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disinformation technology

Can AI help mend trust in business damaged by trolling and fakes?

  • min read
    • In 2021, artificial intelligence (AI) might have finally become better than humans at being human. A study found people trust AI-made fake faces more than real people's faces. The researchers warn their findings show widespread use of AI poses a threat to the public. They recommend stronger safeguards restricting code to those with good reason to use it. As deepfakes and manipulated imagery increase, we've seen a counter-balance in businesses wanting to communicate with customers in a more authentic voice. This also means rooting out manipulated sentiment and trolling, so we see governments and brands contracting tech firms to reveal those spreading disinformation on social media. Could we get better at spotting fakes? In a world where four in ten teens can't tell the difference between fake and real news, rebuilding trust must be a business priority. But how AI technology has advanced makes it complex. It's now easy to create many synthetic images and videos using AI systems that analyze thousands of real images to learn how to make fakes. In the past, that workload would've needed many trained people. The FBI recently alerted that HR managers have spotted people using deepfakes to apply for remote jobs. But why? "Fake employment applicants may be hoping to access and hack the hiring company's IT systems," says Kaspersky senior data scientist Dmitry Anikin. "It's happening, but today, it's hard to do well. Making a high-quality deepfake needs much data and computing power, especially for online meetings. We should prepare for the threat to grow, because technology doesn't stand still." Fighting to maintain online credibility All business relies on trust. So how can business operate with the albatross of fakery hovering above? Luckily, companies like Truepic offer solutions. Truepic's software development kit (SDK) integrates with apps that rely on images, verifying media in real time. It checks image metadata – like when, where and on which device it was created – then cryptographically signs it, embedding validation in the file. This lets businesses on the back end know if content is real or synthetic. Truepic's clients include credit reporting company Equifax and car manufacturer Ford. When Ford pays out on a warranty claim, they need to know if car parts or damage photographs are legitimate or have been manipulated or pulled from the web. Brown says tagging tools like Truepic come at a time of brand and consumer frustration with social media companies doing little to stem disinformation. "We're in that sea change now. A consumer push for authenticity is starting to pressure social media platforms to take notice." But firms like Truepic aren't sitting on their hands waiting for Facebook, Twitter and TikTok to act differently. Truepic belongs to Adobe's Content Authenticity Initiative (CAI), a community of tech companies, media, non-government organizations (NGOs) and others promoting an open industry standard for proving content authenticity. This year, Adobe released a three-part open-source toolkit to help get trust-building technology into developers' hands faster. Rooting out social media trolls ZeroFox, Astroscreen and Cyabra use AI to identify disinformation disseminators and 'sock puppets' – fake profiles creating an illusion of wider support for (or opposition to) an idea or company. Cyabra calls itself a "social media search engine designed to track and measure the impact of fake accounts." Companies like Cyabra recognize social media platforms aren't doing this enough. Rafi Mendelsohn, vice president of marketing at Cyabra, says, "We look at the 'breadcrumbs' to find out if an account is real or not." Breadcrumbs might be, for example, a user posting 23 hours a day or multiple users Tweeting the same text. Brands using Cyabra include Warner Media, which faced a deluge of Twitter trolling when they announced the first female-led superhero film, Wonder Woman, in 2017. "We helped Warner recognize authentic enthusiasm for the film and what was false," says Mendelsohn. Cyabra works with Brazilian, Colombian and US state governments to track fake narratives leading up to major elections. Mendelsohn notes, "Human analysts have done this work for years, but we can do it at scale with AI." In the highly publicized battle between Elon Musk and Twitter, Musk contracted Cyabra to find out how many Twitter users were fake before confirming purchase – their analysis found about 11 percent. Towards a more authentic internet Those studying distrust online have found language is important in identifying disinformation. Victoria Rubin, associate professor of information and media studies at University of Western Ontario, has tracked disinformation spreaders for several years and noticed some hallmarks. "Those who lie don't often reference themselves in posts and prefer to use 'they' in language." She says fake news and election lies spread fast when people don't look critically at what they see. It's common – haven't we all reacted to a news item after just reading the headline? Disinformation isn't going anywhere soon, so to attack it requires greater vigilance. Those I interviewed said education and company responsibility must be part of confronting it. They agree we must teach people how bad actors use new technologies to fool us something's real when it's not. Platforms like Twitter and Facebook must also do as much as possible to rebuild trust, says Truepic's Brown. "Our big-picture goal is an internet with built-in authenticity infrastructure, so everyone has the right data to make decisions that affect their lives."
earning customer trust

