The future is here. With the exploding commercial market for high-powered, cloud-computing AI services provided by the likes of Amazon, Microsoft, and Google, the reach of artificial intelligence technologies is virtually unlimited. What does this mean for humans? How will we adapt to a world in which we increasingly find ourselves in economic, creative, and cognitive competition with machines? Will we embrace these new technologies with the same fervor as we embraced televisions and smartphones? Will we trust them? Should we trust them

Vallor, Shannon

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Santa Clara UniversityScholar CommonsPhilosophy College of Arts & SciencesSummer 2017Artificial Intelligence and Public TrustShannon VallorSanta Clara University, svallor@scu.eduFollow this and additional works at: https://scholarcommons.scu.edu/phiPart of the Philosophy CommonsThis Article is brought to you for free and open access by the College of Arts & Sciences at Scholar Commons. It has been accepted for inclusion inPhilosophy by an authorized administrator of Scholar Commons. For more information, please contact rscroggin@scu.edu.Recommended CitationVallor, S. (2017). Artificial Intelligence and Public Trust. Santa Clara Magazine, 58(2), 42–45.                                                                                          Artificial Intelligence and Public Trust A future with artificial intelligence is no longer a sci-fi fantasy. But how do we ensure that it is shaped with moral intelligence? W O R D S  B Y  S H A N N O N  V A L LO R I L LU S T R AT I O N S  B Y  J O S H  C O C H R A N   The future is here. With the exploding commercialmarket for high-powered, cloud-computing AI servicesprovided by the likes of Amazon, Microsoft, and Google,the reach of artificial intelligence technologies is virtuallyunlimited. What does this mean for humans? How will weadapt to a world in which we increasingly find ourselvesin economic, creative, and cognitive competition with ma­chines? Will we embrace these new technologies with thesame fervor as we embraced televisions and smartphones?Will we trust them? Should we trust them? Popular essays and news articles about an AI-drivenfuture often highlight grim warnings of science and tech­nology luminaries like Elon Musk and Stephen Hawking,who raise the specter of the emergence of “superintelligent”machines that could threaten human survival or assumecontrol of our future. Yet most AI researchers regard thisprospect as highly unlikely, for it presupposes the emer­gence of artificial general intelligence (AGI)—the kind offlexible, self-aware, and fairly comprehensive understand­ing of the world that humans enjoy. The AI that we havetoday (and will be seeing a lot more of) is of an entirelydifferent kind, one that fundamentally lacks the capaci­ties needed for AGI. For the foreseeable future, humanswill navigate a world populated by artificial agents thatpossess no general understanding of the world—or of us,or of themselves, or much of anything at all, really. Whatthey will have is exceptional skill and speed at perform­ing specific, well-defined tasks that used to require humanintelligence. This kind of AI, powered by large datasetscombined with advances in machine learning techniques,doesn’t recreate or even imitate our kind of smarts at all.It bypasses it—and does smart things without it. Althoughthis kind of AI may seem far less scary than a self-awareSkynet that decides to wipe out human pests, the risks ofthis more mundane species of AI are nearly as profound. One obvious risk: a new wave of AI-driven technologicalunemployment. Although economists’ predictions vary, anoft-cited 2013 study from the Oxford Martin School esti­mates that as many as 47 percent of American jobs couldbe at risk from AI-driven automation within a few decades.Even if artificial agents cannot wholly replace most humanworkers in the short term, the emergence of task-specificartificial intelligence across a broad range of new industriesand social contexts is already rapidly transforming everydomain of human activity, from commerce and transporta­tion to education and medicine. Every system that makes,Lack oftransparencyin some “deeplearning”algorithmsmeans thateven systemprogrammerslack a clearview of howor why the sys­tem reachedits conclusion. sells, or distributes goods and services to human beings hasthe opportunity to benefit—and to be radically destabilizedby—the new wave of machine automation and decisionsupport that task-specific AI makes possible.CAN WE TRUST AI? Today, AI-powered software is used to identify terroristthreats and targets in voice, image, email, social media,and SMS data; to assign criminal defendants risk scoresfor judges to use in making bail, sentencing, and parole de­cisions; to tell your local law