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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.
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