I’m sure you are familiar with the old saying that a rising tide lifts all boats. There is the other side of that coin, perhaps not as well known, namely that a receding tide sinks all ships.
Bottom-line, sometimes the tide determines whether you are going up or going down.
The tide is going to do what it does. You might not have any particular say in the matter. If your boat is docked or anchored in the tide, you are at the whim of the tide. The key is to realize that the tide exists, along with anticipating which way it is heading. With a bit of luck, you can ride out the tide and remain unscathed.
Let’s consider how all of this seafaring talk about boats and tides relates to Artificial Intelligence (AI).
First, I’d like to introduce to you the increasingly popular catchphrase of Responsible AI. The general notion is that we want AI that abides by proper and desirable human values. Some refer to this as Responsible AI. Others similarly discuss Accountable AI, Trustworthy AI, and AI Alignment, all of which touch upon the same cornerstone principle. For my discussion on these important issues, see the link here and the link here, just to name a few in my ongoing and extensive coverage of AI Ethics and AI Law in my Forbes column.
A crucial ingredient entailing the AI alignment conundrum involves a semblance of trust. Can we trust that AI will be safe and sound? Can we trust that those devising AI will seek to do so in a responsible and proper manner? Can we have trust in those that field AI and are engaged in operating and maintaining AI?
That’s a whole lot of trust.
There is an ongoing effort by AI Ethics and AI Law to bolster a sense of trust in AI. The belief is that by establishing suitable “soft laws” that are prescribed as a set of guidelines or Ethical AI precepts, we might have a fighting chance of getting AI developers and AI operators to abide by ethically sound practices. In addition, if we craft and enact sufficiently attentive laws and regulations overseeing or governing AI, considered “hard laws” due to being placed onto the official legal books, there is a strong possibility to guide AI toward a straight and legally permissible path.
If people don’t trust AI, they won’t be able to garner the benefits that good AI imbues. I’ll be momentarily pointing out that there is AI For Good and regrettably there is also AI For Bad. Bad AI can impact humankind in a myriad of adverse ways. There is AI that acts in discriminatory fashions and exhibits undue biases. There is AI that can indirectly or indirectly harm people. And so on.
So, we’ve got AI For Good that we ardently want to be devised and put into use. Meanwhile, there is AI For Bad that we want to curtail and try and prevent. AI For Bad tends to undercut trust in AI. AI For Good usually increases trust in AI. An arduous struggle ensues between the mounting increases in trust that are continually being whittled away by the atrocious undermining of trust.
Upward goes AI trust, which subsequently gets batted down. Then, lowered levels of AI trust get stepped upward once again. Back and forth, the levels of AI trust seesaw. It is almost enough to make you get dizzy. Your stomach churns, akin to a semblance of seasickness like being in a boat that is rocking and bobbing in the ocean.
While that battle is taking place, you might assert that there is another macroscopic factor that serves an even greater exertion on the trust scaling progress. There is something much bigger at play. The bobbing up and down of AI trust is at the whim of a sea monster tide. Yes, AI trust is like a boat floating in a realm that is ultimately more pronounced than the battles and skirmishes taking place between AI For Good and AI For Bad.
What in the world am I referring to, you might be asking quizzically?
I’m alluding to the massive “trust recession” that our society is currently enduring.
Allow me to elucidate.
There is plenty of talk in the media today about recessions.
In an economic meaning, a recession is considered a contraction of the economy usually associated with a decline in economic activity. We normally witness or experience a recession by such economic conditions as drops in real income, a decline in the GDP (Gross Domestic Product), weakening employment and layoffs, decreases in industrial production, and the like. I’m not going to go into an extended discussion about economic recessions, for which there is much debate about what constitutes a bona fide recession versus claimed or contended ones (you can find plenty of talking heads that heatedly debate that topic).
The notion of a “recession” has widened to include other aspects of society, going beyond just the economic focus. You can refer to any slowing down of one thing or another as perhaps getting mired in a recession. It is a handy word with a multitude of applications.
Get ready for one usage that you might not have yet especially heard of.
