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data ethics interview

How data ethicists are helping tech business be better

  • min read
    • Using data responsibly is a hot topic. Tech companies must ensure they don't cause harm, for example, by breaching consumer privacy or reinforcing bias through automated decisions. It's a moral and legal question, but it may be unclear how businesses and governments should approach their data responsibilities despite increased regulation. We still see egregious data use practice. Alice Thwaite is technology ethicist and responsible owner for ethics at OmniGOV, MG OMD, working with the UK government on communications and advertising. We chat about what data ethics means in practice. Gemma: What does it mean to be a data ethicist today? Alice: Ethics is the study of how to live. An ethicist takes a theory of how humans want to be treated and how we should interact with the wider world, and thinks about how an action or technology could contribute to that goal. We use ethical methodologies and knowledge about the consequences of a technology or business model to transform the business into one that prioritizes ethics. Businesses have always operated in the realm of ethics, but usually without a systematic approach. Ethicists co-create frameworks with stakeholders like customers, colleagues and wider society so that an organization's values become critical in business decisions. What does that look like in practice? First, we help organizations understand where they're making ethical decisions and show how they'll need a more systematic process to reach their ethical goals. Second, we educate organizations on ethical methodology and issues. Third, we assess practice to understand what the company wants to prioritize and how current practice meets ethical goals, making suggestions like 'ethical pilots.' Finally, we implement and measure ethical pilots so they can become policy. We don't work in a vacuum – we must be in constant contact with various communities. For example, before establishing diversity, equity and inclusion (DEI) initiatives, a company might think racial equality is important but have no roadmap to get there. With an influence campaign, education, auditing and an implementation program, they may still have a way to go. But they're now measuring ethnicity pay gap and thinking about inclusive work environments for different people. They can keep following the 'influence, educate, assess, implement' process to fruition. Where do organizations start? What tips do you have for those keen to start doing ethics? Hire someone to build an ethical program and link them with a sponsor who can ensure the organization implements their recommendations. They may have a degree in philosophy, international relations, social science or community work experience. Ensure they can learn as much as possible about business ethics and support their attempts to transform the culture. Those in data security or compliance should recognize an ethicist has different processes. The goals may be similar, but the process aligns more with social science and humanities methods. What questions should organizations wanting a data ethicist ask? Are we committed to change? Do we want a world prioritizing human values like democracy, dignity and freedom? Are we prepared for the discomfort that comes with changing procedures? If you're prepared to change, write a job description incorporating things I've mentioned and encourage your team members to apply. Opinions reflect those of the expert. Interview edited for clarity.

Could this voluntary charter fix data ethics in big tech?

