After several high-profile cases, it’s understandable that governments would want to start regulating artificial intelligence (AI), and biometric technology in particular. The Clearview AI scandal has shown that people are really ‘not OK’ with the knowledge that companies scraped the internet for private images in order to train a facial recognition AI solution they then turned around and sold to law enforcement agencies.

Additionally, a number of cases by civil rights groups have shown that when AI is employed to make decisions about providing credit, rendering a verdict, or simply verifying the identity of a person, minorities are often discriminated against.

At the end of April, the EU adopted a proposal for a regulation called the Artificial Intelligence Act (AIA) designed to regulate AI-based solutions. When these new rules go fully into effect, the EU hopes to become a global trendsetter in AI regulation. The framework of these new laws are similar to the General Data Privacy Regulation (GDPR), which went live in 2018: The legal machinations trigger whenever personal data of an EU citizen is processed anywhere in the world.

New legislation on the horizon

The good news for AI and biometrics companies is that GDPR took two years to move from the proposal stage to the regulation finally adopted by the bloc, so the business world had time to prepare. In its current form, the AIA looks similar to GDPR in what it seeks to accomplish: a means to give end-users a way to control the collection and use of their personal data and digital likeness. In a word: transparency.

AIA argues that the end-user should know, at all times, that they are being judged by AI-powered technology. Is that a chatbot or a live person helping them online? Is their likeness being collected for biometric identification?

Companies that already offer settings to disallow collection of biometric data, or can integrate well with personal data management systems, will find they have the advantage under this emergent new regulatory scrutiny. For biometrics companies in general, the adherence to the final version of these new rules will be required for the correct collection, filtering, and labeling of datasets.

A spate of independent U.S. regulations adds to the complexity

The fragmented nature of the U.S. rules governing the collecting of biometric data has already cost Facebook upward of half a billion dollars, with similar lawsuits against Google, Amazon and Microsoft underway. The absence of clear rules at a federal level leaves it up to the states to decide what AI companies are allowed to collect in terms of personal information without their user’s consent. However, California’s CCPA, Illinois’ PIPA, Massachusetts Data Privacy Act, New York Privacy Act, and the Hawaii Consumer Privacy Protection Act all have the same aim.

For instance, New York’s strict privacy statute has a private right of action for any violation of the law, applicable to all businesses. This means virtually anyone who feels they deserve to pursue legal recourse against a New York business they feel might have violated their rights as described under the state’s privacy statute can do so by simply going down to the civil courthouse, and filing a lawsuit.

Regulation yields… growth?

From an industry standpoint, a common set of regulations governing the use of AI would be a great way to reduce friction when introducing biometrics-based solutions to different markets across large markets like the EU constituent countries and fifty U.S. states. Under one framework, companies can focus on creating solutions that offer the maximum amount of privacy and transparency while solving the kinds of problems that AI exists to solve in the first place.

What we can expect to see in the near future is a flourishing of companies that will provide third party certifications of compliance with the new regulation—from dataset audit to algorithm bias measurements. Some of these services are already standardized via the U.S.-based National Institute of Standards and Technologies (NIST) that, for instance, compares accuracy and speed of facial recognition and fingerprint algorithms, among others. NIST even conducted an extremely thorough comparison of all submitted algorithms regarding their bias against minority groups or ability to recognize faces when wearing protective masks.

Universal regulation also singles out large-scale, facial-recognition-driven surveillance of open spaces as an especially high-risk application of the technology. Due to its “big brother” nature, it is understandable that such an application will be a domain of only a handful of companies, with the rest shying away from such controversy.

There is a growing number of benign applications of biometrics that improve the life of the users without opening their personal data to possible misuse, and that is where the future lies. The COVID-19 pandemic has shown that biometric applications allowed certain industries like financial services and telcos to continue doing business that used to be conducted in-person to confirm proof of identity (opening a bank account, for example) even during the lockdowns. In fact, the technology has proven to be so convenient that even branches adopted digital onboarding instead of their former paper processes. This is where the strength of the technology lies, solving problems in a way that facilitates and maximizes convenience.

Final thoughts

AI is only as intelligent as the data we feed it. If you show a machine learning algorithm 100,000 pictures of a fish, it eventually can draw conclusions about the fish, but a toddler can see one or two pictures of a fish and determine whether or not the following picture is a fish or something else.

However, researchers aren’t always entirely sure as to how AI comes to the decisions that it makes, other than if you feed it biased information, you get biased results. This is why facial recognition has problems with correctly identifying people with darker skin. Datasets of photos used to train facial recognition algorithms contain more images of people with lighter skin than darker skinned people. Resultantly, there’s been an industry push towards explainable AI. ‘XAI’ so you can see what decisions the machine went through to come to its verdict.

At Innovatrics, we found that our AI algorithm is able to identify faces behind face masks, even though it has not been taught to do so. It’s virtually incomprehensible how AI arrives at its decisions because up until now, transparency or explainability haven’t been major outcomes AI engineers have considered when looking for results.

Looking towards the future, as new regulations governing the technology come into effect, explainability and comprehensibility will become the standard. Companies who value transparency and their customer’s privacy will come out ahead in this new era of machine learning.

