What Does the Tech Industry Value?

Most people try to do the right thing most of the time. But “right” is relative, of course. This has been especially evident in the recent generative AI boom, hailed by some as potentially world saving and decried by others as quite literally apocalyptic. As the global tech industry rapidly expands the frontiers of these new technologies, we at HBR pondered several questions: What values guide tech leaders’ decisions? What ideologies, cultural expectations, and mindsets inform their priorities? And what risks do these ethical frameworks carry with respect to how AI will be developed? We asked six experts on the history of the tech industry and the ethics of AI to weigh in on these questions. Their responses shed light on the culture and mentality driving decision-making in the tech world — and what the ethos of today’s leaders can tell us about the opportunities and threats we will all face tomorrow. We’ve organized their edited responses into three sections: one about the industry’s culture of speed; another on why technologists fail to consider the broader context in which their products exist; and the last on the ways in which commonsense guardrails are often an afterthought (and why that needs to change). . . . A Glamorization of Speed Margaret O’Mara is a professor of history and the Scott and Dorothy Bullitt Chair of American History at the University of Washington. “Move fast and break things” has been an animating value of Silicon Valley for generations. This attitude buoyed the development of home computing and video games in the wake of stagflation in the late 1970s; drove venture capital investments in platforms like Netscape, Yahoo, and eBay following the early 1990s recession; and defined the post-dot-com-bubble-burst explosion in search, social media, mobile, and cloud software. Today, generative AI is the next big thing. And even as industry leaders warn of the technology’s potential dangers and urge a six-month pause on training more advanced AI systems, the same age-old Silicon Valley mindset appears to be reemerging. A deeply ingrained need for speed and growth may hinder efforts to put adequate guardrails in place. “I’ve done this for 50 years,” Eric Schmidt, former Google CEO, said on This Week with George Stephanopoulos in early April. “I’ve never seen something happen as fast as this.” Of course, technologists have worried about sentient computers for more than half a century — and about robot overlords for much longer — and current large language models do show that computing power (and available data on which to use that computing power) is coming closer than ever to matching human intelligence. However, in my view, the greatest danger isn’t the technology — it’s the ethos and business imperatives that have for so long defined the people building it. The humans designing AI tools are fundamentally fallible, and that’s resulted in biased algorithms, hackable security systems, and deadly campaigns of disinformation. In the world of tech, speed is nothing new. But generative AI systems are rocketing ahead so quickly and so powerfully that even the most seasoned observers are taken aback. Only time will tell how much we’ll break if we move this fast. . . . Dan Wadhwani is a professor of clinical entrepreneurship at the University of Southern California Marshall School of Business. The rapid development of AI seems to be driven, by and large, by technological fatalism: Fearing they will fall behind the curve, companies are releasing increasingly powerful models with an increasingly blasé attitude toward ensuring these tools are productively integrated into society. While identifying commercially viable use cases for a new technology isn’t a bad idea, an exclusive focus on keeping up with the competition can blind leaders to social repercussions and unintended consequences. For example, social media, which promised to connect the world, has also wreaked all sorts of damage, from fake news to mental health issues to developmental concerns for teenage users. AI has the potential to benefit society in powerful ways, but it also poses great risks — many of which are likely still unknown. Tech companies have tried to anticipate some potential downsides but have largely focused on mitigating narrow, limited risks, such as intellectual property concerns or liability for harmful content created with their models through specific algorithmic rules. Moreover, the pace at which AI operates and the lack of real-time visibility into the processes by which AI outputs are generated pose an entirely new kind of challenge. Once quirky and countercultural, the tech sector has grown into a powerful part of contemporary society, culture, and politics. But many of its leaders still seem to embrace a remarkably, perhaps willfully, naive view of their power. It’s time for them to grow up. Recognize and navigate the unpredictable risks posed by AI — or commit even costlier mistakes. . . . An Obliviousness to the Broader Context Ming-Hui Huang is a distinguished professor of information management at National Taiwan University and the editor in chief of the Journal of Service Research. Among marketers, the concept of identifying target customers and proactively meeting their needs is well established. But in the AI race, many companies have failed to align developers’ goals with those of their target end customers, causing ethical considerations to fall by the wayside. For example, through their competition to develop large language models, both OpenAI and Google have created conversational tools (ChatGPT and Bard, respectively) that do not always generate helpful and harmless content. Indeed, these models often replicate the human biases embedded in the online data sources on which they’ve been trained — and sometimes, because they rely on all available text data on the internet rather than prioritizing accuracy, they offer responses that are misleading or just flat-out wrong. So, what’s the fix? AI developers need to build their tools around the priorities, needs, and contexts of the people interacting with these new tools. If they do, they’ll be compelled to make more-ethical decisions every step of the way and build products that not only are profitable but also create real value for users. . . . Quinn Slobodian is the Marion Butler McLean Professor in the History of Ideas at Wellesley College. What does AI think about its own ethics? I decided to ask it directly — and what it told me was