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Aishwarya Jagani

Aishwarya Jagani is a freelance tech writer based in India. She writes on cybersecurity, science and the human impact of technology.

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personalisation customer data

How to be clever – not creepy – with customer data

  • Data and privacy
  • min read
    • Businesses are collecting ever-increasing customer data and marketers targeting consumers with more personalized ads. But when does hyper-targeted marketing cross the line from clever into creepy? In 2012, an angry customer approached a manager of a Target store in Minneapolis, US, demanding to know why his teenage daughter was receiving coupons for baby clothes and cribs. The manager, who had no idea why, apologized. Days later, the customer called to apologize, having found out his teenage daughter was, in fact, pregnant. Target's customer-tracking technology showed people ads based on what they bought. It had identified that purchasing vitamins, cotton balls and fragrance-free skincare products suggested future purchases of a crib and baby clothes, or in other words, that the customer was expecting a baby. Businesses can collect huge amounts of data about customers, and when combined with shopping or browsing patterns, it can tell marketers much about someone, sometimes more than we know ourselves. They can correlate data from different sources into a 'buyer persona' that may be surprisingly accurate. But ads based on this deep data collection and correlation can come across as creepy, leaving consumers wondering how brands know so much about them. Tracking people across the internet "Most websites and emails contain some form of tracking," Karen Gullo of EFF (Electronic Frontier Foundation) told me. "Cookies let advertisers follow your web searching. Tracking pixels in emails tell the sender if you opened the message. Tracking links let websites know what you clicked on. If you've ever filled out an online form, the data may have been collected even if you didn't submit the form." But with great data comes great responsibility. 'The more targeted the ad, the higher the response' has been a standard in advertising for decades. But as ads grow more focused, they begin to cross lines. Marketers must think about how their campaigns might come across to customers. What makes ads creepy? Research shows that personalization tends to increase response, but it can also have the opposite effect. When personalization and targeting are 'too much,' it puts people off by seeming intrusive. CEO of Email Optimization Shop Jeanne Jennings told me, "I got an email from a department store where I shop, with my first and maiden name in the subject line." She had not used her maiden name for 20 years and could not figure out how they knew it. She eventually worked out they got her maiden name through her social security card, which she had shared when setting up her debit card. While perceptions vary between customers and age groups, people tend to base judgments of creepiness on four things. 1.   Consent: Was the data collected voluntarily? Using data voluntarily provided by the customer means great targeting, but using third-party data or data obtained without consent can be an invasion of privacy. "You want the customer to be pleased the content is relevant. You don't want them to wonder how you got that information," said Jennings. 2.   Explainability: How does a brand know this about me? "Some of the best recommendations… suggest why you are seeing that recommendation," said Emad Hassan, former Head of Data Analytics at Facebook and Paypal, on CMSWire. Using targeted data in a way that makes it easy for the customer to know why you have the data reduces intrusiveness. For example, a donut shop might say, "From your purchase data, we know you're a huge fan of our donuts, so here's a special discount for being one of our top customers." 3.   Third-party data: When information given to one company shows up in an ad for another Using demographic or preference data from a third-party source to target consumers often comes across as intrusive. Someone buying books for a niece or nephew from a children's book shop may not appreciate a children's clothing brand urging them to get "their" children new clothes for school. There's presumptuousness alongside exchange of personal data between brands. "If you learned that a friend had revealed something about you to another friend, you'd probably be upset, even if you have no problem with both parties knowing the information," wrote Harvard Business Review. 4.   Security and privacy: Will this brand keep my data safe? Consumers want to know they can trust brands to store and process their data ethically and securely against hacks and breaches. Poor data storage or weak privacy policies can turn consumers off. Seeing personal information pop up in an ad or communication from another brand can be distressing and feels like a breach in trust between consumer and brand. The same principles that apply to social interactions apply here – don't use information gained without consent or from a third party, keep customers' data secure and avoid using sensitive information like sexual orientation, health information or financial status. "It's about privacy, safety, and security, and being able to control your data and internet experience," said Gullo. Future advertising: Hyper-targeted… or not targeted at all? Third-party cookies are put on a website by someone other than the website's owner and collect data for that external entity. Many search engines have already, or plan to, phase these out soon. The third-party cookie ban means marketers must rely more on data from their own company's customer interactions. This may mean more transparent, direct and explainable ads that improve trust between the consumer and the brand. Jennings thinks marketing's future should be more responsible and smarter personalization. "As the industry is more responsible with how they use data, personalization is going to stay around but be better," she said. Advertising must respond to evolving consumer attitudes to privacy, but data-based targeting is likely here to stay. How companies obtain and use data will change, focusing more on consent and privacy. Make sure your business gets ahead and leaves the (sometimes creepy) old ways behind forever.
blockchain disinformation