A trust recession.
That’s right, we can speak about a phenomenon known as a trust recession.
The gist is that society at large can be experiencing a slowdown or decrease in trust. You’ve undoubtedly sensed this. If you use any kind of social media, it certainly appears as though trust in our major institutions such as our governments or major entities has precipitously fallen. Things sure feel that way.
You are not alone in having felt that spine-chilling tinge of a societal-wide drop in trust.
An article in The Atlantic entitled “The End Of Trust” last year postulated these key findings about where our society is heading:
- “We may be in the midst of a trust recession”
- “Trust spiral, once begun, is hard to reverse”
- “Its decline is vaguely felt before it’s plainly seen”
A trust recession kind of sneaks up upon us all. Inch by inch, trust weakens. Efforts to build trust are made harder and harder to pull off. Skepticism reigns supreme. We doubt that trust should be given. We don’t even believe that trust can be particularly earned (in a sense, trust is a ghost, a falsehood, it is unable to be made concrete and reliable).
The thing is, we need trust in our society.
Per that same article: “Trust. Without it, Adam Smith’s invisible hand stays in its pocket; Keynes’s ‘animal spirits’ are muted. ‘Virtually every commercial transaction has within itself an element of trust,’ the Nobel Prize-winning economist Kenneth Arrow wrote in 1972” (as cited from “The End Of Trust”, The Atlantic, November 24, 2021, Jerry Useem).
Research suggests that there is a nearly direct tie between economic performance and the element of trust in society. This is perhaps a controversial claim, though it does seem to intuitively hold water. Consider this noteworthy contention: “The economist’s Paul Zak and Stephen Knack found, in a study published in 1998, that a 15 percent bump in a nation’s belief that ‘most people can be trusted’ adds a full percentage point to economic growth each year” (ibid).
Take a moment and reflect upon your own views about trust.
Do you today have a greater level of trust or a lessened level of trust in each of these respective realms:
- Trust in government
- Trust in businesses
- Trust in leaders
- Trust in nations
- Trust in brands
- Trust in the news media
- Trust in individuals
If you can truly say that your trust is higher than it once was for all of those facets, a gob-smacking tip of the hat to you (you are living in a world of unabashed bliss). By and large, I dare say that most of the planet would express the opposite in terms of their trust for those hallowed iconic elements have gone down.
The data seem to support the claim that trust has eroded in our society. Pick any of the aforementioned realms. In terms of our belief in governmental capacities: “Trust in government dropped sharply from its peak in 1964, according to the Pew Research Center, and, with a few exceptions, has been sputtering ever since” (ibid).
You might be tempted to argue that trust in individuals shouldn’t be on the list. Surely, we still trust each other. It is only those big bad institutions that we no longer have trust in. Person to person, our trust has got to be the same as it has always been.
Sorry to tell you this: “Data on trust between individual Americans are harder to come by; surveys have asked questions about so-called interpersonal trust less consistently, according to Pew. But, by one estimate, the percentage of Americans who believed ‘most people could be trusted’ hovered around 45 percent as late as the mid-’80s; it is now 30 percent” (ibid).
Brutal, but true.
A recent interview with experts on trust led to this exposition:
· “The bad news is that if trust is this precious natural resource, it’s endangered. So, in 1972, about half of Americans agreed that most people can be trusted. But by 2018, that had fallen to about 30%. We trust institutions far less than we did 50 years ago. For instance, in 1970, 80% of Americans trusted the medical system. Now it’s 38%. TV news in the 1970s was 46%. Now it’s 11%. Congress, 42% to 7%. We are living through a massive trust recession and that is hurting us in a number of ways that probably most people are totally unaware of” (interview by Jonathan Chang and Meghna Chakrabarti, “Essential Trust: The Brain Science Of Trust”, WBUR, November 29, 2022, and quoted remarks of Jamil Zaki, Associate Professor of Psychology at Stanford University and Director of the Stanford Social Neuroscience Lab).
Various national and global studies of trust have identified barometers associated with societal levels of trust. Even a cursory glance at the results showcases that trust is falling, falling, falling.