enforcement where they aremost likely to encounter certain crimes; and to diagnosecancers and recommend personalized treatment plans.Task-specific AI algorithms are calculating how likely youare to “fit” into the corporate culture or remain with thecompany to which you have applied, how close a “match” astranger is to your romantic preferences, how likely you areto repay the loan you applied for, or the chances that yourkid will thrive at the selective private school you want herto attend. These decisions govern how well or how poorlyour lives go: whether we live or die, whether we work or areunemployed, whether we are free or unfree. What would ittake for you to trust a machine to make such life-changingdecisions for you—or for your employer, loan officer, doc­tor, insurance company, or your child’s college admissionscommittee? In many cases, it’s already happening. There is a common saying that commands prudence inmatters of social reliance: “trust, but verify.” Consider this:In virtually none of these artificial decision support systemscan you, as an ordinary person affected by the outcome,know how the algorithmic decision process is carried out,or what salient factors drove the algorithm’s result in yourparticular case. In many cases—due to the lack of trans­parency in “deep learning” algorithms that work withoutshowing their internal logic—even the system’s program­mers and administrators lack a clear view of how or whythe system reached its conclusion. So who, what, and howdo we verify? And if we cannot verify, can we still trust? One might think that careful regimes of inspection caneasily ensure that artificial agents are operating properly,and that what’s “under the hood” is not broken or poorlydesigned. Yet what’s under the hood in many such systemsis not a set of clear, stable rules and inferences that we canexamine and test for their validity, but rather a tangledmess of artificial neural networks arranged in complex lay­ers with nodes and weightings that constantly rearrange42 SANTA CLARA MAGAZINE                                                                                                  themselves based on changing inputs and outputs. Veri­fication of such a system’s accuracy and reliability, orreconstruction of a machine’s pattern of reasoning, is oftenimpossible in individual cases. At best we can say that as astatistical matter, over a large number of trials, the systemproduces acceptable results at least as often as a humanwould. In fact, the impressive power of many machine-learning techniques results from designs that simultane­ously make it impossible to guarantee an accurate resultin any particular case. In such systems, it is inevitable thatthey will sometimes, however rarely, produce “inappropri­ate” solutions—even wildly inappropriate, just because AIagents “reason” so differently from human intellects.Ironically, at other times algorithmic systems will pro­duce harmful and unfair outcomes for the opposite reason—that is, because their decisions will not be differentenough from ours, if they are trained on human-generateddata that infects them with our own harmful biases andfalsehoods. Examples include racial bias found in criminalrisk-score algorithms widely relied upon by U.S. judges, al­gorithms which produce the illusion of “neutral,” “objective”analysis but in fact reproduce unjust human prejudicesby mislabeling black defendants as high-risk reoffenders atfar higher rates than similar white defendants are misla­beled. A less grave but still ugly example was Microsoft’snotorious “Tay” teen chatbot that in 2016 began “learning”to adopt white supremacist slurs and conspiracy theorieswithin hours of its release on Twitter. WHO’S RESPONSIBLE? One might be tempted at this point to say, “Well then, so much the worse for AI—let’s just get rid of it and go back to relying on our own mental horsepower!” But this kind of neo-Luddite response to AI would be throwing the baby out with the bathwater. Due to the immense speed, adapt­ability, and computational power of these new software tools, they hold the promise of helping us solve countless urgent problems that human minds are just too slow, too distractible, or too constrained by evolutionary pressures to solve alone. Would you be willing to forgo—or forgo for your children and grandchildren—a cure for Alzheimer’s, or cleaner and vastly more efficient power systems, orreliable weather and global climate forecasts, or better re­sponses to drought and famine? Then we cannot afford to reject artificial intelligence out of hand. This creates an unprecedented ethical imperative for AIresearchers, designers, users, and companies and institu­tions that employ them. Artificial intelligence is immenselypowerful, but it is not magic. It does not run without human intelligence—including, even chiefly, our