How one high-tech solution is helping fight fake news

  • Emerging tech
  • min read
    • As ways to create and disseminate disinformation get more sophisticated and widely available, it's getting harder to tell fact and fiction. But the next version of the internet, Web3, might offer a 'force field' that could protect citizens from this troubling phenomenon. Start-ups and established media companies are using blockchain as part of solutions that aim to verify information and filter out false news. Is this what the world needs now, or could it open the door to even more disinformation? Fakes harder to tell from reality "There's huge money and huge power in fake news. We've seen it here in the US and around the world," says Jim Nasr, CEO of blockchain developer Acoer. Acoer created blockchain-based news platform NewsHash. Disinformation can have far-reaching and sometimes dangerous impacts. We saw this over the pandemic years especially, with rampant covid-related medical disinformation. Tommaso Canetta, vice director of Italian fact-checking agency Pagella Politica, says how false news spreads is "ever-changing. It can [come from] and go in every direction – from traditional to online media to politicians, or the other way around." A 2020 Reuters report found over 56 percent of internet users are worried if they can tell fake from real online. And 'deepfakes' – highly realistic looking audio, video and photos created using AI – further muddy the waters. A technological solution for filtering out fakes? Blockchain could create a 'built-in truth index' for the internet, effectively labeling and verifying each piece of information, creating a sort of watermark for authenticity. Used the right way, this could help people be sure news and media they receive are real. But it's complicated. Using blockchain in journalism might solve some problems but create others. For example, it may trigger more sophisticated disinformation, poor transparency or a single company like Meta (formerly Facebook) dominating the industry. What is blockchain? Blockchain stores information as blocks added in chronological order to a virtual 'chain,' stored securely and permanently. Once added to the chain, blocks are hard to change. Well-known blockchain platforms like Bitcoin and Ethereum use it to store financial transaction information, but the technology can also store and distribute news. Blockchain-based news platforms provide a secure and decentralized way to store verified information, which cannot be changed – like putting a digital watermark on verified news stories. Initiatives like the now-defunct Civil for journalism, Steemit for social media and Truepic for photo and video let publishers permanently link verified information to content in a blockchain. New York Times is also developing a blockchain-news platform, News Provenance Project. Fighting a powerful form of disinformation, miscaptioning – real photos or videos described in misleading ways – the project uses blockchain to permanently attach context (like who shot the film or image, when and where) to media. Acoer's NewsHash works by giving an article an encrypted hash (a unique string of characters) containing metadata added by the publisher, like the author's name and a publication identification number. "If the article changes in any way – even the most minuscule change – we can tell," says CEO Nasr. "And all in real-time." Readers can scan a QR code, which consults the blockchain to check whether an article or video is real. Nasr's company also assigns 'confidence scores,' rating information's authenticity. But while blockchain-based platforms can help protect against doctored images or false news pretending to be from a reputed source, they may also create new problems. But what's further up the chain? Technologies chasing a disinformation-free internet can backfire by encouraging more sophisticated disinformation or offering different versions of 'the truth.' Nasr says there are blind spots when publishing a completed news article on the blockchain because there are many places data can come from. The immutable nature of blockchain also means it may become harder to correct inadvertent errors in published material, making journalism as a whole seem like a less trustworthy source. Another issue is the potential for centralization – a single platform or business may start to dominate the news. "It's easy for an AI algorithm or company to become a centralized location for everything. It's like the internet – the idea is great, but we now have four companies [Meta, Amazon, Apple and Google] dominating it," says Nasr. "That's why we use a public ledger, instead of specific tools or proprietary algorithms, to [let readers] interrogate how we're getting there." The first and second lines of defense aren't technology With advances in AI, video and image editing software, tools for spreading disinformation will improve. Deepfake videos have already fooled internet users many times. "With the progress of fact-checking and technological tools to fight disinformation, we'll also see more complex disinformation," says Canetta. There's also a paradox in journalism requiring trust, while blockchain seeks to eliminate the need for trust. "Journalism is built around gatekeeping. You need someone to approve a story before it goes online – a hierarchy or system in place to ensure quality," says Walid Al-Saqaf, former journalist and senior lecturer in media technology and journalism at Södertörn University, Sweden. "But the notion of Bitcoin and the original blockchain was that you don't need trust." Canetta believes awareness and media literacy are the first and second lines of defense in fighting disinformation. He feels blockchain journalism isn't a silver bullet because "misinformation is not just an online phenomenon. We must teach school children that while they have access to unlimited information, not all of it is true or accurate." Using blockchain to secure other information Decentralized platforms like blockchain have many applications, like more secure communication between organizations, facilitating payroll and making auditing easier. Start-ups are working on blockchain systems that authenticate supply chains, enable digital voting, create and implement smart contracts and more. Within companies, blockchain can help secure data and make sure digital communications aren't altered. But it's not failsafe. For example, an unauthorized user can access a blockchain with a stolen password. And although it would require significant resources, it's possible to take over a whole blockchain and force a change. As blockchain evolves, so will ways to hack into it. Keeping systems updated and secure, and following security best practice will remain crucial to safer business.
How AI can make therapy better