The otherwise average version of a typical trust recession has been “upgraded” to being labeled as a massive trust recession. We don’t just have a run-of-the-mill trust recession; we instead have an all-out massive trust recession. Big time. Getting bigger and bigger. It permeates all manner of our existence. And this massive trust recession touches every corner of what we do and how our lives play out.
Including Artificial Intelligence.
I would guess that you saw that coming. I had earlier mentioned that a battle of AI For Good versus AI For Bad is taking place. Most of those in the AI Ethics and AI Law arena are daily dealing with the ups and downs of those erstwhile battles. We want Responsible AI to win. Surprisingly to many that are in the throes of these pitched battles, they are not cognizant of the impacts due to the massive trust recession that, in a sense, overwhelms whatever is happening on the AI trust battlefields.
The tide is the massive trust recession. The battling trust about AI is a boat that is going up and down on its own accord, and regrettably going downward overall due to the tide receding. As society at large is infected by the massive trust recession, so is the trust in AI.
I don’t want this to seem defeatist.
The fight for trust in AI has to continue. All I’m trying to emphasize is that as those battles persist, keep in mind that trust as a whole is draining out of society. There is less and less buffeting or bolstering of trust to be had. The leftover meager scraps of trust are going to make it increasingly harder to win the AI For Good trust ambitions.
That darned tide is taking all ships down, including the trust in AI.
Take a moment to noodle on these three rather striking questions:
- What can we do about the pervasive societal massive trust recession when it comes to AI?
- Is AI doomed to rock-bottom basement-level trust, no matter what AI Ethics or AI Law does?
- Should those in the AI field toss in the towel on AI trust altogether?
I’m glad that you asked.
Before diving deeply into the topic, I’d like to first lay some essential foundation about AI and particularly AI Ethics and AI Law, doing so to make sure that the discussion will be contextually sensible.
The Rising Awareness Of Ethical AI And Also AI Law
The recent era of AI was initially viewed as being AI For Good, meaning that we could use AI for the betterment of humanity. On the heels of AI For Good came the realization that we are also immersed in AI For Bad. This includes AI that is devised or self-altered into being discriminatory and makes computational choices imbuing undue biases. Sometimes the AI is built that way, while in other instances it veers into that untoward territory.
I want to make abundantly sure that we are on the same page about the nature of today’s AI.
There isn’t any AI today that is sentient. We don’t have this. We don’t know if sentient AI will be possible. Nobody can aptly predict whether we will attain sentient AI, nor whether sentient AI will somehow miraculously spontaneously arise in a form of computational cognitive supernova (usually referred to as the singularity, see my coverage at the link here).
The type of AI that I am focusing on consists of the non-sentient AI that we have today. If we wanted to wildly speculate about sentient AI, this discussion could go in a radically different direction. A sentient AI would supposedly be of human quality. You would need to consider that the sentient AI is the cognitive equivalent of a human. More so, since some speculate we might have super-intelligent AI, it is conceivable that such AI could end up being smarter than humans (for my exploration of super-intelligent AI as a possibility, see the coverage here).
I’d strongly suggest that we keep things down to earth and consider today’s computational non-sentient AI.
Realize that today’s AI is not able to “think” in any fashion on par with human thinking. When you interact with Alexa or Siri, the conversational capacities might seem akin to human capacities, but the reality is that it is computational and lacks human cognition. The latest era of AI has made extensive use of Machine Learning (ML) and Deep Learning (DL), which leverage computational pattern matching. This has led to AI systems that have the appearance of human-like proclivities. Meanwhile, there isn’t any AI today that has a semblance of common sense and nor has any of the cognitive wonderment of robust human thinking.
Be very careful of anthropomorphizing today’s AI.
ML/DL is a form of computational pattern matching. The usual approach is that you assemble data about a decision-making task. You feed the data into the ML/DL computer models. Those models seek to find mathematical patterns. After finding such patterns, if so found, the AI system then will use those patterns when encountering new data. Upon the presentation of new data, the patterns based on the “old” or historical data are applied to render a current decision.