moralintelligence. The future of an AI-driven world depends lessupon new breakthroughs in machine learning algorithmsand big data than it does upon the choices that humansmake in how AI gets integrated into our daily lives andinstitutions and how its risks and effects are managed. This imperative falls within the realm of ethics becausecore human goods and values are at stake. An artificialagent that ruins the rest of your life by falsely labeling youa high-risk defendant, or that denies you a home or a jobbecause of a random algorithmic quirk that no one cansee, is implicated in an injustice, especially when it is reliedupon by other humans in ways that deny you due process ormeaningful remedies. We cannot sit by and allow compas­sion, justice, liberty, and respect for human dignity to besacrificed at the altar of algorithmic efficiency. Every AI-en­abled decision process is still a human responsibility, all theWe cannot sitby and allowcompassion,justice, liberty,and respectfor humandignity to besacrificed atthe altar ofalgorithmicefficiency.way down to its deepest, darkest, most inscrutable layers.Things can be done to foster and earn the public’s trustin artificial intelligence. First, companies that develop andmarket AI-driven technologies need to cultivate a sincerepublic conscience and internal corporate culture, sup­ported by incentive structures, that reflect awareness ofthe unprecedented social power of these tools. Respect forhuman life and dignity is not incompatible with healthycommerce and reliance on markets. It’s essential to it. Ifwe don’t tolerate profit-driven recklessness and contemptfor public health and safety from companies that build andoperate nuclear reactors or airliners, we cannot tolerate itfrom companies that build and operate AI, especially whenthey impact critical human systems and institutions.Second, the public needs to adopt a more critical, ques­tioning relationship with technology and its social effects.We each need to become better educated about the prom­ise and the limits of artificial intelligence, and to activelydemand and participate in AI governance and oversight,in both formal regulatory structures and informal citizen-driven structures. From the person who is asked by theirdoctor or employer to surrender genetic data to an AI-driven cloud platform, to the HR manager who downloadsan AI hiring assistant to sort résumés or evaluate interviewresponses, to the juror or judge presented with an AI-gen­erated risk score, we all need to ask reasonable questionsand demand reasonable answers about AI-driven systems,such as: “What are appropriate uses of this tool? What arecommon inappropriate uses/misuses of this tool?” “Whathuman biases could have skewed the data this system wastrained on, and what measures were taken to identify ormitigate biased results?” “What kind of errors will this sys­tem most likely make, when it makes them?” “What au­diting processes are in place to identify individual errors orharmful/unjust patterns in the results?” “What steps can Ior my organization take to ensure that independent humanchecks and other due-process measures are available whenan algorithmic decision is contested by an affected party?” Third, institutions that rely heavily upon AI-driven so­lutions, especially those institutions that protect funda­mental human goods such as education and health, needto develop institutional structures and incentives that en­sure that fundamental human values central to the missionof the institutions are not lost or sacrificed to the rule ofalgorithmic “efficiency” and its opaque authority. Humanjudgment must remain in the loop in such a way that thevigor of human intellect, the virtues of moral wisdom, andan ethos of personal responsibility are preserved and givenample opportunities to be practiced and honed. Artificialintelligence can even be enlisted in this effort as artificialhelpers and tutors that encourage and support the ongoingcultivation and refinement of human intelligence, ratherthan demoting or degrading it to a lesser status. Artificial intelligence is already one of humanity’s sharp­est tools. But like any very sharp tool we have crafted forourselves, it must be treated with care and discernment.We must know where and when it is safe to use, and whereand when it is not. We must know with whom to entrust itsuse, and with whom to not. We must know how to keep itspower from injuring or enfeebling ourselves, or those welove. And we must know that the tool and its power is al­ways the responsibility of the one who trusts it.SHANNON VALLOR is the William J. Rewak, S.J., Professor ofPhilosophy and the author of Technology and the Virtues: A Philo­sophical Guide to a Future Worth Wanting (Oxford University Press). SUMMER 2017    45 

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