  • min read
    • We know some people and businesses will, given the chance, use personal location data in unethical ways. When automotive cybersecurity experts Karamba Security posted a fake vehicle control unit online, they saw 25,000 attempted breaches in three days. Stalkers have used AirTag, Apple's coin-sized device for finding lost keys or other property, to track victims. Regulations like the EU's General Data Protection Regulation (GDPR) define minimum standards of data ethics, but the case for going further is strong. To that end, what can help those tech businesses who want to stay ahead of the data exploitation curve by upholding the highest data ethics standards? Learning to foresee the unforeseen International non-profit the Locus Charter promotes a higher standard of responsibility for professionals and businesses when it comes to location data. They want to avoid unforeseen privacy breaches by working out what can go wrong and pinpoint big problems before they're exploited. They champion public interest by showing businesses how to prevent unintentional harm. The charter is based on ten principles that balance moral behavior with economic benefit, like realizing opportunities and understanding impacts. Location data is one of many frontiers. As veterans of data policy in and out of government, the charter's founders want to be part of all kinds of data discussions. Automation challenging data ethics "The most important focus in data ethics is exploring the power granted by the accumulation of data so everyone can recognize how that power can be used and misused," Ben Hawes, Technology Policy Consultant and co-author of the Locus Charter, told me. He continues, "There's an assumption automated systems and digital technologies are always better. Economic pressures mean companies prefer the promise of greater productivity at all costs, which can be alarming when people are unaware of many potential data misuses, accidental or deliberate." "Some risks in business going digital crop up in all sectors," said Hawes. "We often see 'designer biases,' where developers center their own experiences, leading to alienating people who don't fit the same racial, economic or physical profile. Another issue is automation making back-end processes more complex until they're opaque. There are also sector-specific issues." With the digital world's size and potential, regulating the space is an uphill battle. New ways to exploit data are appearing faster than lawmakers can address them. Is self-regulation enough? Hawes doesn't support a relaxed approach to data ethics. "It's unwise to avoid oversight and critical regulation in the hope self-regulation may be enough, as it rarely is." A 'wild west' model is attractive for short-term gains by freeing emerging markets from red tape, but loose protections don't create conditions for long-term growth or stable markets, especially in an age of social pressure on brands to be ethical. Can whitewashing be cleaned up? With limited regulative authority, a non-profit trade initiative likely isn't eager to bicker with companies they're working with. Organizations like Locus may be better placed, approaching economic ethics from a collaborative angle. Of course, some companies are only interested in the PR boost of appearing ethical by whitewashing their marketing because they don't want to be the only ones following the rules in a competitive arena. They end up paying lip service alone, without taking the issues seriously. Hawes thinks despite this, there's still benefit in working in good faith. "To get ethically reluctant companies to behave well with data, is it better to give them the benefit of the doubt or be critical and accusing? I think, better to have a company aspire to a good standard, even if it's empty promises, because they can at least be held to those standards in some way." Less direct ways of influencing A cooperative strategy may create the best conditions in a self-regulating atmosphere. When ethics groups collaborate with business, they influence thousands of staff members, creating internal pressure for higher standards in the boardroom. It's also worth championing an ethical company for consumers looking to do the right thing with their money. Though strategies like these can't do it all, they have worked, motivating large companies to produce net-carbon zero plans and banks to create green investment options. But data misuse is not the same as pollution. "It's harder to see and analyze compared with other high-profile scandals, so it's hard to translate into a common grievance that can unite people," says Hawes. Will something have to go badly wrong for the world to take it seriously? Hawes considers, "If authorities avoid developing solutions until a problem causes substantial harm, they'll still need about a decade to develop tools that would be effective in this complex market. Governments should be working on this yesterday." In other words, have a fire extinguisher ready when playing with matches, rather than trying to invent one as your house burns down. Reforming data use culture The task of reforming fast-evolving data industries is daunting. Hawes' recommended method involves slowly, steadily establishing ethical practice as the norm. "The UK has the Digital Regulation Cooperation Forum for sharing information and approaches. It lets regulators gain knowledge of the risks data and digitalized business generate, so they can manage them sector by sector. We need a forum like that on an international scale," says Hawes. "Understanding risks like explainability, transparency and bias creates a foundation for building standards that address the problem and brings stability to the data economy." This pressure can also come from outside industry, and often flavors the expectations that filter into business. "More public debate on how we can avoid driving digital inequalities would go a long way," says Hawes. "This is particularly important in social media, which is the frontline of data ethics in many ways." While data privacy issues haven't seen the emphasis Hawes and his peers would like, they're eager to change the culture. Business can choose to be part of the solution and be heralded as ethical data use champions for their efforts.
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.
ethical data AI privacy