Earn customer trust with ‘inside out’ business values

  • min read
    • I've particularly noticed two business trends since the pandemic. First, people aren't rushing back to work – whether it's called the Great Resignation or Great Reshuffle, we're thinking hard about where and why we work. Second, we've become more dependent on, and aware of, technology and how it's shaping narratives in society. Tech can earn business gains in efficiency and productivity, but there's no shortcut to earning trust. Fuelled by mistrust in media and government, public trust is declining. There's an opportunity for businesses to lead in regaining public trust, and I believe they should start with their employees. Distrust has become the default The pandemic sparked fears about personal and societal issues. There are existential concerns like climate change and growing worries about personal freedoms and economic security. This anxiety and insecurity make it hard to trust. The Edelman Trust Barometer surveys 36,000 respondents in 28 countries on their relationship with business, government and the media. The 2022 Barometer shows distrust has become the default, especially distrust of government and media. Who people trust has become more local, like co-workers and neighbors, and distrust of outsiders has risen. The business of raising trust Trust has long been weak in media, but the Edelman Trust Barometer showed trust in government has fallen around 25 percent between 2020 and 2022. Trust in business meanwhile stayed at about the same level. People are looking to business to tackle big issues rather than their governments. It's an opportunity for business leaders, but not those who picture nothing more than trust-promoting marketing operations and communications ploys. Cutting through the fake The public sees CEOs and business leaders with a wary eye. Between 2021 and 22, those who said they worry business leaders are intentionally misleading people went up to 63 percent. Similar concerns about journalists (67 percent) and government leaders (66 percent) have risen too. Build trust from the inside out The Edelman Trust Barometer found employees tend to give credence to their CEO. But what about your employees? It's worth doing an anonymous internal survey to find out how much they trust your company. Internally, transparency is an excellent way to earn confidence. Show faith in your employees and give them accurate information about your strategy and intentions. There are, of course, limits to how transparent businesses can be. You can't divulge intellectual property secrets or make big announcements before an earnings report as a publicly-traded company. But the way you operate and communicate internally must be consistent with how you want customers and stakeholders to see you. I call this the 'inside-out' model: What goes on at the core radiates out through the value chain. The core is your executive and employees. Moving out from the core are your ambassadors, distributors and suppliers. The next level is customers. Demonstrating consistency over time – from the inside out – will mean greater trust. Gain trust the old fashioned way: Earning it I have seen these three leadership attitudes matter when it comes to instilling trust throughout your organization and value chain. First, when possible, under-promise and over-deliver – for example, in marketing promises, delivery dates and product performance. Second, have a karmic attitude – giving without expecting anything in return. A transactional approach, based on equal exchange, means no margin for 'extras,' and can rapidly decline into an adversarial relationship. A karmic attitude builds up credit. Marcus Sheridan built a successful swimming pool business by giving away relevant and honest content. Credit is important over the long haul if you need forgiveness for an error. Unexpected things happen, and there are unavoidable, big-picture issues like global supply chains breaking down and wholesale price rises. Third, bring your company's purpose to life and ensure it impacts your behavior, language and decision-making. The most important audience is your employees: Ensure they clearly understand and experience the company purpose and vision – many employees say they don't fully understand theirs. However, avoid overselling your purpose – it's better to understand your limits than to promise something you can't deliver. Strive to live up to what you say. Admit mistakes and be honest about your failings. This will help build trust and engender loyalty. Overcoming the challenges of tech and trust As businesses grow, they need more tech to help manage many customers and workflows. Many organizations are starting to use artificial intelligence (AI) to support customer relationship management (CRM,) customer service and eCommerce. While there are great uses for AI, issues around data privacy, invasive personalization and bias are legion. You need a solid ethical framework to manage these. Using empathy to understand the employee and customer journey better is vital. And to be trusted, especially with AI, you must have a firm grasp on the outcomes you want. For example, when using chatbots to augment customer service, tell your customers what kind of agent they're dealing with (bot or not) and be alert to when a human agent must step in to ensure the best customer experience. There's no shortcut to building trust. It takes a concerted effort across the organization. If you've built a loyal and trusting partnership with your employees and stakeholders, they'll be your best ambassadors and be more forgiving of your mistakes. Be vigilant when trust starts to erode: Trust is hard-earned and quickly lost.
How AI can make therapy better

