and responsible AI lead Saska Mojsilovic told VentureBeat in a phone interview. "That's fundamentally important, because we know people in organizations will not use or deploy AI technologies unless they really trust their decisions. And because we create infrastructure for a good part of this world, it is fundamentally important for us -- not because of our own internal deployments of AI or products that we might have in this space, but it's fundamentally important to create these capabilities because our clients and the world will leverage them," she said.

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).

Teaching anyone about "fairness" is a laudable goal. As humans, we may not necessarily agree on what's fair. It sometimes depends on the context. Teaching kids to be fair -- both at home and in school -- is fundamental, but it's easier said than done. With this in mind, how can we, as a society, communicate the nuances of "being fair" to artificial intelligence (AI) systems?

Google subsidiary DeepMind is leveraging AI to determine how to refer optometry patients. Haven Life is using AI to extend life insurance policies to people who wouldn't traditionally be eligible, such as people with chronic illnesses and non-U.S. And Google self-driving car spinoff Waymo is tapping it to provide mobility to elderly and disabled people. But despite the good AI is clearly capable of doing, doubts abound over its safety, transparency, and bias. IBM thinks part of the problem is a lack of standard practices.