AI has been facing a PR problem. Too often AI has introduced itself as a misogynist, racist and sinister robot. Remember the Microsoft Twitter chatbot named Tay, who was learning to mimic online conversations, but then started to blur out the most offensive tweets?
Think of tech companies creating elaborate AI hiring tools, only to realise the technology was learning in the male-dominated industry to favour resumes of men over women. As much as this seems to be a facepalm situation, this happens a lot and seems not so easy to solve in an imperfect world, where even the most intelligent people have biases.
“Data scientists are capable of creating any sort of powerful AI weapons”,Romeo Kienzler, head of IoT at IBM and frequent speaker at Data Natives.
“And no, I’m not talking about autonomous drones shooting innocent people. I’m talking about things like credit risk score assessment system not giving a young family a loan for building a home because of their skin color.”
These ethical questions rang alarm bells at government institutions. The UK government set up a Centre for Data Ethics and Innovation and last month the Algorithmic Accountability act was proposed in Washington. The European Union created an expert group on artificial intelligence last year, to establish an Ethics guidelines for Trustworthy Artificial Intelligence.
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IBM had a role in creating these guidelines, which are crucial according to Matthias Biniok, lead Watson Architect DACH at IBM, who designed CIMON, the smiling robot assisting astronauts in space. “Only by embedding ethical principles into AI applications and processes can we build systems that people can trust,” he tells.
“A study by IBM’s Institute of Business Value found that 82% of enterprises are now at least considering AI adoption, but 55% have security and privacy concerns about the use of data.”Matthias Biniok, lead Watson Architect DACH at IBM
AI can tilt us to the next level – but only if we tilt it first.
“Artificial intelligence is a great trigger to discuss the bias that we have as humans, but also to analyse the bias that was already inducted into machines,” Biniok tells. “Loans are a good example: today it is often not clear for a customer why a bank loan is granted or not -even the bank employee might not know why an existing system recommended against granting a loan.”
It is essential for the future of AI to open up the black boxes and get insight into the models.
“The issue of transparency in AI occurs because of the fact that even if a model has great accuracy, it does not guarantee that it will continue to work well in production”Thomas Schaeck, IBM’s Data and AI distinguished engineer, a trusted portal architect and leader in portal integration standards.
An explainable AI model should give insight into the features on which decision making is based, to be able to address the problem.
IBM research, therefore, proposed AI factsheets, to better document how an AI system was created, tested, trained, deployed and evaluated. This should be audited throughout their lifecycle. It would also include suggestions on how a system should be operated and used. “Standardizing and publishing this information is key to building trust in AI,” says Schaeck.
Schaeck advises business owners to take a holistic view of the data science and machine learning life cycle if they are looking to invest in AI. Choose your platform wisely, is his advice. One that allows teams to gain insights and take a significant amount of models into tightly controlled, scalable production environments. “A platform, in which model outputs and inputs are recorded and can be continuously monitored and analysed for aspects like performance, fairness, etc,” he tells.
IBM’s Fairness 360 toolkit, Watson Studio, Watson Machine Learning and Watson Open Scale can help you out with this. The open-source Fairness 360 toolkit can be applied to every AI model before it goes into production. The toolkit has all the state of the art bias detection and mitigation algorithms. Watson Studio allows teams to visualize and understand data and create, train and evaluate models. In Watson Machine Learning, these models can be managed, recorded and analyzed. And as it is essential to keep on monitoring AI during its lifecycle, IBM Open Scale connects to Watson Machine Learning and the resulting input and output log data, in order to continuously monitor and analyze in-production models.
Yes, it can all be frightening. As a business owner, you don’t want to end up wasting a lot of time and resources creating a Frankenstein AI.
But it is good to keep in mind that just as our human biases are responsible for creating unfair AI, we also have the power to create AI which mitigates, or even transcends human biases. After all, tech is what we make of it.
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