I think you can guess where this is heading. If humans that have been making the patterned upon decisions have been incorporating untoward biases, the odds are that the data reflects this in subtle but significant ways. Machine Learning or Deep Learning computational pattern matching will simply try to mathematically mimic the data accordingly. There is no semblance of common sense or other sentient aspects of AI-crafted modeling per se.
Furthermore, the AI developers might not realize what is going on either. The arcane mathematics in the ML/DL might make it difficult to ferret out the now-hidden biases. You would rightfully hope and expect that the AI developers would test for the potentially buried biases, though this is trickier than it might seem. A solid chance exists that even with relatively extensive testing that there will be biases still embedded within the pattern-matching models of the ML/DL.
You could somewhat use the famous or infamous adage of garbage-in garbage-out. The thing is, this is more akin to biases-in that insidiously get infused as biases submerged within the AI. The algorithm decision-making (ADM) of AI axiomatically becomes laden with inequities.
All of this has notably significant AI Ethics implications and offers a handy window into lessons learned (even before all the lessons happen) when it comes to trying to legislate AI.
Besides employing AI Ethics precepts in general, there is a corresponding question of whether we should have laws to govern various uses of AI. New laws are being bandied around at the federal, state, and local levels that concern the range and nature of how AI should be devised. The effort to draft and enact such laws is a gradual one. AI Ethics serves as a considered stopgap, at the very least, and will almost certainly to some degree be directly incorporated into those new laws.
Be aware that some adamantly argue that we do not need new laws that cover AI and that our existing laws are sufficient. They forewarn that if we do enact some of these AI laws, we will be killing the golden goose by clamping down on advances in AI that proffer immense societal advantages.
In prior columns, I’ve covered the various national and international efforts to craft and enact laws regulating AI, see the link here, for example. I have also covered the various AI Ethics principles and guidelines that various nations have identified and adopted, including for example the United Nations effort such as the UNESCO set of AI Ethics that nearly 200 countries adopted, see the link here.
Here’s a helpful keystone list of Ethical AI criteria or characteristics regarding AI systems that I’ve previously closely explored:
- Justice & Fairness
- Freedom & Autonomy
Those AI Ethics principles are earnestly supposed to be utilized by AI developers, along with those that manage AI development efforts, and even those that ultimately field and perform upkeep on AI systems.
All stakeholders throughout the entire AI life cycle of development and usage are considered within the scope of abiding by the being-established norms of Ethical AI. This is an important highlight since the usual assumption is that “only coders” or those that program the AI is subject to adhering to the AI Ethics notions. As prior emphasized herein, it takes a village to devise and field AI, and for which the entire village has to be versed in and abide by AI Ethics precepts.
I also recently examined the AI Bill of Rights which is the official title of the U.S. government official document entitled “Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People” that was the result of a year-long effort by the Office of Science and Technology Policy (OSTP). The OSTP is a federal entity that serves to advise the American President and the US Executive Office on various technological, scientific, and engineering aspects of national importance. In that sense, you can say that this AI Bill of Rights is a document approved by and endorsed by the existing U.S. White House.
In the AI Bill of Rights, there are five keystone categories:
- Safe and effective systems
- Algorithmic discrimination protections
- Data privacy
- Notice and explanation
- Human alternatives, consideration, and fallback
I’ve carefully reviewed those precepts, see the link here.
Now that I’ve laid a helpful foundation on these related AI Ethics and AI Law topics, we are ready to jump into the heady topic of exploring the irksome matter of the ongoing massive trust recession and its impact on AI levels of trust.
Getting A Bigger Boat To Build Up Trust In AI
Let’s revisit my earlier postulated questions on this topic:
- What can we do about the pervasive societal massive trust recession when it comes to AI?
- Is AI doomed to rock-bottom basement-level trust, no matter what AI Ethics or AI Law does?
- Should those in the AI field toss in the towel on AI trust altogether?
I’m going to take the optimistic route and argue that we can do something about this.
