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data ethics interview

How data ethicists are helping tech business be better

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    • 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?

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    • 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.
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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.
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