at once revealing and concerning. I began by asking ChatGPT what was driving the AI race. It answered that the search for “efficiency and productivity” and the “desire to create more value for shareholders and stakeholders” were paramount. When pressed further, it added secondary motives such as the pursuit of innovation, national security and competitiveness, and environmental sustainability. I didn’t find this hierarchy surprising. The AI race is, at its core, profit driven. But of course there’s more than one way to make a profit. So next I asked ChatGPT how it thinks AI developers make decisions with respect to social or ethical concerns. It responded that a company may consult experts, monitor impact, develop ethical frameworks, or establish ethics boards or review committees. Its use of the word “may” was striking to me, as was its exclusive focus on self-regulation, devoid of any acknowledgment that a society or state might direct a company’s decision-making. Sure, a company may do any of these things — but if they’re not required to by law, or compelled to by the threat of litigation or reputational damage, will they? Or will they simply defer to the AI’s first directive of maximizing shareholder value? The collective wisdom of a community or democratically legitimated legislation did not seem to exist for my AI conversation partner. Like its creators, ChatGPT cultivates the fantasy of being a brain in a vat, blissfully unaware of its dependence on larger structures — that is, until the time comes that it will inevitably need to be bailed out. . . . A Lack of Guardrails Meredith Broussard is an associate professor at the Arthur L. Carter Journalism Institute of New York University. “We can’t keep regulating AI as if it […] works. Most policy interventions start with the assumption that the technology lives up to its claims of performance, but policymakers & critical scholars need to stop falling for the corporate hype and should scrutinize these claims more.” So warned computer scientist and algorithmic auditing expert Inioluwa Deborah Raji back in 2021. Many tech leaders have come around to the idea that their products should be “fair” or “ethical,” but they still fail to conduct the audits necessary to actually ensure such outcomes. To build tech that does the right thing, we need to know what it does and how it does it. That means leaders must work to identify biases in their products against different groups, whether based on race, gender, ability, or other identities. For example, if your facial recognition tool consistently performs better with people from certain racial groups, you might have an algorithmic bias issue. Developers must test (and retest) their AI systems constantly, and they must be prepared to change the code or even cancel a project entirely when problems are found. Because these biases are often unconscious, proactivity is imperative. Beyond simply reviewing the end product, tech leaders must audit the code, data, and models to be used in AI systems before they are released. The Biden administration has already proposed legislation that would mandate this sort of auditing, and similar regulations are likely to be adopted around the world. But this isn’t about compliance. It’s about doing the work to make sure our technology does what we say it does — and actually achieves the equity outcomes we say we want. . . . Joy Buolamwini is the founder of the Algorithmic Justice League, a former AI bias researcher at MIT, and a poet of code whose artwork is exhibited internationally. The tech industry is motivated by profit, not public interest, and so we cannot continue to expect it to self-regulate. Unceasing competition to be first, to be best, to capture market share will come at a grave cost to society: job displacement; synthetic media that undermine our information ecosystems; AI outputs that reinforce harmful stereotypes and proliferate hateful, toxic content; and so much more. In particular, the data practices underlying the current AI race demonstrate just how sorely ethical guardrails are needed. Companies are building products on a foundation of data that has been collected without affirmative consent and with total disregard for individual privacy. In 2020, Meta agreed to pay a $650 million settlement due to its use of personal data to train its facial recognition system, and ChatGPT has already been banned in Italy due to data privacy concerns. If companies continue to operate in an environment that eschews transparency and accountability, I suspect more settlements, fines, and bans are likely to follow. Of course, until there is comprehensive legislation to govern the development, deployment, and oversight of AI systems around the world, companies that endeavor to use them in a manner that honors human rights will remain disadvantaged. But I am optimistic about both legislative proposals such as the Algorithmic Accountability Act (reintroduced to the U.S. House and Senate in April 2022) and a growing consumer-driven movement to demand proof of consent for data collection, delete systems created without consent, and protect people from AI exploitation. We have an opportunity to stop the normalization of data plundering and shift the trajectory of AI development away from these exploitative practices. We have the power to say no, and we have the ingenuity to build better alternatives. Data is only destiny if we fail to intervene. I will conclude with a human-made poem that captures my thoughts on the current AI race. Unstable Desire Prompted to competition Where be the guardrails now? Threat in sight Will might make right? Hallucinations Taken as prophecy Destabilized On a middling journey To out pace To open chase To claim supremacy To reign indefinitely Haste and Paste Control Altering Deletion Unstable Desire Remains Undefeated The Fate of AI Still Uncompleted Responding with fear Responsible AI, beware Profits do snare People still dare To believe our humanity Is more than neural nets And transformations of Collected muses More than data and errata More than transactional diffusions Are we not transcendent beings Bound in transient forms? Can this power be guided with care? Augmenting delight alongside Economic destitution? Temporary bandaids cannot Hold the wind when the task Ahead is to transform the Atmosphere of innovation. The android dreams entice The nightmare schemes of vice. —Poet of Code, certified human-made

What Does the Tech Industry Value?

Summary.

Partner Center