I would also vehemently say that we should not toss in the towel. The key instead is to work even harder, plus smarter, toward dealing with the trust in AI question. The part about being smarter entails realizing that we are in a massive trust recession and soberly taking that macroscopic looming factor into mindful account. Yes, for everything that we do during the fervent efforts to adopt and support AI Ethics and AI Law, be watchful of and adjust according to the falling tide of trust all told.
Before I go further into the optimistic or smiley face choice, I suppose it is only fair to offer the contrasting viewpoint. Okay, here you go. We cannot do anything about the massive trust recession. No point in trying to tilt at windmills, as they say. Thus, just keep fighting the fight, and whatever happens with the tide, so be it.
In that sad face scenario, you could suggest it is a shrugging of the shoulders and a capitulation that the tide is the tide. Someday, hopefully, the massive trust recession will weaken and become merely a normal form of a trust recession. Then, with a bit of luck, the trust recession will whimper out and trust will have returned. We might even end up with a booming sense of trust. A trust boom, as it were.
I’ll categorize your choices into the following five options:
- The Unaware. These are those advocates in the AI Ethics and AI Law arena that don’t know there is a massive trust recession. They don’t even know that they don’t know.
- The Know But Don’t Care. These are those advocates in AI Ethics and AI Law that know about the massive trust recession but shake it off. Ride it out, and do nothing else new.
- The Know And Cope With It. These are those advocates in AI Ethics and AI Law that know about the massive trust recession and have opted to cope with it. They adjust their messaging; they adjust their approach. At times, this includes blending the trust recession into their strategies and efforts about furthering trust in AI and seeking the elusive Responsible AI.
- The Know And Inadvertently Make Things Worse. These are those advocates in AI Ethics and AI Law that know about the massive trust recession, plus they have opted to do something about it, yet they end up shooting their own foot. By reacting improperly to the societal trend, they mistakenly worsen Responsible AI and drop trust in AI to even lower depths.
- Other (to be explained, momentarily)
Which of those five options are you in?
I purposely gave the fifth option for those of you who either don’t like any of the other four or that you genuinely believe there are other possibilities and that none of the ones listed adequately characterizes your position.
You don’t have to be shoehorned into any of the choices. I merely proffer the selections for the purpose of generating thoughtful discussion on the meritorious topic. We need to be talking about the massive trust recession, I believe. Not much in-depth analysis has yet occurred in the particulars of Responsible AI and Trustworthy AI endeavors as it relates to the societal massive trust recession.
Time to open those floodgates (alright, that’s maybe over-the-top on these puns and wordplay).
If you are wondering what a fifth option might consist of, here’s one that you might find of interest.
There is a contingent in the AI field that believes AI is an exception to the normal rules of things. These AI exceptionalism proponents assert that you cannot routinely apply other societal shenanigans to AI. AI isn’t impacted because it is a grandiose exception.
In that somewhat dogmatic viewpoint, my analogy of a tide and AI trust as a boat that is bobbing up and down would be tossed out the window as an analogous consideration. AI trust is bigger than the tide. No matter what happens in the massive trust recession, AI trust is going to go wherever it goes. If the tide goes up, AI trust might go up or might go down. If the tide goes down, AI trust might go up or might go down. Irrespective of the tide, AI trust has its own fate, its own destiny, its own path.
I’ve got another twist for you.
Some might contend that AI is going to materially impact the massive trust recession.
You see, the rest of this discussion has gotten things backward, supposedly. It isn’t that the massive trust recession is going to impact AI trust, instead, the reverse is true. Depending upon what we do about AI, the trust recession is potentially going to deepen or recover. AI trust will determine the fate of the tide. I guess you could assert that AI is so powerful as a potential force that it is akin to the sun, the moon, and the earth in determining how the tide is going to go.
If we get the AI trust aspects figured out, and if people trust in AI, maybe this will turn around the trust recession. People will shift their trust in all other respects of their lives. They will begin to increase their trust in government, businesses, leaders, and so on, all because of having ubiquitous trustworthy AI.
Maybe, maybe not.