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Google’s move to eliminate gender labels from its artificial intelligence-powered computer vision services has put pressure on Microsoft, Amazon, and IBM to remove the feature, which automatically assigns the label of “man” or “woman” to people, according to Business Insider India.

Google took the step weeks ago, saying it wanted to bring the service into compliance with its AI Principles, and that “gender cannot be inferred by appearance.”

“Google’s move sends a message that design choices can be changed,” observes MIT Researcher Joy Buolamwini, whose work has been cited as an influence on Microsoft. “With technology it is easy to think some things cannot be changed or are inevitable. This isn’t necessarily true.”

Buolamwini has also been credited with directly influencing Google’s change. She said that she encourages all companies audited by her team to re-examine their demographic markers.

MIT Associate Professor Sasha Costanza-Chock says that all classification tags for people should be used on an opt-in basis, as well as revocable, and noted that some transgender Uber drivers have been locked out of the app because their physical appearance does not match images on file.

IBM and Microsoft did not provide comments to Business Insider, but Amazon noted that its guidelines say Rekognition’s gender predictions do not indicate a person’s gender identity, and should not be used to determine gender, raising obvious questions about the feature’s utility.

Researchers recently found no evidence that gender-balanced data sets, facial expression, occlusion or make-up and other factors suspected of causing inequality in biometric facial recognition matching rates are responsible for demographic discrepancies.

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Artificial intelligence has been a mainstream science-fiction dream ever since the release of “2001: A Space Odyssey.” In 2017, the tech industry has proven that this technology isn’t a fantasy, but a concept that is driving modern innovation. Today, there are four revolutionary changes being made possible by modern AI algorithms.

Natural Language Chatbots

According to research compiled by VentureBeat, most people expect businesses to be available 24 hours a day, 7 days a week. Industries from airlines to pizzerias have begun to offer live agents through online chat, but AI is about to change everything.

Leading the way is a company called Datalog. This up and coming startup has developed a chatbot that can respond to people using natural language, as well as direct users through a specific process. Whether you’re booking your flight or ordering some dinner, your next customer service interaction could be with a robot. And you might not even know it.

Digital Doctors

Medical school is one of the longest and most challenging educational paths in the nation. For good reason, too. Doctors need to be familiar with an almost incomprehensible amount of data. No matter how good they are, mistakes can happen. The sad reality is that medical errors are the third leading cause of death.

Galaxy.AI is a venture harnessing AI technology to address this issue. The system will tap into your medical history, analyze it, and recommend certain treatments or diagnoses that may be hard for a human to identify. The software isn’t going to replace doctors but works with them, allowing experienced professionals to give their input and have the software validate the details.

Biometrics

When we think of biometrics, most of us imagine retina scanning security software. With artificial intelligence, this technology can go much deeper. Security software looks for a present trend to identify the user, but true biometrics software analyzes thousands of different data points. Studies have shown that mood can be inferred when there is a correlation between only 5 data points, proving that this technology is feasible.

Startups like Moment, Affectiva, and Nvisio have already laid the foundation for emotion tracking software, and the technology is expected to be first adopted by advertising networks and television providers to measure response to their content.

Apps That Know You

Google has spent years researching ways to turn data into a positive user experience. The information you see changes depending on your browsing history, your location, and a number of other factors. But their deep learning platform is helping them move away from an algorithm towards an artificial intelligence based system that knows you.

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Looking at the future of marketing and new trends in business, Zachary Harding, local marketing specialist and CEO of Hyperion Equity, has invested in a 10 per cent stake in Blue Dot Data Intelligence Ltd.

Blue Dot is a research and data intelligence company based in Jamaica, which was started a little over two years ago by Larren Peart.

Blue Dot uses biometric technology that tracks eye movement and monitors emotional responses to various stimuli or questions being asked. This ensures that the disconnect between what people say and how they actually feel does not affect the results of the research being done. This way, marketers can understand and target their buyers’ subconscious mind.

“In 2016 elections, locally and internationally, you see where even the most respected research firms got the predictions wrong not because their methods were faulty, but simply because what people say does not necessarily indicate how they feel. Using biometrics, we can know what people are really thinking,” said Peart.

For Harding, the decision to invest in Blue Dot was simple. Having done the appropriate due diligence, Harding deemed that the company’s financials were strong and the growth trajectory positive.

“As a marketer, I always dream about leveraging new technologies to help companies make smarter decisions, both as it relates to their consumers as well as how they do business. The team at Blue Dot was able to show me how easy it is for traditional marketing research firms to get it wrong. It’s not often that I’m surprised, but they were one step ahead of me. The team has the right mindset, and that convinced me that this is something I want to invest in,” said Harding.

New to the marketing world, especially in Jamaica, is the concept of data mining.

“What a lot of people don’t get is the impact that data mining can have on the local economy. As a commodity, data is the new oil, however, many companies either don’t understand the value of their own data or don’t have the expertise to analyse and extract the valuable insights from the data. In some cases, the data they already have can negate the need for them to do market research in the first place. Blue Dot’s emphasis on this area of data mining shows that as a company, they are ready for the future of marketing,” said Harding.

Blue Dot Data Intelligence Ltd has conducted several studies for leading companies in North America, Jamaica, and several other Caribbean islands.

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