Could AI make mental health therapy more effective?

  • 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."
adverserial AI

How to keep a business safe from attacks through AI’s machine-learning

  • min read
    • We hear every day of new uses companies find for artificial intelligence (AI,) but security of machine-learning models that underpin AI doesn't get as much attention. When machine learning goes wrong, it's never good for business – like when a text-generation model spews racists slurs or social media comment filters are tricked into displaying toxic comments. But some businesses are taking action – especially those at the top of their game. The cybersecurity community is producing guidelines like MITRE ATLAS to help businesses to secure their machine learning models and tech giants like Microsoft and Meta are assembling expert teams to safeguard the machine learning beneath their business-critical AI. One way Kaspersky protects the machine-learning technologies powering everything from our malware detection to antispam, is we attack our own models. Machine learning is vulnerable because it learns like us Known threats to machine-learning models, called adversarial examples, don't stem from error or misconfiguration – they're part of how the machine-learning model is made. Alexey Antonov, Lead Data Scientist at Kaspersky and expert in machine learning-based malware detection, has a great way to describe these threats. Antonov's team set out to attack their own malware detection model. Rather than being programmed with rules, this model learns to tell malicious from legitimate software by training on troves of malware examples collected over the years. It's like how our minds learn – and like our minds, it can be fooled. "We craft specific data to confuse the algorithm", says Antonov. "For example, you can glue pieces of data to a malicious file until the model stops recognizing it as malicious." Unlike bugs in traditional software, adversarial examples are hard to fix. There's not yet a universal way to protect against them, but you can improve your machine learning's security by adding adversarial examples to training data. Diluting the impact of poisoned data Machine-learning models are so called because they aim to model what happens in the real world. But what they really do, is describe the data used to train them mathematically. It can produce biased results if the training data is biased, for example, a face-recognition model trained predominantly on white faces will struggle to recognize People of Color. If an adversary can modify your training data (for example, if you use openly available datasets,) they will change your models – known as 'data poisoning.' Nikita Benkovich, Head of Technology Research at Kaspersky, says his team realized a model protecting enterprises from spam could be attacked with data poisoning. Models are frequently retrained because spam is always evolving, so an adversary could send spam emails using a legitimate company's technical email headers, perhaps causing the model to stop all customers receiving that company's real emails. "We had many questions," says Benkovich. "Can you actually do it? How many emails would we need? And can we fix it?" After verifying such an attack was possible, they looked at ways to protect the system, coming up with a statistical test that would flag anything suspect. How businesses can prevent adversarial attacks on AI Adversarial attacks can affect fields as broad as object detection to machine translation, but Alexey says real-world adversarial attacks aren't yet happening on a large scale. "These attacks need highly-skilled data scientists and much effort, but they can be part of Advanced Persistent Threats (APTs) for targeted attacks. Also, if a security solution relies solely on machine learning, an adversarial attack can be highly profitable – fooling the algorithm once, the adversary can use the same method to make new strains of malware the algorithm can't detect. That's why we use a multi-layered approach." Benkovich says pay close attention to where your machine-learning training data comes from, or 'data provenance.' "Know where your training sample comes from." Both experts agree the best way to keep your models protected is to test by attacking them yourself before others do. Alexey quotes Chinese military strategist Sun Tzu for the best advice: "If you know the enemy and know yourself, you need not fear the result of a hundred battles."
human and machine teaming