Without getting you into a gloomy mood, do realize that the opposite perspective about AI trust could also emerge. In that use case, we all fall into an utter lack of trust in AI. We become so distrustful that the distrust spills over into our already massive trust recession. In turn, this makes the massive trust recession become the super gigantic mega-massive trust recession, many times worse than we could ever imagine.
Dovetail this idea into the bandied-around notion of AI as an existential risk. If AI starts to seem as though the extremital risk is coming to fruition, namely that AI that is going to take over humankind and either enslave us or wipe us all out, you would certainly seem to have a solid argument for the massive trust recession taking a pretty dour downward spiral.
I get that.
Anyway, let’s hope for the happier side of things, shall we?
Now that you know about the massive trust recession, what can you do regarding AI trust?
First, for those of you steeped in the AI Ethics and AI Law realm, make sure to calculate your Responsible AI and Trustworthy AI pursuits via the societal context associated with being in a trust recession. You should be careful in feeling dejected that your own efforts to boost trust in AI are seemingly hampered or less than fully successful as to what you expected to occur. It could be that your efforts are at least helping, meanwhile unbeknownst to you, the trust drainpipe is rub-a-dub usurping your valiant activity in a silent and sadly detrimental way. Do not despair. It could be that if the trust recession wasn’t underway, you would have seen tremendous advances and extraordinarily laudable results.
Second, we need to do more analyses on how to measure the trust recession and likewise how to measure the ups and down’s of trust in AI. Without having reliable and well-accepted metrics, across the board, we are blindly floating in an ocean where we don’t know how many fathoms we have lost or gained.
Third, consider ways to convey that trust in AI is being shaped by the massive trust recession. Few know of this. AI insiders ought to be doing some deep thinking on the topic. The public at large should also be brought up to speed. There are two messages to be conveyed. One is that there is a massive trust recession. Second, trust in AI is subject to the vagaries of the trust recession, and we have to explicitly take that into account.
As a final remark, for now, I imagine that you know the famous joke about the fish in a fishbowl.
Here’s how it goes.
Two fish are swimming back and forth in a fishbowl. Around and around, they go. Finally, one of the fish turns to the other one and says it is getting tired of being in the water. The other fish contemplates this comment. A few ponderous moments later, the mindful fish inquisitively replies, what in the heck is water?
It’s a bit of an old joke.
The emphasis is supposed to be that whatever surrounds you might not be readily recognizable. You become accustomed to it. It is just there. You do not notice it because it is everywhere and unremarkable as to its presence (I’ll mention as an aside that some cynics don’t like the joke since they insist that real fish do know they are indeed in water, and realize “cognitively” as such, including being able to leap out of the water into the air, etc.).
As a convenient fish tale or parable, we can use this handy dandy allegory to point out that we might not realize that we are in a massive trust recession. It is all around us, and we viscerally feel it, but we don’t consciously realize that it is here.
Time to take off the blinders.
Take a deep breath and breath in the fact that our massive trust recession exists. In turn, for those of you mightily striving day after day to foster Responsible AI and garner trust in AI, keep your eyes wide open as to how the trust recession is intervening in your valiant efforts.
As Shakespeare famously stated: “We must take the current when it serves, or lose our ventures.”
originally posted on forbes.com by Lance Eliot
About Author: Dr. Lance B. Eliot is a Stanford Fellow and a world-renowned expert on Artificial Intelligence (AI) with over 6.8+ million amassed views of his AI columns. As a seasoned executive and high-tech entrepreneur, he combines practical industry experience with deep academic research and serves as a Stanford Fellow at Stanford University. Formerly a professor at USC and UCLA, and head of a pioneering AI Lab, he frequently speaks at major AI industry events. Author of over 50 books, 750 articles, and 400 podcasts, he has made appearances on media outlets such as CNN and co-hosted the popular radio show Technotrends. He’s been an adviser to Congress and other legislative bodies and has received numerous awards/honors. He serves on several boards, has worked as a Venture Capitalist, an angel investor, and a mentor to founder entrepreneurs and startups.