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

  • 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.
ai ethics

The mirror we don’t recognize ourselves in: Talking AI with Dr. Beth Singler

  • min read
    • One of many experts appearing in Tomorrow Unlocked's new audio series Fast Forward is Dr. Beth Singler, anthropologist and Junior Research Fellow in artificial intelligence at University of Cambridge. Dr. Singler (@BVLSingler) examines the social, ethical and philosophical implications of artificial intelligence and robotics. She has spoken at Edinburgh Science Festival, London Science Museum and New Scientist Live, and been interviewed by New Scientist, Forbes and the BBC. I interviewed Dr. Singler about AI and the future of work. Ken: In your work, you engage people in conversations about the implications of artificial intelligence (AI) and robotics. What do people think AI is? Beth: For the public, it isn't one thing. People point to examples of AI being implemented, but it has different definitions for people. They draw presumptions from science fiction and media accounts of dangerous AI and scary robots. It's a malleable term – people say 'the algorithm' and mean AI. Many think of AI in the workplace replacing human physical work, but we see AI taking on more knowledge labor and even emotional labor. Ken: What kind of emotional tasks can AI do? Beth: We increasingly see interfaces with AI that give simulated emotional responses. AI assistants do tasks for you but pleasantly and civilly. Call center work is already highly structured and scripted – an AI assistant or chatbot can take over that pleasantry system. How workplaces implement AI will influence how we connect with other humans. Ken: Are we creating a human-machine social world we'll have to learn to interact with? Beth: Yes. We're seeing these human-machine interactions playing out in different places – in the home, workplace, and care settings. We're having to understand that relationship and teach our children to negotiate it. There are discussions on whether children should be polite when using AI assistants. We're coming up with a new social format for interactions with AI. Ken: I thought, of course you should be polite to machines – if only because one day they'll look at everything we've said and done and judge us accordingly. I want to be on the right side of them. Beth: We also see arguments that you should be civil to AI assistants because this is how we should behave to other entities, whether human or non-human – that it reflects our natures. If we aren't civil to machines, it says more about us than their needs. There are many different answers to questions of politeness to AI assistants. Ken: People find conversations with Cleverbot amusing when it asks things like, "Don't you wish you had a body?" or "What is God to you?" They don't consider Cleverbot thinks it's appropriate because a human asked IT those questions. We're looking into a strange, distorting mirror and not recognizing our reflection. Beth: Absolutely. There's a reason the Black Mirror TV series is called Black Mirror – it's a reflective surface for understanding ourselves. AI and machine responses come from data sets, and those involve biases. It's a moment to reflect, for instance, on questions of personhood before we even get to anything like artificial general intelligence (AGI) or superintelligence. Should we be civil? If we say rude or sexist things to a female AI assistant, does that matter? These questions come out again and again. I'm an anthropologist, meaning I study what humans do and think. These big questions are integral to our concept of what AI is. I've seen in my work engaging the public and seeing their sometimes hopeful, sometimes fearful responses that this will be a conversation we'll have for some time yet. Ken: I was at an airport buying a train ticket one afternoon. It was quiet, and the woman behind the counter said, "You should have been here yesterday – the automatic ticket machines had recalibrated and was giving wrong tickets. People adjust. Machines don't." I wondered if this ability to adjust is part of our relationship with machines. Beth: It's interesting how much we adjust to machines. With the airport systems that use facial recognition software, I often have to take off my glasses, change my hair or bob down. We adjust ourselves to be accepted by the system. You see this in how automation is changing the workplace. There are interviews with facial recognition software involved, so we're trying to smile more in a video interview. We're increasingly making changes to fit the machine-based system. Ken: It suggests an element of trust. Where does trust fit in our relationship with machines? Beth: Trust is key. We want to believe software that observes our responses in job interviews is fair and neutral, but we have examples where trust is let down. In the UK in 2020, an algorithm that helped grade student exam papers damaged public trust – it penalized students studying at less high-achieving schools. In my work, I see examples of people trusting too much – they have an image of a superintelligence that doesn't exist yet. Society can only trust technology it understands. Digital literacy – understanding what AI is and isn't – is key to that. Ken: We tend to understand things better as fiction. It's a way to get a grip on the world. But I get the feeling fiction's not a grip anymore, but a stranglehold. Is that fair? Beth: I enjoy science fiction accounts of AI in their many interpretations, fears and hopes. One of the hazards is a strict, negative story used too often. I'm a fan of the Terminator film franchise, but I see how dystopian imagery of robot uprisings shapes people's views of AI. And AI making crucial decisions about our future – whether we get a job or a mortgage, or how we're treated in hospital – may also be overshadowed by Terminator-like stories. Ken: And it stops us noticing when AI does good things, like in medicine and traffic control. The robots are already among us, but they don't usually walk on two legs. They're more likely to be sorting out your airplane ticket. Beth: There's a move toward making robots cuter and replicating child and animal forms to reduce those threatening associations from science fiction. Think of Arnold Schwarzenegger's Terminator versus the therapeutic robot PARO, modeled on a baby harp seal. Ken: Is there an element of trying to make work more fun? Perhaps work becomes more like play if you have an AI assistant who helps with the emotional labor? Beth: Yes. There's a history of trying to gamify the workplace – developing 'third space' options that involve games or places where you can nap. Perhaps how we apply AI is a part of how we make the workplace more enjoyable. If our software chatted back to us, was entertaining and responded to us, it might seem less laborious. Ken: Going back to emotional labor, programs could soften the edges of work relationships, whether online or in an office – I can imagine something like an 'emotional Roomba' (robot vacuum cleaner) allowing for moments of interaction. Beth: We see examples of AI mediating between humans in conversation, like machine learning algorithms suggesting how to respond to emails or warning your tone is too harsh – softening the edges of our interactions at work is a developing space. Ken: After some emails I've had, I see the value in something like that. Beth: I also saw an application for divorced or divorcing couples helping conversations be more amicable for the benefit of any children. A machine learning algorithm warns you things like, perhaps you're being a bit sarcastic. Ken: I'm scared of an algorithm that understands sarcasm. That will be the end of humanity. Beth: There's a wonderful Tom Gauld cartoon about scientists trying to create a sarcastic bot. And the bot says to the scientist, "It's going great. This guy is a real genius." Ken: What thought about AI and the future of work would you most like people to take away? Beth: I'd like people to consider how much we should change our behavior in relation to AI in the workplace. People don't normally interact in purely rational ways. If we curtail that normal human messiness, we're not anthropomorphizing AI but robo-morphizing humans. If we make ourselves smile more to do well in an interview with facial recognition software, we limit ourselves. Although we might see AI as a human simulation, do we become a human simulation in response to AI? Listen to Tomorrow Unlocked's Fast Forward audio series for more expert views
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