Why AI and human teaming up may mean a better working future

  • min read
    • AI can mimic human thinking abilities like problem-solving and do it faster and more accurately. It's even solved problems thought unsolvable. Google Deepmind's AlphaFold can predict the shape amino acids will fold into, a challenge biologists have wrestled with for the past 50 years. To use the technology to speed up new drug development, Google's parent company Alphabet recently launched Isomorphic Laboratories. While machines outperforming the human brain may seem alarming, businesses are finding when human and machine capabilities are combined, they get the best results. Making up for each other's shortcomings Researchers at Harvard Medical School and Massachusetts Institute of Technology (MIT) say their AI outperforms expert pathologists at identifying cancer in breast x-rays. AI had fewer false positives, meaning greater confidence in the diagnosis. But what's more intriguing is what happened when combining AI and an expert pathologist. False cancer detections fell from 3.5 percent for the expert pathologist and 2.9 percent for the AI to just 0.5 percent when they evaluated the x-ray together. The AI spotted anomalies the expert pathologist didn't and vice versa. Designing workflows for machines and humans to work together could maximize the strengths and minimize the weaknesses of each working alone. But before we rush into re-designing organizations around human-machine teams, let's consider the risks. Unintended negative consequences AI can perpetuate biases. In the US, criminal court judges use a decision support tool, Correctional Offender Management Profiling for Alternative Sanctions (COMPAS,) to help decide whether to bail an accused. COMPAS predicts the risk the accused will offend on bail using 137 factors, such as age, gender and criminal history. It gives a one to 10 score, and if the score is five or above, most judges will remand the accused until trial. Race is not one of the 137 factors, but a ProPublica analysis found the AI model produced race-biased results. COMPAS produced false positives, giving accused people scores of five or more who did not offend on bail, 45 percent of the time if the accused was Black versus 24 percent if they were white. The same bias showed up in predicting low risk of bail offending: Where the accused was white and offended on bail, they were much more likely to have been rated low risk than a Black accused who offended on bail. This bias came from the data used to train the AI model. There was bias in choosing data to use or in the data itself, affected by prejudice in past judge's decisions. Over-reliance on sophisticated algorithms that perpetuate prejudice should concern us. Using AI responsibly Research institutes, governments and corporations have started thinking about what it means to use AI ethically. With some forms of AI, you can see what the algorithm is doing and the data it generates. But it's near impossible for humans to understand what's happening in the billions of numbers crunched by applications that use large neural networks – algorithms mimicking how the human brain works. Experts are developing tools to help extract information from these models, and there are efforts underway to develop more explainable AI models. Meanwhile, organizations are adopting measures to govern their AI capabilities, following Google's lead with their AI 'model cards' showing compliance with ethical approaches to choosing and verifying AI training data and validating the AI model. They've kept sight of a simple truth: An AI system's human operator is responsible for explaining why they used the model in a certain way to support their decisions. AI and human superpowers combined AI's ability to outcompete humans at some tasks is in its data processing scale and speed. It can assess many alternative paths at the same time. Testing many hypotheses can yield successful strategies that would be too expensive, time-consuming or dangerous for humans to experiment with. But machines do not yet have a sense of right or wrong, nor imagination to hypothesize about things that haven't happened. How should we make best use of AI and humans working together while reducing the risk of unintended consequences? Nicky Case's prizewinning essay in the Journal of Design and Science proposes: Consider how humans process information. We take in data from our senses, add structure to create information, then link that information with experience to create knowledge. From knowledge of the situation, we determine what action to take in the form of wisdom. AI can do most of the heavy lifting of processing data and information, so humans can spend more time gathering knowledge and proposing courses of action that might find better outcomes. The relationship between AI and humans is in its infancy. We don't yet know how it may evolve. There are fears machines will replace us in many work contexts, from the production line to radiology. Every job that exists today will be affected – positively and negatively – by AI. Leaders and engineers must understand the impact their decisions about AI will have on society.
Drones and emergency response

