The previous Presidential elections was guided by big data analytics to help predict outcomes, gauge where effort needed to be spent, and organize the campaign workforce. Since then, interest in and usage of advanced analytics and artificial intelligence has surged. With its predictive capabilities AI techniques and predictive algorithms are a natural fit for election strategists. Big data analytics has already been demonstrated with Nate Silver’s FiveThirtyEight blog and others, showing continuing improvements in accuracy. AI brings something new to the field. But what it can do, and its current capabilities in this area are very much in debate.
With so much at stake it would be expected that AI would play a role in election forecasting. Indeed a recent poll a recent prediction based on artificial intelligence from an AI system called MogIA from Sanjiv Rai, founder of Indian healthcare industry AI startup Genic.ai, used 20 million data points from public platforms to predict that Donald Trump would win the election in a landslide. This program has correctly predicted the winners of the last three U.S. presidential elections. Evidence on election day makes that prediction dubious, at best. It could still happen. But it does emphasize the strengths and weaknesses of current AI and cognitive approaches, as well as the directions in which it they likely to develop.
The MogIA prediction was based upon social media interaction. Engagements with tweets and Facebook live videos were used as predictors, among other data. While this has worked in previous elections where social media was less of a factor, it would be clear to American voters that the social media engagements would be a poor predictor in this election. Donald Trump’s campaign was based upon negative tweets; these came so furiously and provoked such enormous response, both positive and negative, that engagement was not necessarily going to lead to a vote. In fact, engagement with Donald Trump’s tweets could well have capsized his electability.
Post-Election Update: Trump won the election, against all expectations and contrary to poll results. Was MogIA right? Possibly. But it also indicates a new social media role in US elections. As this role changes, the model will need to be re-evaluated since social media exist within the context of a much larger and more complex system.
This points to the problem of data selection and interpretation in feeding a predictive system. Artificial intelligence can draw conclusions from data and find patterns, but its ability to define those patterns depends upon how they are described and the relationships that are initially drawn in the prediction model.
MogIA was a sentiment-driven social media analysis approach. Artificial intelligence has also been engaged in this contest in a variety of other ways. TechRepublic teamed with Unanimous A.I. to create “swarm AI” sessions. This is a hybrid strategy using a crowdsourcing approach, where voters used an online platform that employed AI techniques to aid the group in coming to a decision. It used a crowd controlled pointer and focused upon a set of specific issues related to economy and technology to create a final measure. The sample was very small but the result went to Clinton. While, in this case, the results are somewhat irrelevant due to the size of the sample, it does point to the use of a hybrid human-AI approach to prediction which short-circuits some of the problems in an entirely AI-based system. Such an approach adds a ready understanding of issues, which can be used to create an immediate group response with potential for greater possibility of predictive success. Eliminating potential bias in such a system does present difficulties, however.
In another approach, the Washington Post is attempting to improve on election coverage of every race by using AI in a program called Heliograf which the company built in-house. Heliograf permits massive data collection and analysis and is used to collect and report on data that has been missed or has not been extensively covered. Its technique is to use templates to automatically update stories; a technique previously used in the company’s coverage of the Rio Olympics. This again is a hybrid human – AI approach—not to prediction, but as an aid in the understanding of an event by sorting out the signal from the noise. Ability to glean important information and assemble it to the point where unknown or invisible connections might be discerned, or the significance of buried data to current news headlines, creates an opportunity for improving human awareness and understanding.
Another AI approach being developed to understand the hugely complex information relating to elections is Electome, a project of the laboratory of the Laboratory of Social Machines at MIT Media Lab in partnership with media companies such as the Knight Foundation, Twitter, the Washington Post, CNN, Bloomberg and others, provides real time analytics on public opinion related to the election. Electome ingests public opinion focusing upon Twitter. It classifies conversations by issues; segments by demographics, tone and other factors; and provides a deeper understanding of occurrences. Access to Electome is provided to journalists and was also used by the Commission on Presidential Debates to brief moderators.
AI is also being used in this hybrid context by Amazon’s Alexa, the AI used by the company’s Echo smart speaker devices. The election has brought a wide range of new concepts and new information requirements for home users. Questions posed by Alexa’s users–people at home who ask questions of the device–need to be anticipated to create an immediate response. This demands a hybrid approach, with a team of experts screening potential content and developing concepts which can be referred to Alexa. Human fine tuning is essential in these cases, where meaning is critical and understanding needs to be instant.
Finally, the election itself is creating new avenues of thought for AI. The recently unveiled Election Algorithm (EA) is an optimization and search technique inspired by the election. It is a form of genetic algorithm that works with a data population of data clustered as candidates and voters. Candidates for form parties, and advertise. They can confederate to increase success. On election day, the candidate cluster to achieve the most votes is announced as the winner. The new algorithm competes successfully with the other genetic algorithms in making certain kinds of prediction.
Presidential elections are entering a new type of predictive era that involves deep analysis of social media, external news sources and strategy. This combination reveals some issues with AI techniques of today, and the need to incorporate a human crowdsource capability to ensure an accurate result in many real-world situations. How such interactions are supported will be the subject of ongoing research.
It is also clear that as with the very earliest computer systems “Garbage In Garbage Out (GIGO)” still applies. But, the definition of what constitutes garbage is increasingly complex. In many cases, bad data of today is likely to be data that has not been sufficiently explored and understood. Complex data will require independent analysis before being subjected to an analytic process. The ETL of yesterday’s fixed databases will need to be extended with an analytic approach—which is already happening on one level, with Natural Language Processing (NLP).