Could AI make mental health therapy more effective?

  • Tech for good
  • min read
    • Artificial intelligence (AI) can analyze vast amounts of data more quickly than humans and draw accurate conclusions, making for myriad business applications. But now, some technologists and mental healthcare clinics are using artificial intelligence (AI) to inform mental health therapy. They think it could help pinpoint the most effective strategies to help sufferers get better. This may mean better staff retention and productivity and greater value for money from employee assistance programs for business. UK-based mental health clinic Ieso and US-based mental health technology firm Lyssn are among those experimenting with AI-augmented therapy. Why mental health is harder to treat I spoke with Valentin Tablan, Chief AI Officer at Ieso. He says while physical healthcare has MRIs, CAT scanners and hundreds of blood tests to diagnose conditions, "In mental healthcare, we've been slower to adopt technology. But that's changing. We can start using artificial intelligence and natural language processing techniques to understand therapy-specific language and gain insights from that." Ieso uses AI to analyze transcripts of patient-therapist conversations to identify which elements of therapy give the best outcomes, identify cases that may need a more experienced therapist and help evaluate the quality of care. They're also working on a cognitive behavioral therapy (CBT) companion app that supports patients in between therapy sessions. Lyssn also uses AI to evaluate the quality of psychotherapy and give therapists actionable feedback. Practitioners hope to increase recovery rates and quality of care to bring it on par with physical healthcare's higher recovery rates. Ieso has seen recovery rates increase 2 percent alongside its AI program – small but encouraging. AI no replacement for therapists "Psychotherapy's quality is usually measured by experts observing a conversation. Our AI is trained to replicate this human judgment, reducing the cost and time to evaluate the conversation," says Zac Imel, chief science officer and co-founder of Lyssn. More accessible, AI-powered forms of therapy include chatbots. These can help support patients between sessions, collect useful data and provide emotional support to people who cannot access therapy because of stigma, time or financial reasons. Chatbots simulate a conversation between humans, using AI to learn from examples of real conversations. The AI in this case is often trained on therapy session transcripts. It replicates how a therapist would respond to a patient with some accuracy. UK's National Health Service (NHS) says up to 8 million people in England want to access mental health treatment but do not qualify for it. High costs, long waiting lists and not wanting to unburden themselves to a stranger also keep people away from therapy. "One barrier to providing care to more people is that CBT, the most common evidence-based treatment for depression, is hard to teach. Having tools to speed up the training would help," Dr. Paola Pedrelli, Assistant Professor of Psychology at Harvard Medical School, told me. Additionally, supervisors usually evaluate and provide feedback to therapists in training, but there isn't always the time or resources. AI could bridge that gap. Pedrelli is part of a team that worked on a project using machine-learning algorithms to diagnose and monitor major depressive disorder. "Sometimes treatment takes months to work. Tools to speed the process would be beneficial," she added. AI's advantage over the human brain AI's biggest advantage over the human brain is being fast and reliable at analyzing data, said Imel of Lyssn. AI can quickly analyze, process and gain insight from large data sets, making it a powerful