Personal blog of Yzmir Ramirez

IBM has launched a software service that scans AI systems as they work in order to detect bias and provide explanations for the automated decisions being made — a degree of transparency that may be necessary for compliance purposes not just a company’s own due diligence.

The new trust and transparency system runs on the IBM cloud and works with models built from what IBM bills as a wide variety of popular machine learning frameworks and AI-build environments — including its own Watson tech, as well as Tensorflow, SparkML, AWS SageMaker, and AzureML.

It says the service can be customized to specific organizational needs via programming to take account of the “unique decision factors of any business workflow”.

The fully automated SaaS explains decision-making and detects bias in AI models at runtime — so as decisions are being made — which means it’s capturing “potentially unfair outcomes as they occur”, as IBM puts it.

It will also automatically recommend data to add to the model to help mitigate any bias that has been detected.

Explanations of AI decisions include showing which factors weighted the decision in one direction vs another; the confidence in the recommendation; and the factors behind that confidence.

IBM also says the software keeps records of the AI model’s accuracy, performance and fairness, along with the lineage of the AI systems — meaning they can be “easily traced and recalled for customer service, regulatory or compliance reasons”.

For one example on the compliance front, the EU’s GDPR privacy framework references automated decision making, and includes a right for people to be given detailed explanations of how algorithms work in certain scenarios — meaning businesses may need to be able to audit their AIs.

The IBM AI scanner tool provides a breakdown of automated decisions via visual dashboards — an approach it bills as reducing dependency on “specialized AI skills”.

However it is also intending its own professional services staff to work with businesses to use the new software service. So it will be both selling AI, ‘a fix’ for AI’s imperfections, and experts to help smooth any wrinkles when enterprises are trying to fix their AIs… Which suggests that while AI will indeed remove some jobs, automation will be busy creating other types of work.

Nor is IBM the first professional services firm to spot a business opportunity around AI bias. A few months ago Accenture outed a fairness tool for identifying and fixing unfair AIs.

So with a major push towards automation across multiple industries there also looks to be a pretty sizeable scramble to set up and sell services to patch any problems that arise as a result of increasing use of AI.

And, indeed, to encourage more businesses to feel confident about jumping in and automating more. (On that front IBM cites research it conducted which found that while 82% of enterprises are considering AI deployments, 60% fear liability issues and 63% lack the in-house talent to confidently manage the technology.)

In additional to launching its own (paid for) AI auditing tool, IBM says its research division will be open sourcing an AI bias detection and mitigation toolkit — with the aim of encouraging “global collaboration around addressing bias in AI”.

“IBM led the industry in establishing trust and transparency principles for the development of new AI technologies. It’s time to translate principles into practice,” said David Kenny, SVP of cognitive solutions at IBM, commenting in a statement. “We are giving new transparency and control to the businesses who use AI and face the most potential risk from any flawed decision making.”

More than seven years after IBM Watson beat a couple of human Jeopardy! champions, the company has continued to make hay with the brand. Watson, at its core, is simply an artificial intelligence engine and while that’s not trivial by any means, neither is it the personified intelligence that their TV commercials would have the less technically savvy believe.

These commercials contribute to this unrealistic idea that humans can talk to machines in this natural fashion. You’ve probably seen some. They show this symbol talking to humans in a robotic voice explaining its capabilities. Some of the humans include Bob Dylan, Serena Williams and Stephen King.

In spite of devices like Alexa and Google Home, we certainly don’t have machines giving us detailed explanations, at least not yet.

IBM would probably be better served aiming its commercials at the enterprises it sells to, rather than the general public, who may be impressed by a talking box having a conversation with a star. However, those of us who have at least some understanding of the capabilities of such tech, and those who buy it, don’t need such bells and whistles. We need much more practical applications. While chatting with Serena Williams about competitiveness may be entertaining, it isn’t really driving home the actual value proposition of this tech for business.

The trouble with using Watson as a catch-all phrase is that it reduces the authenticity of the core technology behind it. It’s not as though IBM is alone in trying to personify its AI though. We’ve seen the same thing from Salesforce with Einstein, Microsoft with Cortana and Adobe with Sensei. It seems that these large companies can’t deliver artificial intelligence without hiding it behind a brand.

The thing is this though, this is not a consumer device like the Amazon Echo or Google Home. It’s a set of technologies like deep learning, computer vision and natural language processing, but that’s hard to sell, so these companies try to put a brand on it like it’s a single entity.

Just this week, at the IBM Think Conference in Las Vegas, we saw a slew of announcements from IBM that took on the Watson brand. That included Watson Studio, Watson Knowledge Catalog, Watson Data Kits and Watson Assistant. While they were at it, they also announced they were beefing up their partnership Apple with — you guessed it — Watson and Apple Core ML. (Do you have anything without quite so much Watson in it?)

Marketers gonna market and there is little we can do, but when you overplay your brand, you may be doing your company more harm than good. IBM has saturated the Watson brand, and might not be reaching the intended audience as a result.

Apple and IBM may seem like an odd couple, but the two companies have been working closely together for several years now. That has involved IBM sharing its enterprise expertise with Apple and Apple sharing its design sense with IBM. The companies have actually built hundreds of enterprise apps running on iOS devices. Today, they took that friendship a step further when they announced they were providing a way to combine IBM Watson machine learning with Apple Core ML to make the business apps running on Apple devices all the more intelligent.

The way it works is that a customer builds a machine learning model using Watson, taking advantage of data in an enterprise repository to train the model. For instance, a company may want to help field service techs point their iPhone camera at a machine and identify the make and model to order the correct parts. You could potentially train a model to recognize all the different machines using Watson’s image recognition capability.

The next step is to convert that model into Core ML and include it in your custom app. Apple introduced Core ML at the Worldwide Developers Conference last June as a way to make it easy for developers to move machine learning models from popular model building tools like TensorFlow, Caffe or IBM Watson to apps running on iOS devices.

After creating the model, you run it through the Core ML converter tools and insert it in your Apple app. The agreement with IBM makes it easier to do this using IBM Watson as the model building part of the equation. This allows the two partners to make the apps created under the partnership even smarter with machine learning.

“Apple developers need a way to quickly and easily build these apps and leverage the cloud where it’s delivered. [The partnership] lets developers take advantage of the Core ML integration,” Mahmoud Naghshineh, general manager for IBM Partnerships and Alliances explained.

To make it even easier, IBM also announced a cloud console to simplify the connection between the Watson model building process and inserting that model in the application running on the Apple device.

Over time, the app can share data back with Watson and improve the machine learning algorithm running on the edge device in a classic device-cloud partnership. “That’s the beauty of this combination. As you run the application, it’s real time and you don’t need to be connected to Watson, but as you classify different parts [on the device], that data gets collected and when you’re connected to Watson on a lower [bandwidth] interaction basis, you can feed it back to train your machine learning model and make it even better,” Naghshineh said.

The point of the partnership has always been to use data and analytics to build new business processes, by taking existing approaches and reengineering them for a touch screen.

“This adds a level of machine learning to that original goal moving it forward to take advantage of the latest tech. “We are taking this to the next level through machine learning. We are very much on that path and bringing improved accelerated capabilities and providing better insight to [give users] a much greater experience,” Naghshineh said.

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