This difference will apply to public institutions too: this will both offer citizens more efficiently delivered government services and raise privacy concerns.
To drastically simplify things, ML enables users to use computers’ ability to analyze massive amounts of data and identify patterns, trends, or, in the case of consumers and citizens, behavior. It doesn’t take a lot of imagination to figure out where governments could apply this capability; let’s look at the more positive ones first.
| How Can We Use Machine Learning in Weather? | Government agencies collect a lot of data about weather and that data is used to provide middling forecasts, which are especially valuable when applied to storms. Weather systems are notoriously difficult to model due to the massive amounts of data and complex interrelationships involved. If ML were able to make forecasting outcomes more accurate and do so further in advance of storm activity, billions of dollars in storm damage, as well as thousands of human lives could be saved. |
| How Can We Use Machine Learning in Agriculture? | Related to weather, is the need that farmers have for accurate data, increasingly available at a highly localized level, about the factors that affect crop performance, quality, and yields. Machine learning in agriculture could help government agencies develop broader and deeper data sets that researchers would use to develop crops that require fewer pesticides, provide better flavor use fertilizers more efficiently, and help farmers manage their crops on an “ultra-local” basis. |
| How Can We Use Machine Learning for the Environment? | As the country becomes more populated and more developed, the pressure on the environment will increase, as will the concomitant need to both understand and manage it better. Ecosystems (Like the weather described above) are fiendishly complex systems with billions of actors and interactions. ML could help researchers understand these interactions better and give government agencies (The EPA, and California’s Coastal Commission) better tools with which to make decisions. |
| How Can We Use Machine Learning in Healthcare? | Machine learning applications in healthcare are changing the game when it comes to the potential of AI. It is easy to imagine how the Centers For Disease Control and the National Institutes of Health could use ML to mine that vast amount of data collected on diet, health, disease, and drugs to help folks improve their health. Meta-studies - ones where data is collected about studies to assess the findings about a particular topic - are common today; one could see a time when meta-meta-studies become the norm. Those are all pretty much positive examples of how public agencies could use ML to enhance their services, reduce spending, and increase the quality of life for citizens. Now for some examples that raise the issue of privacy and individual liberty, some of which are already taking place. |
| How Can We Use Machine Learning in Surveillance Data? | Vigilant Solutions photographs license plates of cars, geotags them, and sells access to its database (2.2 BN pieces of data so far.) Imagine increasing this dataset 100-fold - say, tracking 95% of the cars in the country with photographs every hour for several years, cross-referencing it against local demographic data, driver records, and consumer data and then applying ML to understand indicators of criminal intent. If police were given access to this analysis and were able to pull it up for anyone they stopped, they would be able to make the presumption of guilt without any actual evidence. And, they would know way more about that individual than most of us would be comfortable with. |
| How Can We Use Machine Learning in Criminal DNA Data? | Most states currently collect DNA samples from convicted felons and this data has been essential in freeing wrongly convicted individuals who would have otherwise spent years in jail or gone to the execution chamber (Good!). However, imagine authorities aggregating this DNA data and using ML to look for correlations between DNA patterns and patterns of criminal behavior. Not too many steps away from running DNA tests on folks and predicting their likelihood to break the law. Or classifying folks by background (i.e. race.) |
So, how the government uses ML hinges on the good: consistency, quality of life, efficiency; and the more thorny issues of privacy, fairness, and bias. As AI systems become more sophisticated, understanding their decision-making processes becomes increasingly challenging. Ensuring transparency and accountability in AI systems is essential to maintaining trust.