tool in mental healthcare where markers of recovery are vague. "With repetitive tasks, humans get tired and can drift over time. And if you show a human the same conversation twice, they might evaluate it differently. With AI – same data in, same answer out," Imel says. How AI-powered mental health can help employees AI-based therapy tools like Ieso's CBT companion app, Ellie, Woebot designed by Institute for Creative Technologies (ICT) at the University of Southern California, The Trevor Project's Crisis Contact Simulator and others cannot replace human therapists but help improve access and reduce cost. In a randomized, controlled trial of 70 participants, Woebot users displayed a significant decrease in depression symptoms compared with the control group. Both groups showed a decrease in anxiety. Those experiencing mental distress or stress at work could be directed to therapy bots as a bridge between needing therapy and getting access to it. Chatbots can also gather data about how a patient feels and suggest clinical intervention where required. Organizations considering employee access to therapy bots should consult an expert and make sure therapy is available alongside these solutions rather than as a substitute. Privacy and security in AI-powered therapy Alongside benefits, AI-powered therapy programs come with obvious cybersecurity and privacy risks. Sensitive and private conversations between patients and therapists could be exposed or manipulated. Iron-clad security is the first step to protecting patient privacy. Tablan and Imel shared the importance of data security to their companies and how they keep patient data safe, such as using multiple layers of security and robust security certifications. "Most people working at Ieso can't access this data. It's accessible to computers, but few humans," said Tablan. Even with the strongest security practices, risks to patient privacy still exist, as a lot depends on humans who can access the data. But those involved believe the potential benefits of AI-powered therapy outweigh the risks. AI-augmented, not AI-based therapy There's much to suggest unconscious bias is present in the therapy room as in other parts of life. Black and minority ethnic people are less likely to find counseling fit for purpose. Lesbian, gay and bisexual people also report greater dissatisfaction with counseling and psychotherapy than their heterosexual counterparts. When AI programs can imbibe biases in data used to train them, there must be concerns that AI-based outcomes could further marginalize some patients. Dr. Tonya Davis, faculty member at Northwestern University, Illinois, suggests "unconscious biases – things hidden just beneath the surface of our awareness" might impact training AI chatbots. "I think that if there is a deliberate effort to identify, understand and resolve biases beneath the surface of our awareness, we could be one step closer to addressing the cultural challenges in person-to-person interactions, thereby minimizing these challenges in the development of AI." Pedrelli said, "There have been cases where AI has provided inappropriate directions in the context of therapy. And AI is still unable to detect subtle emotional changes." However, AI tools only augment diagnosis and therapy. They shouldn't be relied on for unilateral decisions on patient care. "The tools we've built look over our quality of care – they never make any decisions automatically," said Tablan. "They just present information to a qualified clinician who has access to the transcripts and make up their own minds." Imel doesn't describe what Lyssn does as "AI-based therapy." Instead, he says, "This is about using technology to augment the human talent of therapists. So, it's human-based therapy, augmented by AI."
ethical data AI privacy