How drones with neural networks could transform disaster response

  • min read
    • Climate change and COVID-19 have made conversations on how we respond to emergencies mainstream. We've become more aware of the essential workers who risk their lives to save lives or even just keep things ticking over. We've seen multiple applications of new technology fast tracked in the quest to address never-before-seen challenges. In disaster relief, it's great to see ever-widening applications for drones (also known as unmanned aerial vehicles or UAVs,) with their ability to give a fast and relatively inexpensive overview. For example, during 2020's locust invasion, Kenyan Red Cross used drones to assess impact on agriculture and livestock and the effectiveness of control efforts. In Oregon, California and other fire-ravaged US states, firefighters used drones to map fires and even to drop incendiary fireballs that help stop forest fires. These start small fires, removing fuel an approaching fire needs to spread. Artificial brain takes to the sky At Kaspersky, we've been working on a particular contribution to making it easier for authorities to respond to disasters and emergencies with drones and artificial intelligence (AI.) We're piloting an emergency analytics system that uses a neural network. These are mathematical models or algorithms made up of interconnected nodes (neurons) working a little like a human brain. Drones gathering data and identifying objects Of this technology's many possible applications, the most important are in situations where human life is at risk. Drones collecting data for real-time analysis by neural networks can search for missing people, animals and houses washed away in floods. It can assess damage from aerial images after an incident or provide crowd management information, helping to avoid human crush incidents. We're working with drone manufacturers Albatros and have already installed the Kaspersky Neural Network in two of their drone models. In developing the system, we focused on its capacity to detect small objects in limited visibility. For example, emergency services need to find buildings that survived after an earthquake and distinguish residential from non-residential buildings. In a flood, they need to know the amount of debris in a flooded area. Machine learning technology like a neural network can process images and recognize objects instantly. In one of our tests, the Kaspersky Neural Network processed thousands of high-resolution images of rough terrain on a laptop with limited computing power in a few minutes. Our neural networks self-teaches – each time you use it, results improve. Trained on tens of thousands of images, it can recognize and classify people, houses, cars and more. You can process data onboard a drone or on a server or laptop after the flight. Nikita Kalmykov, CEO of drone makers Albatros says, "The system is initially focused on incident analytics and people search. It can also help in the agricultural, construction and oil industries. It has the flexibility customers need for intelligent aerial surveys." Concerns around drones in emergency relief Using drones that process and categorize information using a neural network comes with challenges. Drone technology researcher Faine Greenwood points out public distrust of drones has slowed their adoption in disaster relief. To help smooth over this turbulence, Humanitarian UAV Network educates people and develops standards for humanitarian drone use. When training a neural network, as with all AI, it's important to consider potential bias in data sets and work to reduce it. For example, can the system identify people of all skin colors, genders and body sizes? Can it identify buildings or plants seen in a wide range of countries? Gathering data ethically and storing it securely is another area of concern. In their best practice guidance, Humanitarian UAV Network advises, "Only collect aerial data that has a clear value for specific humanitarian purposes. Other data could be used in discriminatory or other negative ways, or exploited by third parties to harm local communities." The International Red Cross also issues a handbook for data protection in humanitarian action. One thing is clear: In a world of growing challenges in preserving human life, every new tool is worth having. While AI rapidly transforms every part of our lives, drones using neural networks have an important role in disaster relief. It's no surprise humanitarian organizations and emergency services increasingly use drones in their work. Processing information with neural networks can add another level of efficiency and effectiveness to their important work. I hope we'll see this technology widely adopted, and with it, many lives saved.
by Innovating leaders.
Innovative tech.
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