Thoughts from the CEO – The Risks of AI Governance
So, the questions that often plague us are important and deserving of serious consideration. We must ask ourselves:
So, then, if the problems aren’t just with the algorithm, where do the risks lie? Let’s take a closer look…
Do you want to know how we have helped to create better policies, more effective budgets and earlier interventions with Artificial Intelligence?
Sources of Risk
For instance, the demographic bias in the training data poses a major risk. If the data does not accurately represent the population for which it’s built to operate in, results may favor, or discriminate against, a certain gender or race.
A good example of a faulty data source is Microsoft and its experimental AI chatbot, Tay, that was released via Twitter in 2016. The results were not very pretty. The intention was for Tay to mimic the language patterns of a millennial female using Natural Language Understanding (NLU) and adaptive algorithms in an experiment to learn more about “conversational understanding” and AI design.
After just 16 hours, Tay was removed from the internet after her jovial exchange turned into a medley of insults –from sexism to racism. What went so horribly wrong so quickly? Initially-innocent Tay had been “corrupted” by twitter trolls – those malicious people who purposely stir up trouble online by starting arguments and inciting ill will. Knowing that AI is only as “smart” as the data it is fed, these trolls went about teaching Tay all the wrong things.
Despite the media outcry, the reality is that the AI worked. It listened, it learned, and it adapted; its responses were scarily human. The problem was that the data set was influenced by a devious source –internet trolls.
It all boils down to the need to create a solid and reliable data strategy. For us, that means working collaboratively with local experts in relevant industries and use cases that have the relevant knowledge and expertise. For example, teaming up with NTT Data for effective natural disaster relief efforts in Japan.
Unsupervised learning trains the algorithm with data that isn’t classified or labeled –essentially allowing the algorithm to make deductions and to identify the underlying structure or pattern of a data set. In this case, there is no “right” answer –or output– to give it because it’s unknown.
The middle ground between the two is a semi-supervised machine learning system –which combines the two. At Citibeats, we develop custom algorithms for every context. We do this strategically as a safeguard against inherent bias within the algorithm.
In our use of semi-supervised machine learning systems, there is a huge amount of data provided, with most of it being unlabeled and some of it being labeled. The benefits of this type of system are that getting labeled data is time-consuming and labor-intensive, whereas unlabeled data is inexpensive and quick to collect. With many socially-impacting issues, speed is of the essence, so there is a clear advantage to having a partially unsupervised system. But, having a layer of supervision –a human being that is there to monitor the input before the output is executed– helps control for inaccuracy and bias.
This can come about because:
An example is FSD and Kenya, where Citibeats technology was implemented to process hundreds of thousands of customer complaints during 2018, generating regular alerts on the quality of issues being addressed –like fraud, scams and frozen accounts. Due to the effective and straight-forward style of reporting and analysis, FSD confirmed that 85% of those alerts were quality enough to warrant an investigation, decreasing their reaction time by 45 days.
Another example is our collaboration with the government of Navarra, Spain in hate speech detection. Our AI technology and detection system provided the context on the where who (via aggregated profiling) and what has been said against vulnerable communities. The information and insights extracted from this data were used directly to affect education budgets and policies that are helping to mitigate and counteract the effects of this hate speech.