When AI goes low, go high: How ethical AI fights unethical AI

  • Data and privacy
  • min read
    • "This is the world now. Logged on, plugged in, all the time,'' said Jason Clarke in 2015 movie Terminator: Genisys set in 2029. He might as well have been talking about today. Devices have become so integral that today's world wouldn't be possible without artificial intelligence (AI.) AI powers everything from smart homes to customer service to airport security. In Terminator, sentient artificial neural network Skynet sends robots back in time from 2029 to kill the humans who will, in the future, lead the resistance against it. Technology today might be quickly catching up to some aspects of Terminator's fictional 2029, but AI experts say we're a long way from evil, self-aware humanoid robots taking over the world. AI coming close to human intelligence is such a long way off that CEO of Allen Institute for Artificial Intelligence Oren Etzioni says it's "like being worried about overpopulation on Mars before we even have gotten a person on Mars." But there's more to AI than what science fiction shows us. Surveillance and cybercrime applications of AI are concerning, but AI can also strengthen systems and improve data privacy. Like in Terminator, real-life AI can be ethical or unethical. How can AI be ethical? As well as keeping databases and systems safe, ethical AI can influence human behavior, 'nudging' people to make better decisions. Geoff Webb, vice president for solutions, product and marketing strategy at AI-enabled payroll firm isolved, says, "We should expect to see greater use of AI not only to protect privacy, but to enhance ethical behavior. A good example is the use of AI to evaluate things like diversity and inclusion within a business. We'll see AI moving from protecting data to helping us see what that data means." "Businesses are looking to implement machine learning which gives them a smart, efficient and effective way of querying all of the data they hold about people and chop that data up in various ways that can be useful to them. But we need to recognize criminals can also make use of this," says David Emm, Principal Security Researcher at Kaspersky in audio series Fast Forward by Tomorrow Unlocked. Anonymizing and analyzing data more privately Companies collect mountains of data going about their business, and each of us generates data when we check a message from a friend, browse the internet or use an app to check the weather. Some of that data will be sensitive and confidential. To analyze that data, someone's had to look at it. But AI might be able to fix that. AI models can scrub data of identifying characteristics and draw conclusions from large data sets. Francesco Di Cerbo, AI privacy research lead at SAP Security Research, said, "It's like looking at blurred shadows of people. We can still see how many there are, their heights and postures, but not their faces. We can extract information like average and distribution of heights, but it will be hard to identify them. For example, an AI technique like Named Entity Recognition (NER) can detect a date in a sentence, but it might be an invoice date or birth date. Our models must know if that date is personal information. Once different pieces of information are detected in a sentence, we proceed to anonymization." Humans make mistakes. AI (mostly) doesn't. The weakest points of a database are always those where humans are involved. A Kaspersky survey found 46 percent of security incidents that led to business data being exposed were linked to employee error. AI could mean human operators don't need to see sensitive data, giving the processing and sharing of data stronger privacy and security. "AI tools can operate on the data without exposing it to humans. Analysis within platform means reduced risk of someone needing to copy it or print it, or accidentally exposing it," says Webb. "If the platform is secure, granting an AI tool access is often less risky than a human pawing through the data." AI can't be bribed into revealing healthcare data or details of a celebrity's divorce proceedings, for instance. It doesn't leave sensitive data sitting around on a desk or unsecured device. Ethical AI in cybersecurity In 1999, a 15-year-old hacked into NASA with the US Department of Defense, downloading software worth 1.7 million US dollars and shutting systems down for three weeks. Over two decades later, security systems are more sophisticated and harder to break into, but cybercriminals are smarter too. The days of teenagers hacking into government databases are gone – cyberattacks today are dynamic and customized. Traditional security can fail against targeted attacks, but AI can strengthen fortifications and respond more effectively to phishing attacks or breaches. Di Cerbo says, "Automatic defense approaches using AI are highly effective." AI is especially effective in password protection and user authentication. Defensive AI can rapidly detect and contain any emerging cyberthreats, and it's the best way to fight back when AI is part of the attack method. According to Capgemini's report AI in Cybersecurity, hackers use AI for more sophisticated attacks, including spear phishing Tweets – personalized Twitter messages sent to targeted users tricking them into sharing sensitive information. AI can send Tweets six times faster than a human, with twice the success. But while AI can be used for foul play, it also deters these attacks. "Alongside traditional methods, AI is a powerful tool for protecting against cybersecurity attacks," says Capgemini. AI can't do everything, but its potential is great AI is far from an alternative to all human oversight. AI programs have famously replicated human biases – from facial recognition systems not recognizing People of Color to AI recruitment tools not wanting to hire women. But well-designed AI can create a more secure online world, keep data safe and nudge humans towards more ethical behavior. "Security, privacy and compliance will remain business challenges because the counter-pressures are always changing – hackers develop new tools, new privacy expectations and legislation evolve," says Webb. "While businesses could try to manage these risks without AI, it would be complex and expensive. And the more humans involved in processes, the more likely human error or malicious activity will result in a breach," Webb continues. "AI tools can't do everything, but they can deliver better insight more quickly and with less risk of expensive and ugly data exposure." US technology historian Melvin Kranzberg's first law of technology famously says, "Technology is neither good nor bad, nor is it neutral." There may be no greater example of a technology more often called good, bad and non-neutral than AI, and perhaps fairly. So AI is what tech has always been. As in Terminator: Genysis, when good AI robots battle bad AI robots to save humans, today we see AI on both sides of the ethical fence. Using this power wisely isn't a question of if we use it, but how.
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